1、中文3150字本科毕业设计（论文） 外 文 翻 译 原文 ： Regional Business Cycle and Real Estate Cycle Analysis and The Role of Federal Governments in Regional Stability For the last two decades the topic of the real estate cycle has gained a lot of attention not only in the fields of m
2、icro and macro economics, but also in the field of finance and investment. Recently real estate became a lucrative investment option for investors (Leonhardt 23； Dhar and Goetzmann 15). Securitization of the real estate market was one important trend that attracted many investors into this field. Fu
3、rther, now there are more investors who can participate in the global real estate market than a decade ago (Case, Goetzmann, and Rouwenhorst, 9). Nevertheless, in recent decades the world has experienced a couple of global real estate fluctuations including recent U.S. real estate crisis. This makes
4、 researchers and investors wonder about the structures of real estate cycles and how they are related to other economic activities in the nation as well as throughout the world. Many studies show that the real estate cycle has a direct impact on the behavior of households, investors, b
5、anking systems, as well as on the national economy (Case 8, Wheelock 35, and Barlevy 1). Very few studies, however, have compared and analyzed national and state level business cycles with the national and regional real estate cycles. This comparison is important for at least three reasons: first, t
6、he clear idea about the national and state level real estate cycle will help home owners and real estate investors minimize their losses. Second, it will help proper authorities (government, mortgage brokers, banks, etc.) to make effective decisions. Third, future researchers will have vivid underst
7、anding of states economic structures and better understanding of the behavior of the real estate cycles. This paper strictly focuses on macroeconomic perspective of real estate science and analyzes the patterns of real estate cycles. Thus, the study has three main objectives. First, using Markov- Sw
8、itching estimation technique, this study compares the U.S. national and state level business cycles with the U.S. national and state level real estate cycles. Second, depending on the formation of the state level real estate cycles, this study categorizes different states, and _nally it analyses the
9、 severity of the state level real estate cycles. The rest of the paper is organized as follows. First, we discuss related literatures, second we explain the data descriptions, third we provide model and methods, forth we give data description, fifth we state the results by prese
10、nting comparison of business cycles and real estate cycles, thus categorize states depending on the formation of real estate cycles. To give some idea how the U.S. states real estate sector converges during the different phases of the real estate cycles, in section sixth we provide a convergence ana
11、lysis and finally we conclude in the section seven In the United States national business cycles are calculated and dated by the National Bureau of Economic Research (NBER). Hamilton 20 used state space Markov Switching estimation technique on the U.S. GDP data to estimate busine
12、ss cycle turning points. Hamiltons estimated dates coincided with the dates provided by the NBER which confirms the validity of the Markov Switching estimation technique to measure business cycle turning points. Bold in 3 compared with different business cycles turning point dating methods in the U.
13、S. economy. He concluded that the Stock and Watsons 20, 20 experimental business cycles indicators based on Kalman Filter algorithm and Hamiltons Markov Switching 20 estimation technique outperforms all other business cycles dating methods. Crone 12, 13 used Kalman Filter estima
14、tion technique on the U.S. state level data and grouped U.S. into eight economic regions based on regional business cycles similarities. Using Hamiltons Markov Switching estimation technique on the state level coincident indexes6 Owyang, Piger andWall 27 and later Giannikos and Mona 16 dated the tur
15、ning points of the U.S. state level business cycles. Both studies show that the U.S. state level business cycles do not necessarily coincide with the national business cycles. A recent study by Crone 14 also estimates the U.S. state level business cycles using diffusion indexes. His study concludes
16、that diffusion indexes are better data sets to track or to forecast regional business cycle turning points. Exploring a threshold autoregressive (TAR) model Lizieri, Satchell, Worzala, and Dacco 24 found that regime switching model gives more accurate picture of real esta
17、te market performance than simple linear model. By using real interest rate as a state variable, they compare the behavior of the U.S. and the U.K real estate market. To measure the U.S. real estate market performance the authors used monthly data of the Real Estate Investment Trust (REIT) from Dece
18、mber 1972 to March 1995. The U.K. real estate performance was measured by the monthly data of International U.K. property Price index from January 1975 to August 1995. They found distinct real estate regimes in the U.S. and in the U.K. Thus they concluded that the real interest rate plays a signific
19、ant role as an indicator of real estate performance in both countries, i.e., the property prices fall sharply during the high interest rate regimes and the reverse happens during the lower interest rate regimes. Similarly, Carlino and DeFina 7, 5, 6 showed that changes in interest rate by the moneta
20、ry authorities has differential effect on regions throughout the United State. The regions specialized in construction, housing, or real estate based industries get affected differently compared to manufacturing or service based industry regions. Proposing a simple model
21、of lagged supply response to price changes and speculation in housing market Malpezzi and Wachter 25 generated real estate cycles. They found that demand condition and speculation play major role in housing market and real estate cycles. Further, they showed that the price elasticity of supply is th
22、e dominant component of speculation. The largest effects of speculation were observed when supply is inelastic. Three different data sets are used in this study: 1) the U.S. fifty states coincident indexes； 2) the housing price indexes for the fifty U.S. states and the nat
23、ion； and 3) national business cycle turning dates. Following are the descriptions of the data sets we used for this study. The U.S. fifty states monthly coincident indexes are provided by the Federal Reserve Bank of Philadelphia dating from 1979:IQ . 2007:IIIQ. This data set is developed by Crone 12 estimating four latent dynamic factors of each state. The
