Evolution of Smart Homes

Evolution of Smart Homes I. Introduction Smart homes, the next gigantic leap in the field of home automation, have become an emerging research field in last few decades. Research on smart homes has been gradually moving towards application of ubiquitous computing, tackling issues on device heterogeneity and interoperability. A smart home adjusts its function to the inhabitants need according to the information it collects from inhabitants, the computation system and the context [1]. By 2050, approximately 20% of the world population will be at least 60 years old [2]. This age group is more likely to suffer from long-term chronic diseases and will face difficulties in living independently. According to World Health Organization (WHO), 650 million people live with disabilities around the world [3].The most common causes of disability include chronic diseases such as diabetes, cardiovascular disease and cancer; injuries due to road traffic crashes, conflicts, falls, landmines, mental impairments, birth defects, malnutrition, HIV/AIDS and other communicable diseases. It is not possible and logical to support all these patients in the medical center or nursing homes for an uncertain period of time. The solution is to accommodate health care services and assistive technologies in their home environment which is the main objective of smart homes. Sensors, multimedia devices and physiological equipments are core components to perceive information from home environment Infrared (IR) sensors, pressure sensors, magnetic contacts, passive and active Radio Frequency Identification (RFID) tags are used to track inhabitant location detection. Electrocardiogram (ECG), photoplethysmograph(PPG), ,temperature, spirometry, galvanic skin response, colorimetry and pulse measurement equipments are used to get physiological information from the patient. Camera and microphones provide audiovisual response from home user. Inhabitant can access the system through display panel. Power line communication protocols are widely used for the connectivity of home appliances. Public telecommunication network with voice and text messaging service is involved to provide telecare facility from remote location. Videoconferencing is used as an interactive communication media between caregiver and the client. TCP/IP protocols of Ethernet network provide data connectivity for local and remote sites and locations. Ethernet protocols are also used to connect health-monitoring equipments and to provide data repository service. Algorithms from machine learning, data compression, statistics and artificial intelligent are employed to predict user behavior, detect activities of daily life (ADL) and location. C4.5 algorithm from machine learning is utilized to build spatiotemporal context of user. C4.5 algorithm is developed by Quinlan in 1993 which classify the data to construct a decision tree according to data attributes [47].Active LeZi from data compression algorithms is used to predict inhabitants next behavior. Active LeZi by Gopalratnam et al..in 2007 builds a decision tree utilizing similar methodology of LZ78 data compression algorithm and predict next event using Prediction by Partial Matching (PPM) algorithm[22].Statistical predictive algorithms like Bayesian filtering, dynamic Bayesian network algorithms classify the information and recognize ADL of home client[34][41][44]. Different flavors of AI algorithms extended for smart home data processing. Markov model, Hidden Markov model, Artificial Neur al Network can detect the living pattern of user and can also predict the user [7][13][14][38]. Fuzzy Logic is used for home appliance control [36]. Smart home is mainly dedicated to provide health care, safety, security and monitoring service for patient and elderly. The house is equipped with sensors, cameras to track people and can trigger an alarm to a remote heath care service provider in the case of emergency. Sophisticated physiological devices monitor heart rate, blood pressure, body temperature, ECG record and the patient is being observed from a distance location. Telecommunication service is used for communicating with service provider, relatives or neighbor and as a redundant acknowledgement method from the patient. For home comfort system, lighting, heating, doors, windows and home appliances are automatically controlled by ambient intelligence of smart home. Smart home also has significant contribution towards energy conservation by integration of energy meter with smart home [4]. Home automation is the initial state of smart home where electronic technologies are used to provide an easy access to household devices. Rapid development of sensor technology accelerated the growth of smart home that involved more data processing. Improvement of information and communication technology make possible to develop easy and cost effective methods for data repository and exchange. Smart home is a growing concept, efficient and lower cost solutions for general people are the main idea to promote it. II. Smart Home defination Smart home is an extension of modern electronic, information and communication technologies. The main objective of smart home research is to provide smartness to a dwelling facility for comfort, healthcare, security and energy conservation. Remote monitoring system is a common component of health smart home where telecommunication and web technologies are used to provide quick and proper medication to the patient from specialized assistance centre. The first formal definition of smart home was published by Intertek in 2003, which was involved to Department of Trade and Industry (DTI) smart-homes project in UK [5]. According to Intertek a smart home is a dwelling incorporating a communications network that connects the key electrical appliances and services, and allows them to be remotely controlled, monitored or accessed. A home needs three things to make it smart: Internal network wire, cable, wireless Intelligent control gateway to manage the systems Home automation products within the homes and links to services and systems outside the home III. Review of Smart Homes Smart homes projects are being conducted for last several decades and they convey different ideas, functions and utilities. It is growing to different brunches of specialization focusing the interest of the researchers and user requirements and expectations. This article is a study of the evolution of smart home according to time. Adaptive Control of Home Environment (ACHE) system is developed by Mozer in 1998 in USA. ACHE monitors user device usage pattern utilizing different types of sensors and builds an adaptive inferential engine for neural network to control temperature, heating and lighting. ACHE can control three main components of a home while trying to maximize user comfort and conserve energy [7].ACHE is one of the early smart home projects which is able to partially automate home environment via controlling lighting, temperature and heating components. CarerNet is an architectural model of integrated and intelligent telecare system proposed by Williams et al. in 1998. Its core components are sensor set, a sensor bus, intelligent monitoring system and a control unit. ECG, photoplethysmograph, spirometry, temperature, galvanic skin response, colorimetry, and pulse measurement tools used to collect physiological data. The communication network within the clients local environment is an integration of HomeLAN and Body Area Network (BAN) which is responsible to carry real-time data, event data, command and control data. It has a distributed intelligence system in the form of smart sensors, smart therapy units, body-hub, Local Intelligence Unit (LIU) and Clients Healthcare Record (CHR). Home emergency alarm system, community health information and ambulatory monitoring service can be provided by the system. [8]. CarerNet is an abstract model of health smart home and interconnecting components. No prototype of the model has been developed . Only a hypothetical case study is of an individual who had undergone brain surgery after suffering from a subarachnoid is discussed. Barnes et al. in 1998 have evaluated life style monitoring data of elderly using infrastructure of British Telecom and Anchor Trust in England. The system detects inhabitants movement using IR sensors and magnetic contacts on the entrance of the doors. To measure temperature it uses a temperature sensor in the main living area. An alarm activation system is developed which detects abnormal behavior and communicates to remote telecare control center, the clients and their carers[9]. The researchers presented a lower cost solution for smart telecare. The limitation of the system is it can identify only abnormal sleeping duration, unexpected inactivity, uncomfortable home temperature and fridge usage disorder. Moreover, it uses a special new telecom protocol named No Ring Calling which demands modifying existing telecom protocols. TERVA is a health monitoring system developed in Finland by Korhonen et al.(1998). TERVA processes physiological information like blood pressure, heart beat rate, body temperature, body weight to draw graphical representation of wellness condition of the subject[10].Research goal of the TERVA system is to develop a real time visual monitoring system but it is unable to provide long-term trend of certain physiological information. It cannot detect physiological problems and no assistive service is deployed to provide health care. The intelligent home (IHome) project at the University of Massachusetts at Amherst has developed an intelligent environment (Lesser et al.1999) IHome is a simulated environment designed with Multi Agent Survivability Simulator (MASS) and a Java Agent Framework (JAF) as tools to evaluate agent behavior and their coordination. The focus of the project is to model agent interactions and task interactions so that the agent can evaluate the tradeoff between robustness and efficiency [11].IHome is a simulation only solution, the project never build a practical smart home to evaluate their model. The Aware Home Research at the Georgia Institute of Technology developed a smart home, which equipped with monitoring facilities to study human behavior (Kidd et al. 1999). To build a model of user behavior pattern, it uses smart floor to sense footsteps. Hidden Markov models, simple feature-vector averaging and neural network algorithms are applied on these data to create and evaluate behavioral model [12]. The aim of the project is to study user behavior, which is the primary stage of smart home research. The project never developed home intelligence which is a big shortfall of the research. The EasyLiving project at Microsoft Research based on intelligent environment to track multiple residents using distributed image-processing system (Krumm et al. 2000). The system can identify residents through active badge system. Measurements are used to define geometric relationship between the people, devices, places and things [13][14].The system is workable in single room only and can track upto three peoples simultaneously. SELF (Sensorized Environment for LiFe), is an intelligent environment, which enables a person to maintain his or her health through self-communication (Nishida et al. 2000). SELF observes the persons behavior with distributed sensors invisibly embedded in the daily environment, extracts physiological parameters from it, analyzes the parameters, and accumulates the results. The accumulated results are used for reporting useful information to maintain the persons health. The researchers constructed a model room for SELF consisted of a bed with pressure sensor array, a ceiling lighting dome with a microphone and a washstand with display[15].SELF describes a self-assessment system of human health but measuring only respiratory system and sleeping disorder, which is not sufficient to monitor health condition. The ENABLE project was set up in 2001 to measure the impact of assistive technology on the patient suffering from mild or moderate dementia (Adlam et al. 2004). The researcher installed two devices (cooker and night light) in the apartment of several patients in different locations to evaluate the efficiency of the system [16]. The research scope is limited to only two household devices but to assist this type of patient the whole house must possess some kind of intelligence. Health Integrated Smart Home Information System (HIS) is an experimental platform for home based monitoring (Virone.et al. 2002). IR sensors are used to track inhabitant activities and the information is transmitted via Controller Area Network (CAN) to a local computer. The system generates alerts according to some predefine zones [17]. The research is only limited to single inhabitant monitoring. In 2002, GuillÃÆ' ©n et al. developed a system composed of two parts: home station (HS) and caregiver medical center (CMC) connected via integrated service digital network (ISDN) backbone. The home station is equipped with vital signs recording module to monitor physiological data like blood pressure, temperature, ECG, pulse oximetry. Caregiver medical center is like a call center designed specially with patient monitoring software. An interactive communication system between home and caregiver center is developed using videoconferencing technology [18]. Figure 1 shows functional modules of multimedia smart home. The system requires high Internet bandwidth for videoconferencing, which needs expensive equipments and high maintenance cost. Functional module of multimedia platform [18] At University of Tokyo, Noguchi et al (2002) designed an intelligent room to support daily life of the inhabitant. The system has three main components: data collection, data processing and integration of processed data. The system learns current state of environment from sensors attached to bed, floor, table and switches. A summarization algorithm is used to track any changes in the system. The algorithm segments the collected sensory data at the points where sensor outputs changes drastically (i.e. pressure data appears suddenly or switch sensors are changed). It labels the segment with the room state. It joins a state of each segment to quantize the accumulated data and ties up the changed situation. The algorithm also tries to eliminate and reduces situations that changes slightly [19].The proposed summarization algorithm can detect user activities which is tested for single room only. No home automation method discussed utilizing the algorithm. MavHome (Managing an Adaptive Versatile Home) first introduced by Das et al. in 2002 at the University of Texas, Arlington [20]. Figure 2 describes MavHome architecture in brief. MavHome use multi disciplinary technologies: artificial intelligent, multimedia technology, mobile computing and robotics. It is divided into four abstract layers: physical, communication, information and decision. X10 protocol is used to control and monitor more than sixty X10 devices plugged into the home electric wiring system [21]. Active LeZi algorithm is developed that makes a decision tree based on kth order Markov model and predict next action calculating probability of all actions applying prediction by partial matching method [22]. Although MovHome utilize algorithms to make accurate prediction and decision, it only predicts the behavior of single inhabitant [23]. concrete architecture of MavHome[21] The Rehabilitation Engineering Research Center on Technology for Successful aging (RERC-Tech-Aging) at the University of Florida introduced House of Matilda (Helel et al. 2003, 2005)[24].The home is inhabited by a dummy called Mutilda. The main aim of this research is to perceive user location using ultrasound technology. After two years, in 2005 they designed the second generation of this home named GatorTech'[25]. GatorTech is actually integration of smart device with sensors and actuators to optimize the comfort and safety of older peoples. The system is not user friendly because it requires wearable device for user tracking. In 2004, Mihailidis et al. developed a computer vision system in pervasive healthcare systems. The vision system consists of three agents: sensing, planning and prompting. Statistics and physics based methods of segmenting skin color in digital images are used for face and hand tracing [26]. Only hand and face tracing is not sufficient to make an efficient smart home system, the system should include body tracking and hand gesture reorganization. Multimedia Laboratories, NTT DoCoMo Inc. in Japan, has developed a system for modeling and recognizing personal behavior utilizing sensors and Radio Frequency Identification (RFID) tag (Isoda et al. 2004)[27]. C4.5 algorithm is used to construct decision tree from the data obtained from the sensors and RFID tags. The users behavioral context at any given moment is obtained by matching the most recently detected states with previously defined task models. The system is an effective way for acquiring users spatiotemporal context but no intelligent system is developed for home appliances control. Andoh et al. in 2004 developed a networked non-invasive health monitoring system analyzing breath rate, heart rate, snoring and body movement. Researchers adopted Ethernet network for breath monitoring system implementation. The system can estimate sleep stages analyzing data using the algorithm developed for the purpose [28]. The system cannot summarize long term observation of patients sleeping disorder. In 2005, Masuda et al. have developed a health monitoring arrangement using existing telecommunication system for home visit rehabilitation therapists. Researchers used an air filled mat to measure heartbeat and respiratory condition. When the patient lies on the air mat, his heartbeat and respiratory movement cause significant change in air pressure inside the mat, which is measured by pressure sensor and analyzed by appropriate filtering process [29]. The interesting part of the project is the usage of an air bag as monitoring equipment but its limitation is, it can only measure heart rate and respiratory condition. In 2005, Ma et al emphasized on context awareness to provide automatic services in smart home. They used case-based reasoning (CBR) to provide more appropriate services. CBR technique relies on previous interactions and experiences to find solutions for current problems. The system can adopt any manual adjustment done by modifying case data [30].This is the initial state of the project where few scenarios like AC, TV, lamp interaction is evaluated. Their future plan is to add more contexts and enrich the features of case tables. The House_n group at MIT designed PlaceLab a new living laboratory for the study of ubiquitous technologies in home environment (Intille et al. 2005). PlaceLab deployed with numerous wire, light, pressure, temperature water, gas, current sensors with video and audio devices to create vast amount of real life data from single volunteers as well as couples [31].The goal of the project is to study human behavior, influence of technology on the people and how technology can be used to simplify user interaction with home appliances. Their main contribution is an open online database of smart home sensor events and a well featured analyzing software [48].Researchers never implemented the study to build an autonomous intelligent home. Yamazaki (2006) constructed Ubiquitous Home, a real-life test bed, for home context-aware service. It is a housing test facility for the creation of useful new home services by linking devices, sensors, and appliances across data networks. Active and passive RFID tags located above the ceiling and at the entrance of the door are used to detect and recognize inhabitants. Pressure sensors are used to track user movement and furniture. The system is occupied with plasma panels, liquid crystal display and microphone for better interaction with the users. A network robot is employed to perform certain home services. Researchers concluded that the goal of smart home is not to design an automated home but to develop an environment using interface technologies between human and the system [32].Although, the researchers installed enough sensors and interfacing devices , the system is only sensible to few task automations like TV program selection, cooking recipe display and forgotten property service. Ha et al. (2006) presents a sensor-based indoor location-aware system that can identify residents location. Researchers used an array of Pyroelectric Infrared (PIR) sensor and proposed a framework of smart home location aware system. An algorithm is developed to process the information collected from PIR sensors for inhabitant location detection. Their next step is to design an algorithm to determine location and trajectory of multiple residents simultaneously [33]. The project in dedicated to user location detection system which is an essential part of smart home. No system is developed to provide intelligence to the house employing user location. In 2007, Rahal et al. at DOMUS laboratory, Universit ´e de Sherbrooke, Canada, utilized Bayesian Filtering methods to determine location of the inhabitants. Bayes filters are efficiently used to estimate a persons location using a set of fixed sensors. In this method, the last known position and the last sensor event are both used to estimate a new location. The algorithm based on Bayesian filtering shows a mean localization accuracy of 85% [34].This project also deals with user location detection algorithm, no home automation is developed using the processed information. De Silva et al. (2007) have implemented an audiovisual retrieval and summarization system utilizing multimedia technology for human behavior tracking. Using a large number of cameras a hierarchical clustering of audio and video handover used to create personalized video clips. An adaptive algorithm is used for complete and compact summary of the video retrieved. Basic audio analysis methods are applied for accurate audio segmentation and source localization. An interface allowed users to incorporate their knowledge into the search process and obtain more accurate results for their queries [35].The system can track people, extract key frame, localize sound source, detect lighting change but cannot distinguished different people. At Tampere University of Technology, Vainio et al.(2008) developed a proactive fuzzy home-control system. An adaptive algorithm applied to evaluate the test on obtained results. The goal of the research is to help elderly people live independently at home. Developed system can recognize routines and also recognize deviations from routines. The system can provide information to caregivers about living rhythm, sleeping disorders, and medicine taking of inhabitant [36]. But the system works sensibly only for lighting control. In 2008, Swaminathan et al. proposed an object reorganization system using visual image localization and registration. Appliances are first registered in the image processing system. According to the voice command of the user, appropriate object is selected using an environmental map [27].It is actually a home automaton project using speech reorganization to receive user command and commands are executed to the objects which already known to the system. Growing Self-Organizing Maps (GSOM) used a self-adaptive neural network to detect and recognize activities of daily life addressed by Zheng et al in 2008 [38] [39]. The GSOM follows the basic principle of the Kohonen self-organizing map with a special focus on adaptive architecture. The learning process of the GSOM is started by generating an initial network composed by four neurons on a 2-dimensional grid, followed by iteratively presenting training data samples. The system is tested in single room apartment for about two weeks where it can recognized user pattern of 22 distinct activities. Like other Self Adaptive Neural Networks (SANN), the system is depends on several learning parameters to be determined in advance such as initial learning rate and the size of the initial neighborhood. Other machine learning method must be utilized in parallel to determine optimum parameter for best performance. In 2008, Perumal et al. from Institute of Advanced Technology of University Putra Malaysia (UPM) have presented a design and implemented Simple Object Access Protocol (SOAP) based residential engagement for smart home systems appliances control [40]. An appliance control module based on SOAP and web services developed to solve the interoperation of various home appliances in smart home systems. Fifteen feedback based control channels implemented with residential management system through Web Services. If the residential management system experiences server downtime, the home appliances can still be controlled using alternate control mechanism with GSM network via SMS Module locally and remotely. This system offers a complete, bi-directional real-time control and monitoring of smart home systems. No security mechanism is used to protect the web server from unauthorized access. Virone et al. present a dozens of statistical behavioral patterns obtained from an activity monitoring pilot study. The pilot study examined home activity rhythms of 22 residents in an assisted living environment with four case studies. Established behavioral patterns have been captured using custom software based on a statistical predictive algorithm that models circadian activity rhythms (CARs) and their deviations (Virone et al. 2008). The system cannot differentiate multiple inhabitants [41]. Yoo et al. examined web-based implementation possibility of a central repository to integrate the biosignal data arrives from various types of devices in a remote smart home. Medical waveform description Format Encoding Rule (MFER) standard is followed for communicating and storing the biosignal data in ubiquitous home health monitoring system. The web-based technology allowed ubiquitous access to the data from remote location. The paper presents a common data format for all types of sensor (Yoo et al. 2008)[42].Figure 3 describes functional architecture of web based data retrieval system. Information security, which is a burning issue for any web based system is not considered in this research. A web-based architecture for transferring the measured biosignal data from the u-House to the remote central repository. A snow-flake data model is designed by Zhang et al. in 2008 to represent the activities data in smart homes [43]. Sensor data are stored in the homeML structure. A new algorithm is proposed on the prediction of class labels for variable person and activities of daily life (ADL) indicating who is doing what, given the observed episode and time information. Accuracy is calculated as the proportion of the number of correctly predicted class over the total number of episodes in the evaluation dataset. The learning output in the form of a joint probability distribution is also assessed by the distance to the true underlying probability distribution, using the Euclidean metric. The smaller the distance is, the closer the learned model to the true situation. The algorithm is based on probabilistic distribution and able to predict ADL of more than one inhabitant. The result given is based on simulated data and the example shows only one task identification (à ¢Ã¢â€š ¬Ã‹Å"making drink activi ties). In 2008, Park et al. proposed a method for recognizing ADL at multiple levels of details by combining multi-view computer vision and RFID based direct sensor [44]. A hierarchical recognition scheme is proposed by building a dynamic Bayesian network (DBN) that encompasses both coarse-level and fine-level ADL recognition. Their methodology combines the two tracking technology. The system requires wearable RFID tag which is not comfortable for users. Rashidi et al. developed CASAS at Washington State University in 2008. CASAS is an adaptive smart home that utilizes machine-learning techniques to discover patterns in user behaviour and to automatically mimic these patterns. The goal is to keep the resident in control of the automation. Users can provide feedback on proposed automation activities, modify the automation policies, and introduce new requests. In addition, CASAS can discover changes in residents behaviour patterns automatically. Frequent and Periodic Activity Miner (FPAM) algorithm mines this data to discover frequent and periodic activity patterns. These activity patterns are modelled by their Hierarchal Activity Model (HAM), which utilizes the underlying temporal and structural regularities of activities to achieve a satisfactory automation policy. User can provide feedback on proposed automation activities, modify the automation policies, and introduce new requests [45].To make a system more interactive smart home s hould be equipped with voice reorganization facilities which is absent in this system. Raad et al. developed a cost-effective user-friendly telemedicine system to serve the elderly and disabled people. An architecture of telemedicine support in smart home that consists of web and telecom interface is considered in their research (Raad et al. 2008)[46]. This system also suffers from information security issues. PRIMA (Perception, recognition and integration for interactive environments) research group of the LIG laboratory at the INRIA Grenoble research center in France has defined a model for contextual learning in smart homes (2009). The authors developed a 3D smart environment consisting cameras, a microphone array and headset microphones for situation modeling. It relies on 3D video tracking and role detection process regarding activities of the person. Roles are learned by support vector machines (SVM). It is also capable to learn speed of the inhabitant and distance to the interacting object. Proposed system can identify situations like introduction, presentation, aperitif, game and siesta. Its error rate is very high [49]. Kim et al. developed a pyroelectric infrared (PIR) sensor based indoor location aware system (PILAS) in 2009.The system uses an array of PIR sensors attached with the ceiling and detects inhabitants location by combining overlapped detected areas. PIR sensors construct a virtual map of resident location transition. To improved accuracy, they applied Bayesian classifier using a multivariate Gaussian probability density function to determine the location of an inhabitant. PILAS is unable to detect multiple residents [50]. Wang et al. have developed a smart home monitoring and controlling system(2009). The system can be controlled from remote locations through an embedded controller. They have developed different GUI for mobile devices and PCs. Each device has a unique address. A new command format to control the devices is introduced. It is a complex system and not compatible to previous smart homes architectures [51]. Yongping et al. have developed an embedded web server to control equipments using Zigbee protocol (2009). For this purpose they used S3C2410 microprocessor which was programmed with Linux 2.6 kernel. To provider online access a small web server (only 60 Kbytes) named Boa is installed. An interface had also been designed to communicate with Zigbee module (MC13192).The system do possess any type of intelligence [52]. Hussain el al. have developed inhabitant identification system using wireless sensor network (WSN) and RFID sensors (2009). The system can identify user location by the intensity of the Radio Signal Strength Indicator (RSSI) of WSN. A person is recognized by attached a RFID tag. The combined reading of RSSI signal and RFID receiver can successfully identify specific location of a resident in the home. The system is limited to single person tracking [53]. At Industrial Technology Research Institute (ITRI) in Taiwan, Chen et al.

Trail of Tears and the Five Civilized Tribes

During the early years of 1800s, valuable gold deposits were discovered in tribal lands, which by previous cessions had been reduced to about seven million acres in northwest Georgia, eastern Tennessee, and southwest North Carolina. In 1819, Georgia appealed to the U. S. government to remove the Cherokee from Georgia lands. When the appeal failed, attempts were made to purchase the territory. Meanwhile, in 1820 the Cherokee established a governmental system modeled on that of the United States, with an elected principal chief, a senate, and a house of representatives. Because of this system, the Cherokee were included as one of the so-called Five Civilized Tribes. The other four tribes were the Chickasaw, Choctaw, Creek, and the Seminoles. In 1832, in spite of the fact the Supreme Court of the United States ruled that the Georgia legislation was unconstitutional, federal authorities, following Jackson†s policy of Native American removal, ignored the decision. About five hundred leading Cherokee agreed in 1835 to cede the tribal territory in exchange for $5,700,000 and land in Indian Territory (now Oklahoma). Their action was repudiated by more than nine-tenths of the tribe, and several members of the group were later assassinated. In 1838 federal troops began forcible evicting the Cherokee. Approximately one thousand escaped to the North Carolina Mountains, purchased land, and incorporated in that state; they were the ancestors of the present-day Eastern Band. Most of the tribe, including the Western Band, was driven west about eight hundred miles in a forced march, known as the Trail of Tears. The march west included 18,000 to 20,000 people, of whom about 4000 perished through hunger, disease, and exposure. The Cherokee are of the Iroquoian linguistic family. Their economy, like that of the other southeastern tribes, was based on intensive agriculture, mainly of corn, beans, and squash. Deer, bear, and elk were hunted. The tribe was divided into seven matrilineal clans that were dispersed in war and peace moieties (half-tribes). The people lived in numerous permanent villages, some of which belonged to the war moiety, the rest to the peace moiety. In the early 19th century, the Cherokee demonstrated unusual adaptability to Western institutions, both in their governmental changes and in their adoption of Western method of animal harvesting and farming. Public schools were established and in the 1820s, a tribal member invented an 85-character syllable script for the Cherokee language. Widespread literacy followed almost immediately. In 1828 the first Native American newspaper, the Cherokee Phoenix, began publication. Today in Oklahoma, much of the culture has remained the same. Their traditional crafts are most strongly preserved by the Eastern Band where their basketry is considered to be equal to or better than that of earlier times. In Oklahoma the Cherokee live both on and off the reservation, scattered in urban centers and in isolated rural regions. Their occupations range form fishing to industrial labor to business management. In North Carolina, farming, forestry, factory work, and tourism are sources of income. As of 1990 there were 308,132 Cherokee descendants in the United States. Another member of the five tribes is the Seminoles, a Native American tribe of the Muskogean language family. Most now live in Oklahoma and southern Florida. The Seminole tribe developed in the 18th century from members of the Creed Confederacy, mostly Creeks and Hitchiti, who raided and eventually settled in Florida. After the United States acquired Florida in 1819, the territorial governor, Andrew Jackson, initiated a vigorous policy of tribal removal to open the land for white settlers. After the capture of their leader Osceola in 1837 and the end of the Second Seminole War in 1842, several thousand Seminole were forcibly moved west to Indian Territory. At the end of the Third Seminole War in 1858, about 250 more were sent west. The rest were allowed to remain, and their descendants signed a peace treaty with the United States in 1935. In 1964 the Miccosukee signed a 50-year agreement with national Park Service that allows the Miccosukee access to more than 300 acres of the Everglades. The Florida Seminole have five reservations. They farm, hunt, fish, and some run tourist-related businesses. Many still live in thatch-roofed, open-sided houses on stilts and wear patchwork and applique clothing. The Seminole in Oklahoma were given a smaller reservation after the American Civil War. In the late 19th century they yielded to pressure to divide their tribal land into individual allotments and cede the surplus to the United States; this land was opened to settlers in 1889. In 1990 Seminole descendants numbered 13, 797. Many were Baptists, but both the Florida and Oklahoma groups retained traditional Muskogean observances. The three remaining tribes, Choctaw, Chickasaw, and the Creek, are all close in relationship. All tribes are of the Muskogean linguistic family and all occupied an area that now includes Georgia, Alabama, Mississippi, Louisiana, and Kentucky. The Chickasaw lived in dwellings constructed alongside streams and rivers rather than in villages. They obtained food by hunting, fishing, and farming. The Creek were an agricultural tribe, living in villages consisting of log houses. Creek women cultivated corn, squash, beans, and other crops, and the men hunted and fished. The Choctaw were less warlike that their traditional enemies, the Chickasaw and the Creek. They lived in mud and bark cabins with thatched roofs. They were also agricultural people, probably the most able farmers of the southeastern region. They also raised cattle, fished, and hunted. In 1990 the Chickasaw and their descendants numbered 20,631, the Creek heritage numbered 43,550, and a large number of Choctaw and their descendants live principally in Oklahoma and also in Mississippi and Louisiana. During the 18th and 19th centuries the Choctaw were forced to move farther and farther west to avoid conflict with European settlers. By 1842 they had ceded most of their land to the United States and were relocated in Indian Territory, land set aside for them in present-day Oklahoma. Here the Choctaw became, along with Creek, Cherokee, Chickasaw, and Seminole, part of a group of Native Americans known as the Five Civilized Tribes, so called because they had organized governments the establishment of public schools and newspapers.

Relationship Between Asset Price And Monetary Policy - Free Essay Example

Sample details Pages: 24 Words: 7118 Downloads: 3 Date added: 2017/06/26 Category Finance Essay Type Analytical essay Did you like this example? With the development of capital market and the innovation of financeà ¯Ã‚ ¼Ã…’asset prices have taken a more prominent role in financial economyà ¯Ã‚ ¼Ã… ½Meanwhileà ¯Ã‚ ¼Ã…’financial crisis and economy turbulences arouse by abnormal assets price fluctuation appear in many countriesà ¯Ã‚ ¼Ã… ½Currently, China is confronted with the reality of asset prices inflationà ¯Ã‚ ¼Ã… ½Asset prices rapidly fluctuation bought gigantic impact to monetary policy, thereforeà ¯Ã‚ ¼Ã…’study the relationship between asset price and monetary policy according to Chinas economy is significantà ¯Ã‚ ¼Ã… ½ This dissertation applies correlation analysis, unit root testà ¯Ã‚ ¼Ã…’cointegration test and Granger causality test in the empirical analysis of the relationship between asset price and monetary policy, from the data analysis, we could conclude that asset prices and monetary policy have a long-term relationship. The central bank should focus on the role of asset price on t he transmit mechanism of monetary policy. Key wordsà ¯Ã‚ ¼Ã… ¡asset priceà ¯Ã‚ ¼Ã…’monetary policy, central bank . Don’t waste time! Our writers will create an original "Relationship Between Asset Price And Monetary Policy" essay for you Create order 1. Introduction Motivation With the development of modern capital market and financial innovation, the world economy has into the financial economy era, and disappears increasingly capitalization, virtualization trends. It is no doubt that modern capital market has provided a powerful lever for economic growth, but its instability also cause macroeconomic fluctuations , and in particular the asset price bubbles, which is becoming a key factor for financial crisis and economic fluctuations. So far, the most developed Western countries have experienced a long period of rapid growth, concern is that global asset price has increased sharply in recent years. In the late 1980s, the stock market and real estate in Japan as the representative of asset prices have greatly increased ,which also caused Japanese economy into the bubble economy, the credit crunch and economic recession arising from the bubble economy have serious negative effects so far. In 2006, the Dow Jones industrial average index in USA was beyon d the highest point of network technology bubble expansion from 2000, the stock market of many other developed and emerging market countries generally strongly increased and was beyond history records. In addition to the security market , the global real estate, gold and oil market are also very active. In 2001-2005, real estate prices have nearly doubled in many developed countries, meanwhile, real estate price in many developing countries has also generally increased. In May 2006, the international spot gold price reached USD per ounce 718 score in New York City market since 1980. In mid-July 2006, the International crude oil futures price GE exceeded the highest record to reach 75 USD/barrel. But, inevitably brought the more serious financial crisis in 2007, which has caused huge economic fluctuations to the economy from 2007. In China, securities market have established for ten years, the shares of negotiable securities in the structure of residents capital portfolios continu ed to be increasing, in 1992, the total value of Chinese stock market is 1048 billion yuan, accounting for only 3.9% of gross domestic product (GDP) ratio . But in 2007, the stock market value is 327141 billion yuan, the ratio of market value in GDP has being greatly rising to 130% , which is 312 times growth compared with the total stock market value in 1992 . Not only a huge amount, but also the fluctuations of asset prices have become more frequent and intense. In 2006-2007, Chinese economy under the driven of stock market and real estate market has a certain degree of asset price bubbles. While in the same time , Chinese economy is actually facing the reality of asset price falling from the top digit, shanghai security market falls rapidly from the peak position in 2007, the stock market bubbles receive the extrusion, the real estate market similarly is also facing the similar situation, house price of major cities has falls obviously, the turnover falls into the valley. All of these financial crisis constantly are reminding people that the worldwide economic fluctuations are characterized by the financial instability , and economic cyclical fluctuations, instead of disappearing, and to be getting worse, cause considerable economic depression, frequent asset price volatility and financial crisis and economic recession arising from the asset market collapse, hence , the government should focus on the asset prices on the role of macroeconomic fluctuations and the central bank in the world have to consider the information from asset price fluctuations. The Fed Chairman Alan Greenspan and Bernanke concern much about monetary policy and asset price volatility. Alan Greenspan proposed central banks should be more concerned about the issue of asset price bubbles in the anniversary meeting celebrating the establishment of the Bank of England in 1994 . Chairman Bernanke is an internationally recognized as the founder of monetary policy and asset price research. At their encouragement , the international academic community and the national central banks have recent research and debate whether the monetary authorities should intervene directly in asset price fluctuations. These research and debates are from the different backgrounds in different countries, in accordance with their different assumptions and premises, provide some significance policy advice. Financial markets in particular capital market deepening and broad-based, and financial innovation enables financial institutions have diversity features. The boundaries of currency and other financial assets is blurring, money supply and real economic variables lost stability, the monetary policy impact on the real economy is no longer limited to traditional approaches, according to the traditional Keynesian theory, this impact on consumption and investment mainly through interest rate variable . But as the improvement of financial system and increase of financial assets stock , moneta ry policy can also use the wealth effect of asset prices and Tobin q to affect the consumption and investment, causing the changes of total demand, in stick price, the aggregate demand led to a change in the output , and cause the effect of the output changes on demand, if the aggregate demand exceeds the aggregate supply ,it can lead to inflation pressures. This series of transmission mechanism make the role of asset market on the real economy become more prominent, asset price has become a major transmission channel of monetary policy . From the reality in China, the rapidly development of asset markets have a key role in our national economy, the impact of real estate market and stock market on economic and monetary policy are becoming increasingly apparent. In fact, the Chinese monetary authorities have also already begin to pay attention to the relationship of asset price and monetary policy . Xiaochuan, Zhou , as the Governor of Chinese central bank ,says that the central b ank concerns about changes in asset prices and gives full attention to information from asset price when formulating and implementing monetary policy. Thereforeà ¯Ã‚ ¼Ã…’study the relationship between asset price and monetary policy in Chinese economy is significantà ¯Ã‚ ¼Ã… ½ This dissertation analyze the relationship of asset price and monetary policy systematically, and will use econometric methodology to seek to explore the relationship between asset price and monetary policy in China by using quarterly statistics from 1998 to 2008. I will estimate the relationship between monetary policy variables and asset price variables , through correlation analysis, unit root testà ¯Ã‚ ¼Ã…’cointergration test Granger Causality test to get the conclusion. This dissertation conclude the results thatà ¯Ã‚ ¼Ã… ¡monetary policy and asset price have a long-term relationship, in a short time, the monetary policy aggravated the asset price fluctuations to some degreeà ¯Ã‚ ¼Ã…’the asset market appeared to be rapidly soared and shirked in a short period of timeà ¯Ã‚ ¼Ã… ½The central bank should focus on the role of asset price on the transmit mechanism of monetary policy. 2. Literature Review 2.1 Empirical Studies from Western Economists The research from western economists on relationship of asset price fluctuations and monetary policy mainly reflects the two views. First, asset price and monetary policy do not exist the causal relationship on behavior , and the only relationship is on the information that reflects the present and future output growth. Another view on research of asset price fluctuation and monetary policy is that asset price can affect consumption and investment through wealth effects , the change of capital cost and asset price fluctuations affect consumption and investment respectively through wealth effects and Tobin q, thus affect the financial institutions status of assets and liabilities , further affect the stability of the financial system. It is evidently that asset price has become the transmission mechanism of monetary policy. Frank Smets (1997) is one of the economist who systematically analysis the optimal monetary policy that Central Bank should response on asset price change. He has proposed the following important viewpoint: how the central bank should respond to monetary policy reflecting the unexpected change of asset price, how this change affect the central banks inflation forecast. There are two factors affecting the central bank forecast inflation. First is that the effect of asset price on transmitting mechanism of monetary policy. Second is that the unique information on asset price. He established a simple macro-economic model that contains equation of Phillips curve, aggregate demand on financial asset prices (as represented by the stock price) , arbitrage and dividend , he uses this model to examine the variety of ways that change of financial assets price affect the real economy, and analysis the optimal monetary policy of central bank response to financial asset price movements. He demonstrate that the optimal monetary policy of central bank response to financial asset price movements, that is, according to the structure of established model , weighted average of the short-term interest rate ( traditional intermediary target of monetary policy) and asset price as price index à ¢Ã¢â€š ¬Ã¢â‚¬  monetary conditions index (MCI) , and regard this index as the target of monetary policy operations, therefore it can properly guide central bank make effectively response on monetary policy to change of financial assets price. Gunnarsson and Lindqvist (1997) have discussed the role of asset price on monetary policy from the wealth effect on change of asset price and the effect of inflation. They conclude that the monetary policy should be given more attention on the change of asset price, although it is very difficult to explain. They believe that the change of asset price affect monetary policy as long as this change is long-term change, and in recent years, the impact of this change of asset prices on the economic has been more and more important, so the central bank should spend more energy to analyze the relationship of as set prices and monetary policy, although this relationship is hard to explain but indeed existing. They believe that asset price as an indicator for monetary policy might contribute to the inflation forecast. B.Bemanke and M.Gcrtler (2000,2001) have provided that :In an particular assigned situation , the monetary policy respond to the change of asset price is determined by if existing of the inflation or the deflation pressure on real economy or not, if this change of asset price do not bring the inflation or the deflation pressure on the real economy, then the monetary policy does not need to respond to this kind change of asset price, but If this change of asset price indicates that it indeed brings the inflation or the deflation pressure on the real economy, then the monetary policy should make some response to alleviates this pressure. They propose the above policy seriously under the system frame of flexible currency inflation goal. Cecchetti, Genberg, Lipsky and Wadhwan i (2000) have noted that how the central bank respond to asset price fluctuation mainly depends on the nature of the asset price fluctuation. When only have the shock of financial aspect on macro-economy, the central banks exchange rate policy should make an appropriate response, because doing so will avoid the financial shock on the stability of real economy . When the central banks target is to minimize the fluctuations of Inflation ratio and economic output gap to their target value , the central bank take possible action to eliminate the negative effects of financial volatility is a very good thing. 2.2 Empirical Studies from Chinese Scholars After the Asian financial crisis in 1997, the scholars in China have began to research the relationship between asset price and monetary policy. From a theoretical viewpoint, on the one hand, monetary policy have an impact on asset prices through the adopted operation tool, on the other hand, as a virtual asset relatively to physical asset , asset price fluctuations can also have some impact on peoples consumption and investment behaviour, hence, affect economic development through consumer and investment , further transmit monetary policy purpose to the real economy. Xiaoan Qian (1998) finds that change of asset price make a difficulty in monetary policy transmission mechanism, this will cause the certain effects on monetary quantity management, inflation control and financial risk avoidance. The increase of asset price has been made transmission role of the monetary policy in the currency market change and become a source of funds in the asset markets, causing short-term funds long-term occupation , so that the transmission mechanism of monetary policy to occur difficulty. Part of funds seperated from the bank system, directly to the virtual asset markets. Wenjun Xun (2000) believes that the development of capital market increase the number of the emerging non-bank organization such as superannuation fund, the mutual fund, the Insurance company and so on, the bank also participates in the competition of capital market , the effect of the capital market on the real economy gradually highlight, the transmitted mechanism of monetary policy increases, economic subject and its behavior are diversity, uncertainty about the economical movement increases, therefore the transmitted mechanism of monetary policy is more complex. They thinks that Central Banks monetary policy should control official interest rate through the market , thus indirectly influence the bond and the stock market price in capital market, further influence real economy, achieves the monet ary policy goal. Qiang Qu (2001) has found that it is difficult to put asset prices as the goal of direct control of monetary policy in the monetary policy operations, the possibilities and accuracy of establishment of general asset price index is very small, asset prices can only be used as an indirect reference, in short, to concern on it , but not target on it. Gang Yi and Zhao Wang (2002) have considered that monetary policy have impact on financial asset prices (in particular the stock price), the relationship of currency quantity and inflation not only depends on the price of goods and services, and in a certain degree depends on the stock market. Tianyong Guo(2006) has affirmed the role of asset price fluctuation on real economy , financial stability and monetary policy through analysis, at the same time, he also points out that the asset price as regulatory targets exist difficulties. Chang Cui (2007) analysis the role of asset price on monetary policy through th e model , in asset price inflation period, the central bank can take the measure of interest rate for a given period too control asset price fluctuation, and control the money supply when asset price bubbles exist will receive immediate effect. While in asset price downturn period , interest rate adjust asset price have obvious and relatively durable effect. Yuanquan Yu (2008) obtains through the empirical analysis: the asset price has a certain influence on macroeconomic , particularly the effect of house price is more obvious. Therefore, the Central Bank must give the appropriate attention and control on asset price in the implement of monetary policy . In an conclusion, the asset price fluctuations have an certain impact on the ultimate objective of monetary policy, we can not ignore the unique role of asset price on the transmission mechanism of monetary policy and the macro-economic activities. The central bank should concentrate on the effect of asset prices on monetary policy, particularly in asset prices fluctuations periods, the vast majority of economists believe that the central bank should take an certain monetary policy to address and reduce the negative effect of the economy. For most of research focuses on the study of asset price fluctuation and its relationship with monetary policy, the role of asset prices in the transmission mechanism of monetary policy , as well as the effect size issues ,this dissertation based on the domestic and foreign scholar research results , deeply analyzes the transmitted mechanism of monetary policy in asset price through the impact of monetary policy on asset price . 3. Data Description This dissertation focus on the relationship between asset price and monetary policy in China according to the quarterly statistics during the year of 1998 to 2008. This dissertation mainly use the stock price (index) and house price(hsp) as indicators of asset price , and use boarder money supply (m2), financial institution loan (loan), real rate (rate) as indicators of monetary policy for simplicity. Due to the amount of the data of these variables are really great, we take log of these variables to analyze. This dissertation get all needed data from China Economic Information Network, which is a professionals institution engage in the development of economic data resources and services, provide data support, data integration, and other business data analysis for government and research institutions. All the quarterly data we need from 1998 to 2008 is recorded in the China Economic Information Network. à £Ã¢â€š ¬Ã¢â€š ¬ 3.1 Indicators for asset price in China Asset prices generally including stock prices, bonds, prices, and even exchange rate, and other financial assets and house prices. However, the stock price and house price have a significant effect on real economy, and its fluctuations can have a key role in monetary policy decision-making, hence, in this dissertation , we will use the stock price and house price refer to the asset price. In particular, the Shanghai securities composite index is on behave of the stock price for data limitations, Shanghai securities composite index is established by the Shanghai stock market to reflect the Shanghai securities trading market overall trend. House price is on behave of the average house price in China. We can easily get these data from the China Economic Information Network. 3.2 Indicators for monetary policy in China Monetary policy refers to the Government or the Central Bank influence economic activity, especially by money supply control and regulation of interest rates. To achieve a specific goal or maintain target à ¢Ã¢â€š ¬Ã¢â‚¬  for example, curbing inflation ,achieving full employment and economic growth, directly or indirectly through open market operations and setting the minimum reserve rate. There are many factors needed to be consider in implementing monetary policy, for data restrictions, in this dissertation ,we mainly consider the variable of boarder money supply, financial institution loan and real rate. First, boarder money supply (lnm2) indicates the change of aggregate supply and pressure condition of inflation in the future. In china, boarder money supply is narrow money supply plus the saving, foreign currency and fiduciary deposits of government, organizations, services, businesses and institutions in financial institution. Boarder money supply can be used as a medium and long-term equilibrium target to regulate of financial markets .It is usually the rate of boarder money supply increasing should be controlled at the sum rate of economic growth and price inflation, monetary movement. Second, financial institution loan have some disadvantage as a indicators of monetary policy. First, it is closely associated with the monetary policy objective. Currency circulation and deposit currency caused by loan, the Central Bank control the size of the loan, which also mean to control the money supply. Second, financial institution loan is an accuracy an endogenous variable , loan size is positive correlation with loan demand. As a policy variables, loan size and the demand also have a positive correlation. Furthermore, data of financial institution loan is easily accessible . Third, real rate refers to the real rate of interest return that the depositors and investors can get after eliminating of inflation rate, it is calculate as nominate rate minus CPI. Real rate can be used as the indicator of Central Banks monetary policy due to following reasons : (1) real rate reflect the supply of money and credit, and able to show the relative supply and demand, it is correlation with nominal interest rate ,High level of interest rate is thought to be a tight, low interest rate level of convergence are considered monetary relaxation. (2) real rate belongs to the Central Bank , the Central Bank can use this tools to increase or decrease in interest rates. Table 1: denotation for Variables denotation Variables Implication Lnindex Shanghai securities composite index Shanghai securities trading market overall trend Lnhsp House price Real estate price Lnloan Loan financial institution aggregation loan domestic Lnm2 M2 boarder money supply: M2+M1 Rate Real rate nominate rate minus CPI. 4. Economic Theory and Econometric Model The effectiveness of monetary policy depends not only on the sensitivity of economic subjects on policy signal , but also on numerous external factors of financial system. According to the traditional Keynesian theory, when implementing expansionary monetary policy, increase of money supply will lead to rate decline, i.e. capital costs decreasing, further increasing investment expenditure, hence increasing aggregate demand and aggregate output. Meanwhile, increase of money supply will lead to the bank reserve and deposit increase, thus enhanced bank to increase the loan quantity, the fund that the borrower attains increase, then the total quantity investment will increase, which also lead to the quantity of aggregate demand increase, hence, the total output also rise. We will use following econometric model to analyze the relationship of asset price and monetary policy. 4.1. Analyzing correlation coefficient The correlation coefficient is a measure of two variables relate to each other and their close degree of effective tools. Its absolute value is close to 1 description of relevance, the stronger between variables, the more its relevance with 0. If the correlation coefficient is positive, then the variables presented to changes in the relationship, with one variable with another variable changes. But if the correlation coefficient is negative, then the variables are changes in the relationship in the opposite direction. Using correlation coefficient can be better measured variables and between monetary policy and asset price correlation between Extent its positive and negative symbol can indicate the variable ask changes direction. Generally used to be associated matrix said. 4.2. Testing for Nonstationary In time series, stationary is a key conceptà ¯Ã‚ ¼Ã…’ as it allows powerful techniques for modelling and forecasting to be developed. Stationary is generally regarded as some pattern of data stable or equilibrium. Stationary time series have constant mean and variance, but its covariance only determined by the time distance. However, when time series could not analyze as stationary, this types of time series always have a strong upwards or downward trend over time, we call it as nonstationary, and we can use differencing as an effective tool to transform a nonstationary time series into a stationary time series. Sometimes, Transforming a nonstationary time series into a stationary one needs more than once differencing operation. Generally speaking, if the differencing needs to be operated at least d times to achieve a stationary time series where d is the order of integration, then the time series is said to be integrated of order d, denoted by I(d). Hence, the I(1) time series also referred to have a unit root, while the I(0) time series are stationary. Dickey and Fuller (1979) provided an effective method to test a time series is stationary or nonstationy time series, which is also called as Dickey-Fuller (DF) test. The elementary object is to test the null hypothesis that the time series have a unit root or not. The model the Dickey-Fuller (DF) test involves bellows In this dissertation , indicates the variables on monetary policy and asset price at time t. ÃŽÂ ± denotes unknown parameter and denotes the trend. denotes the first difference which . Also, the t-statistic for testing the null hypothesis that H0: =0 against the alternative hypothesis H1: 0. In this paper, since house price , boarder money supply and loan have a strong upward trend , so we test these time series under the model H0 : against H1: While the index and real rate variable we consider under the model H0: against H1: We also can identify the fittest lag k by running t he ADF(k) test, choosing the fitted order k that gives the minimum AIC and BIC. 4.3 Cointegration Formally, Engle and Granger (1987) defined the cointegration as if there exists a linear combination of two or more I(d) time series which is I(d) with d'd. In most case, two cointegrated time series has a unit root ,as I(1),a their combination is stationary ,as I(0). In practice, we usually use cointegration test to exam the long-run relationship among variables in economics. If times series have relationship between variables, and the trend of the two time series has been common, and thus there will be a linear combination of these time series give us an stationary time series. In this dissertation , we test the long-run relationship between monetary policy variable and asset price variable by cointegration test .First, we consider the regression of two I (1) time series. The model is To test { } and { } are cointegrated, we need to exam that the residuals term { } is stationary .If the residuals term is I (1), then this two times series do not have a cointegration, othe rwise , if the residuals term is I (o), then this two times series are cointegrated. Under this case, to test the residuals for unit root ,we can conduct DF/ADF-statistic test. In this paper, we denote that monetary policy variables as and we regress on a constant and one of the asset price variables as . 4.4. Causality Test Granger (1969) provided that Granger causality test can apply generally for testing the causal relationship on two time series.Granger causality means that if { } Granger causes{ } then { } have a predict power of { } , given any other variables. More formally, it is said that { } Granger causes { }; when the forecast of given data on { } and { } outperforms the forecast of given data on { }only. Granger causality is only related to the predictability of { } using { } and is not concerned as to whether{ }causes { }, it could be that { } Granger causes { } but { } is not causal for { },and vice versa. To test for Granger causality, we could estimate the regression by OLS In this dissertation , denotes an indicator of asset price, i.e. Shanghai composite index (lnindex) , house price ( lnhsp) , Also,denotes the indicator of monetary policy, i.e. financial institutions aggregate loan (lnloan), broad money (lnm2), real interest rate (rate) . Then conduct an F test on the null hypothesis against the alternative at least one of the is not zero. If we reject the null hypothesis, then { } has predictive power for { } and therefore, { } Granger causes { }, on the other hand, if we fail to reject the null hypothesis, then { } has no predictive power for { }, therefore, { } does not Granger causes { }.We usually test the two times series for Granger causality in pairs, that is, first test whether { } Granger causes { } and then test whether { } Granger causes { }.If two variables have Grange causality relationship in both directions, i.e. { } Granger causes { } and { } Granger causes { }, then we could regard these two varibles have causality relationship in both directions, that means these two variables are related. If two variables have Granger causality in one direction, e.g. { } Granger causes { } but { } does not Granger cause { }, then we can conclude that these two variable just have a one way causality relationship. 5. Presentation and Interpretation of Results 5.1. correlation coefficient between monetary policy variables and asset prices variables We analysis the correlation between monetary policy variables and stock prices variable according to the data provided by China Economic Information Network, and the correlation coefficients are presented in Table 2 and Table 3. Table 2 Correlation coefficient between lnindex and lnloan,lnm2,rate in 1998-2008 Lnindex Lnloan Lnm2 Rate Lnindex 1.000000 Lnloan 0.4829 1.000000 Lnm2 0.4867 0.9980 1.000000 Rate -0.4717 -0.9013 -0.9067 1.000000 As we can see, stock price (Lnindex) has correlation relationship with all monetary policy variables. With a higher stock price, loan and money supply will be increased, while real rate will be decreased. For monetary variables, loan and M2 have a strong positive correlation, and M2 have a strong negative correlaton with real rate. In conclusion , for the stock price variable, it has basically the positive correlation with the loan and money supply variables , and has negatively correlatio n with the real rate. Table 3 Correlation coefficient between lnhsp and lnloan,lnm2,rate in 1998-2008 Lnhsp Lnloan Lnm2 Rate Lnhsp 1.000000 Lnloan 0.9667 1.000000 Lnm2 0.9633 0.9980 1.000000 Rate -0.8453 -0.9013 -0.9067 1.000000 From table 3, we can see house price (Lnhsp) has correlation relationship with all monetary policy variables. With a higher house price, loan and money supply will be increased, while real rate still will be decreased. In conclusion ,for the house price variable, it has basically the strong positive correlation with the loan and money supply variables , and has strong negatively correlation with the real rate. 5.2.Results for unit root test We exam monetary policy variables and asset prices variables by Augmented Dickey-Fuller (ADF) to test the stationary of time series. First ,we choose the AIC and BIC to determine the fitted lag it suggest that the optimal lag for time series is lag k =1,Then we run ADF to test stationary of time series. Results are below: Table 4à ¯Ã‚ ¼Ã… ¡Augmented Dickey-Fuller Unit Root Test for Variables Series ADF Test critical values Results 5% 1% Lnhsp -1.685 -3.41 -3.96 have a unit root Lnindex -2.085 -2.86 -3.43 have a unit root Lnm2 -1.992 -3.41 -3.96 have a unit root Lnloan -1.993 -3.41 -3.96 have a unit root Rate -1.185 -2.86 -3.43 have a unit root We conserder Lnindex and Rate for unit root test under case which is constant without trend, and get the ADF values are -2.085, and -1.185.The critical values are from the asymptotic critical values of the ADF statistic table. From the ADF stat istic table. We know the critical value at the 5% significant level is -2.86, while it is -3.43 at the 1% significant level. Since Lnindex ADF value is-2.085, which is greater than -2.86 and Rate ADF value is -2.400,which is also greater than -2.86, so we fail to reject the null hypothesis at the 5% significance level and conclude that we have evidence that both Lnindex and rate have a unit root, and also mean that the time series is nonstationary. Since Lnhsp Lnm2 and Lnloan of the data have a strong upwards or downward trend ,so we exam these three time series under case which is constant with trend,and get the ADF values are -1.1685, -1.992 and -1.993. From the ADF statistic table.We know the critical value at the 5% significant level is -3.41, while it is -3.96 at the 1% significant level. Since ADF value of Lnhsp, Lnm2 and Lnloan is -1.1685, -1.992 and -1.993 respectively , which are all greater than -3.41 at w at the 5% significance level , and which is also greater than-3.96, at the 1% significance level ,so we fail to reject the null hypothesis at the 5% and 1%significance level and conclude that we have evidence that Lnhsp Lnm2 and Lnloan have a unit root, and the time series is nonstationary. To test the orders of integration of all the time series, hence, we do first differencing to all time series and add an initial D to each variables to indicate the new variables. We use ADF-test again to test all the first differencing variables as above. The fitted lag we consider is also lag=1, the results are shown in Table 4. Table 5à ¯Ã‚ ¼Ã… ¡Results from ADF-test with first difference variables Series ADF Test critical values Results 5% 1% DLnindex -3.525 -2.86 -3.43 I(0) Stationary DLnhsp -4.484 -3.41 -3.96 I(0) Stationary DLnm2 -4.876 -3.41 -3.96 I(0) Stationary DLnloan -3.854 -3.41 -3.96 I(0) Stationary at 5% Have a unit root at !% DRate -3.943 -2.86 -3.4 3 I(0) Stationary From above table, the ADF values of all 5 variables are -3.525, -4.484 -4.876, -3.854 and -3.9459 which are smaller than the critical values at 5% significant level, so we reject the hypothesis at 5% significant level , and conclude that we have no evidence that all the first difference time series have unit roots. It suggests that, after first difference for each of the time series, data have been stationary. Moreover, it shows that the original series of lnindex,lnhsp,lnloan,lnm2 and rate are I (1); their orders of integration are 1.While at 1% significant level , the ADF value of Dlnloan is greater than critic level ,so we conclude that Dlnloan have a unit root. 5.3 Results for cointegration test We need to test the long-run relationship of asset price variables and monetary policy variables by using cointegration test , so we use ADF-test to test the residual .From the statistic table ,we know the 5% critical values is -3.34.while , the 1% critical values is -3.9. 5.31 Cointergrate test of Lnm2 and Lnindex Fitted regression model is Lnm2=7.64+0.62Lnindex+ For the residual, the ADF-test results is -4.61, Since -4.61 is smaller than critical values, so we reject H0 at both 5% and 1% significance level and we have evidence that the residual is stationary, hence we conclude that the Lnm2 and Lnindex have cointegration relationship. 5.32 Cointergrate test of Lnm2 and Lnhsp Fitted regression model is Lnm2=-3.58+2.02 Lnhsp + For the residuals, the ADF-test results is -4.883, Since -4.883 is smaller than critical values, so we reject H0 at both 5% and 1% significance level and we have evidence that the residuals is stationary, hence we conclude that the Lnm2 and Lnhsp have cointegration relationship. 5.33 Cointergrate test of Lnloan and Lnindex Fitted regression model is Lnloan =8.02+0.52Lnindex+ For the residuals, the ADF-test results is -4.777, Since -4.777 is smaller than critical values, so we reject H0 at both 5% and 1%significance level and we have evidence that the residual is stationary, hence we conclude that the Lnloan and Lnindex have cointegration relationship. 5.34 Cointergrate test of Lnloan and Lnhsp Fitted regression model is Lnloan =-1.61+1.72Ln Lnhsp + For the residuals, the ADF-test results is -4.397, Since -4.397 is smaller than critical values, so we reject H0 at both 5% and 1% significance level and we have evidence that the residuals have a unit root, hence we conclude that the Lnloan and Lnhsp have cointegration relationship. 5.35 Cointergrate test of Rate and Lnindex Fitted regression model is Rate =21.08 -2.67Ln Lnhsp + For the residuals, the ADF-test results is -4.473, Since -4.473 is sm aller than critical values, so we reject H0 at both 5% and 1% significance level and we have evidence that the residuals have a unit root, we conclude that Rate and Lnindex have cointegration relationship. 5.36 Cointergrate test of Rate and Lnhsp Fitted regression model is Rate =63.29 -7.91Ln Lnhsp + For the residuals, the ADF-test results is -4.4381, Since -4.4381 is smaller than critical values, so we reject H0 at the 5% significance level and we have evidence that the residuals have a unit root, hence we conclude that Rate and Lnhsp have cointegration relationship. In sum, by the testing procedures as above, we can conclude that monetary policy variables and asset price variables have cointegration relationship, which means that they have long-run relationship based on quarterly data during the period from 1998 to 2008 in China. The central bank should focus on the long-run relationship of asset price and monetary policy. 5.4 Results from Granger Causality Test We exam the short-run relationship between asset price variables and monetary policy variables using Grange Causality Test, and reveal the predict power of these variables. We proceed the Granger causality test of asset price variables and monetary policy variables in lag=1and 4. Results are shown below. 1. . Causality test between monetary policy variables and stock prices variable Table 7: Lag=1 Granger causality test Null Hypothesis F-Values Test Critical Values Results 10% 5% Lnloan Does Not Granger Cause Lnindex F(1,40)=0.11 4.61 5.99 Accept Null Lnindex Does Not Granger Cause Lnloan F(1,40)=0.03 4.61 5.99 Accept Null Lnm2 Does Not Granger Cause Lnindex F(1,40)=0.14 4.61 5.99 Accept Null Lnindex Does Not Granger Cause Lnm2 F(1,40)=0.07 4.61 5.99 Accept Null Rate Does Not Granger Cause Lnindex F(1,40)=0.01 4.61 5.99 Accept Null Lnindex Does Not Granger Cause Rate F(1,40)=0.4 6 4.61 5.99 Accept Null If 2 times F-values greater than critical values , we reject null. From table 7, we can conclude that in the lag of 1, at the 10% and 5% significance level ,stock price (Lnindex) variable and monetary policy variables have no Granger cause relationship, so stock price variable has no predict power to monetary policy variables, and also monetary variables has no predict power to stock price variables. Table 8: Lag=4 Granger causality test Null Hypothesis F-Values Test Critical Values Results 10% 5% Lnloan Does Not Granger Cause Lnindex F(4,31)=1.16 7.78 9.49 Accept Null Lnindex Does Not Granger Cause Lnloan F(4,31)=0.03 7.78 9.49 Accept Null Lnm2 Does Not Granger Cause Lnindex F(4,31)=0.89 7.78 9.49 Accept Null Lnindex Does Not Granger Cause Lnm2 F(4,31)=0.16 7.78 9.49 Accept Null Rate Does Not Granger Cause Lnindex F(4,31)=0.62 7.78 9.49 Accept Null Lnindex Does Not Granger Cause Rate F(4,31)=0.28 7.78 9.49 Accept Null If 2 times F-values greater than critical values , we reject null. As presented on Table 8, in the lag of 4, at the 10% and 5% significance level stock price (Lnindex) variable and monetary policy variables still have no Granger cause relationship. We can not use monetary policy variable to predit asset price variable , and vice versa. This also show that adjusting monetary policy variable affects stock markets price level is very difficult, even assuming it has the effect, it must pass through a very long time period, the effect can be appearance. 2. Causality test between monetary policy variables and house prices variable I will analyze the causality relationship between house prices variable and monetary policy variables in the same way above. Results are shown as Table 9 and Table 10 Table 9: Lag=1 Granger causality test Null Hypothesis F-Values Test Critical Values Results 10% 5% Lnloan Does Not Granger Cause Lnhsp F(1,40)=3.58 4.61 5.99 Rejec Null Lnhsp Does Not Granger Cause Lnloan F(1,40)=0.13 4.61 5.99 Accept Null Lnm2 Does Not Granger Cause Lnhsp F(1,40)=2.74 4.61 5.99 Reject Null at 10% Accept Null at 5% Lnhps Does Not Granger Cause Lnm2 F(1,40)=0.22 4.61 5.99 Accept Null Rate Does Not Granger Cause Lnhsp F(1,40)=1.57 4.61 5.99 Accept Null Lnhsp Does Not Granger Cause Rate F(1,40)=2.84 4.61 5.99 Reject Null at 10% Accept Null at 5% If 2 times F-values greater than critical values , we reject null. From table 9 , we can conclude : in the lag of one, at the 5% significance level, loan can Granger Cause house price, but house price does not Granger cause loan , these two variable just have uni-directional Granger cause relationship, house price and other monetary variable have no Granger cause relationship. While at the 10% significance lev el , loan and boarder money supply can Granger Cause house price, and also house price can Granger cause Rate. This also indicates that adjusting financial institution loan and boarder money supply can have certain effect on house price in short time, based on quarterly data, we can use financial institution loan and boarder money supply to predict the house price in a certain period. Table 10: Lag=4 Granger causality test Null Hypothesis F-Values Test Critical Values Results 10% 5% Lnloan Does Not Granger Cause Lnhsp F(4,31)=2.32 7.78 9.49 Accept Null Lnhsp Does Not Granger Cause Lnloan F(4,31)=0.12 7.78 9.49 Accept Null Lnm2 Does Not Granger Cause Lnhsp F(4,31)=1.69 7.78 9.49 Accept Null Lnhsp Does Not Granger Cause Lnm2 F(4,31)=0.36 7.78 9.49 Accept Null Rate Does Not Granger Cause Lnhsp F(4,31)=2.4 7.78 9.49 Accept Null Lnhsp Does Not Granger Cause Rate F(4,31)=2.44 7.78 9 .49 Accept Null If 2 times F-values greater than critical values , we reject null. As presented on Table 10, in the lag of 4, we can see asset price variables and monetary variables have no Granger causality relationship at 10% and 5% significance level. Synthesizes the above analysis, we can conclude that the stock price and monetary variables have no two-way causality relationship in short run, and monetary policy have no impact on stock price in short run . But in some certain degree, loan and boarder money supply can Granger cause house price, it means that loan and boarder money supply can predict house price in short time. 6. Conclusion In this dissertation , we study the relationship between asset price and monetary policy in China by using quarterly statistics during the year of 1998 to 2008. We use the stock price (index) and house price (hsp) as indicators of asset price , and use boarder money supply (m2), financial institution loan (loan), real rate (rate) as indicators of monetary policy. We expect those asset price variables would have some effect on the monetary policy. Hence, we process these time series through ADF-test, Cointegration-test and Granger causality test to reveal the long-term and short-term relationship among them. Then, from the results of cointegration test, the results suggest that the indicators of asset price have cointegration with monetary policy base on the quarterly data during 1998 to 2008 in China, hence, it implies that there is long-term relationship between the fluctuations of asset price market and monetary policy decision-making in China. Further, we use Granger ca usality test to exam the short term relationship. In the lag=1 and 4 , the results reveal that stock price variable and monetary policy variables have no causality relationship in short run, stock price variable has no predict power in monetary policy variables. But in some certain degree, that loan and boarder money supply can predict house price in short time. In summary, from analysis in this dissertation , we can get some useful policy implications in China. First, improving relevant conditions of asset prices and monetary policy transmission mechanism .As Chinas capital market development, asset price transmission channels to monetary policy gradually disappear. Although current credit market and money market funds directly or indirectly to concentrate on the asset market ,which indeed create opportunities for the monetary policy transmission mechanism , but do not form a valid investment and consumption demand, to some certain extent, resulting in distortion of the monet ary policy transmission mechanism. As money supply increasing ,financial insistutions have sufficient funds, make loan enlarge, and also cause rapid increasing in stock price. Dramatic fluctuations in asset prices, on the one hand resulted in financial system instability and, on the other hand, asset price transmission channels to monetary policy is also not very smooth. However, as the stock market, house price and other assets further development, the effect of traditional monetary policy transmission mechanism will gradually diminish, the role of asset price transimitted to monetary policy will play an important role, so now we must improve relevant conditions of asset prices and monetary policy transmission mechanism by Tobin q effect, the wealth effect, balance sheet effects and many other channels . Second, Monetary policy should focus on asset price fluctuation. The asset price has not been able to take as the independent regulation target of the monetary policy ,but sh ould take it as the auxiliary monitor target of currency regulation ,integrates to the field of the Central Bank monetary policy Central Bank .The central bank should establish indicator system relative to asset price monitoring, and make corresponding respond to estimate the impact of market movement and change of asset price on macro economic fluctuation, then determine the trend of monetary policy, implements the essential regulative behaviour, meanwhile , the central bank must clarify the shocks of asset price fluctuation. Third, the central bank should focus on the impact of the real estate market on real economy. Empirical results show that housing price has played a certain role in the transmission mechanism of monetary policy in China, the correlation with monetary policy variable is extremely high. The central banks should focus on the change of real estate market because the real estate industry is pillar industries, the real estate market price fluctuation will affect our investments, further to fluctuations in related industries, to even worse, it may affect the whole macro economy development. Accordingly, the Government should concentrate on impact of the real estate market on macro economy.

Black Women in Sports Sexuality and Athleticism Essay

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