区域经济周期和房地产周期分析【外文翻译】

1、本科毕业设计（论文）外 文 翻 译原文：regional business cycle and real estate cycle analysis andthe role of federal governments in regional stabilityfor the last two decades the topic of the real estate cycle has gained a lot of attention not only in the fields of micro and macro economics, but also in the field of f

2、inance 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. further, now there are more investors who can particip

3、ate 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 researchers and investors wonder about the structur

4、es 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 behaviorof households, investors, banking systems, as well as on the national economy (case 8, wheeloc

5、k 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, the clear idea about the national and state level real estate cycle

6、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 understanding of states economic structures and better understanding of th

7、e 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-switching estimation technique, this study compares the u.s. national

8、and state levelbusiness 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 categorizesdifferent states, and _nally it analyses the severity of the state level real estate cycles. the rest of the paper

9、 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 presenting comparison of business cycles and real estate cycles, thus categorize states dependin

10、g 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 analysis and finally we conclude in the section seven in the united states national business c

11、ycles 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 business cycle turning points. hamiltons estimated dates coincided with the dates provided by the nber which confirms

12、 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.s. economy. he concluded that the stock and watsons 20, 20 experimental business cycles indicators based on kal

13、man filter algorithm and hamiltons markov switching 20 estimation technique outperforms all other business cycles dating methods. crone 12, 13 used kalman filter estimation technique on the u.s. state level data and grouped u.s. into eight economic regions based on regional business cycles similarit

14、ies. using hamiltons markov switching estimation technique on the state level coincident indexes6 owyang, piger andwall 27 and later giannikos and mona 16 dated the turning points of the u.s. state level business cycles. both studies show that the u.s. state level business cycles do not necessarily

15、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 that diffusion indexes are better data sets to track or to forecast regional business cycle turning points. exploring a threshold au

16、toregressive (tar) model lizieri, satchell, worzala,and dacco 24 found that regime switching model gives more accurate picture of real estate 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 ma

17、rket. to measure the u.s. real estate market performance the authors used monthly data of the real estate investment trust (reit) from december 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

18、 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 significant 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 rever

19、se happens during the lower interest rate regimes. similarly, carlino and defina 7, 5, 6 showed that changes in interest rate by the monetary authorities has differential effect on regions throughout the united state. the regions specialized in construction, housing, or real estate based industries

20、get affected differently compared to manufacturing or service based industry regions. proposing a simple model 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 majo

21、r role in housing marketand real estate cycles. further, they showed that the price elasticity of supply is the 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 coi

22、ncident indexes; 2) the housing price indexes for the fifty u.s. states and the nation; 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 federalreserve bank of ph

23、iladelphia dating from 1979:iq . 2007:iiiq. this data set is developed by crone 12 estimating four latent dynamic factors of each state. the four variables are: the total number of jobs in nonagricultural establishments, average weekly hours in manufacturing, the unemployment rate, and the real wage

24、 and salary disbursements. this is one of the most comprehensive monthly data set available for state level economic analysis. the reason for using state level coincident indexes for this study is that there is no monthly gross state product (gsp) data available for the u.s. states. gsp data are in

25、the annual basis, but state level recession or expansion can begin and end within a year. the housing price index (hpi) data used in this study is published by the office of federal housing enterprise oversight (ofheo)8. the hpi is a broad measure of the movement of single-family house prices9, whic

26、h measures weighted average changes in repeat sales, mortgage defaults, prepayments, re_nancings, and housing affordability in specific geographic areas. the primary housing data are collected and provided by fannie mae and freddie mac to the ofheo. the ofheo generates hpi by using a modified versio

27、n of the case-shiller geometric weighted repeat-sales procedure (calhoun, 4). the hpi by ofheo is more accurate and complete measure of housing price change compare to s&p/ case-shiller indexes10 or constant quality housing price index (cqhpi)11. the hpi covers more transactions and geographic areas

28、 compared to other two data sets. we used quarterly hpi for fifty u.s. states from 1979:iq to 2007:iiiq. for the national real estate cycle analysis, we also used quarterly hpi data for the u.s. provided by the ofheo. national business cycles and its turning points dates are listed by the nber from

29、1979:iq to 2004:iq. first, we compare the national business cycle with the national real estate cycle. we use business cycle phases (e.g., recession and expansion) for our comparison. in the following figures, vertical lines represent national recessions dated by the nber. recessionary states are me

30、asured in 0 to 1 scale, where 0 represents zero probability of recession, and 1 represents full probability of recession. therefore if the cycles are under 0.5 probability scale we called these the state expansion; those above 0.5 probability scale we called the state recession. according to the fol

31、lowing figures, the u.s. experienced four major national recessions13 during1979:iq to 2007:iiiq time periods. two recessions were at the beginning of the 1980s, the third one was at the beginning of the 1990s, and the last one was at the beginning of the 2000. real estate recessions are marked by t

32、he solid (curve) lines in figure 4.1. according to the figure 4.1, the u.s. has experienced two major real estate recessions during 1979:iq to 2006:iq period. one started at 1981:iiq and ended at 1985:iq, and the second one started at 1989:ivq and sustained until 1999:iiiq. for both cases, the real

33、estate recessions started before the national recessions, and continued several periods after the national recession ended. the result indicates that even though the real estate is one of the biggest industries in the united states, not all national recessions are due to the real estate sector fluct

34、uation. in many cases real estate fluctuations may play an important role in some national recessions. nevertheless, just from the figure 4.1 alone, we cannot confirm that real estate was the sole reason of two national recessions of the1980s and the 1990s. analyzing figure 4.1, we also observe that

35、 in the recent years, starting from 2006:iiq, the probability of another nation wide real estate recession is very high. a downturn in real estate in 2007:iiiq occurred on the national level and on the state level in about forty five out of fifty u.s. states (appendix 4.a.2). however, so far only ni

36、neteen states tend to have high probability of entering into state level economic recession, but there is no indication of national economic recession.this paper also found that twenty two states15 have state level real estate cycle patterns similar to the national. in other words, these twenty two

37、states experienced two real estate recessions as the nation during the 1980s and the 1990s; the span of recession, however, varied throughout the states. nevertheless, the pattern of the cycle is not a sufficient condition for explaining the reasons behind similar real estate variables, other than r

38、eal estates, are more responsible for the formation of the state level business cycle, which might eventually affect those states real estate sectors. in total, six states - alabama, delaware, maryland, new mexico, texas, and washington - fall into this category of states. in figure 4.3: maryland, w

39、e observe how the 1981s, the 1990s and the 2007s marylands real estate cycles not only followed the states state level business cycle patterns with lags, but it also followed the pattern of the national real estate cycle with lags. in figure 4.3 the state level business cycle is marked by the solid

40、red line17 (curve). in all three cases, the solid red line is followed by the national and state level real estate cycles which are marked by the dotted green and blue lines18 respectively.the third group is a mixture of the leading and the lagging states. this set of states sometimes faces the lead

41、ing real estate cycles, and sometimes faces the lagging real estate cycle compared to these states business cycles. nine u.s. states19 fall into this category. in figure 4.4: maine, we observe that during the 1980s and the 2000s, economic and real estate fluctuations, the real estate cycle followed

42、the state level business cycle with four and twenty quarters lags respectively. the 1980s the real estate recession in maine persisted for maximum six quarters where the state level economic recession persisted for twenty quarters. in other words in maine译文：区域经济周期和房地产周期分析在过去二十年的房地产周期的话题，联邦政府在地区稳定中发挥

43、的作用不仅获得了在宏观和微观经济学领域，而且还得到金融和投资等领域的关注。最近房地产成为投资者有利可图的投资选择。房地产市场的证券化是一个重要的趋势，吸引了许多投资者进入这个领域。此外，现在比十年前更多的投资者参与全球房地产市场。然而，近几十年来，世界经历了全球性房地产，包括近期美国房地产危机波动，这使得研究人员和投资者好奇房地产周期和结构和他们是怎样与国家相关以及遍布世界各地的其他经济活动产生关联。许多研究表明，房地产对周期户，投资者，银行系统，以及对国家经济有直接影响。相关研究很少，但是，比较和分析了国家和区域房地产周期和国家和省级层面的商业周期，这个显得比较重要，至少有三个原因：第一，关

44、于民族和国家水准的清晰的概念，房地产周期将帮助业主和房地产投资者将损失减到最低。其次，这将有助于有关当局（政府，抵押贷款经纪人，银行等）进行有效的决策。三，未来研究人员将对各国的经济结构产生生动的了解和较好的房地产周期行为的理解。本文的重点，严格科学的房地产宏观的角度，分析了房地产周期的模式。因此，这项研究有三个主要目标。首先，利用马可夫开关估计技术，这项研究比较了美国的国家和省级层面与美国国家和州一级房地产景气循环周期。二，关于国家一级房地产周期形成的不同，此研究归类不同的国家，nally分析了国家一级房地产周期的严重性。本文的其余部分组织如下。首先，我们讨论相关文献，第二我们解释数据，第三

45、，我们提供模型和方法，第四我们给出数据描述，第五根据我们国家的经济周期和房地产周期比较的结果，此分类取决于国家的房地产形成周期。为了了解美国各州的房地产部门在房地产周期的不同阶段如何汇集，第六段我们提供了一个收敛性分析，最后我们在第7节结束。在美国，全国商业周期，由国家经济研究局（nber）日期计算。汉密尔顿20用美国gdp数据估计状态空间马尔可夫转换技术，估计商业周期的转折点。汉密尔顿的估计日期恰逢由国家经济研究局证实了马可夫转换估计技术的衡量商业周期的转折点所提供的日期的有效性。大胆3比较不同经济周期结果变成美国测年法的转折点。他总结说，基于卡尔曼滤波算法和交换20估计技术汉密尔顿的马尔可

46、夫的股票和沃森的20，20实验商业周期指标，优于所有其他商业周期测年法。利用k这个人对美国国家数据的估算技术，crone根据美国地区经济周期的相似性将美国分成八个经济区。这两项研究表明，美国州一级的商业周期并不一定配合国家商业周期。按科龙14最近的研究还估计，美国经济周期的扩散指数基于国家级。他的研究结论是扩散指数数据集，以更好地跟踪预测区域或商业周期的转折点。探索一门限自回归（tar）的模型。萨切尔和达科24发现，状态转换模型给出了更准确的房地产市场不是简单的线性模型的表现情况。通过使用状态变量的实际利率，他们比较了美国和英国的房地产市场行为。为了衡量美国房地产市场表现，作者使用从1972年

47、12月1995年3月的房地产投资信托基金（reit）月度数据。英国房地产，从1975年1月至1995年8月的月度数据对国际英国房地产价格指数的性能进行了测试。他们发现美国和英国的房地产制度的不同，因此，他们得出结论，实际利率发挥了作为房地产性能指标在这两个国家重要的作用，即在高利率楼价下跌急剧制度，相反情况发生在较低的利率制度。同样，卡诺和defina表明，利率变动，货币当局在整个地区有差别的统一对国家产生了影响。在建筑，房屋，基础产业或房地产专业的地区相比，制造或服务为主的产业区域得到的影响不同。对价格的反应滞后供给的变化和住房市场投机，沃特malpezzi提出了生成的房地产周期简单的模型。他们发现，在住房市场需求状况和投机，房地产周期发挥了重要作用。此外，他们表现出的供给价格弹性是投机的主要组成部分。观察到投机的最大影响是供给缺乏弹性。三组不同的数据用于研究：1）美国五十个州同步指标; 2）对美国50个州和全国住房价格指数， 3）国家商业周期循环的日期。以下是我们为描述这个研究所使用数据集。美国五十个州每月的一致指数是由联邦储备银行从1979:iq . 2007:iiiq。该数据集是由科龙发展12估计的每四个潜伏状态的动态因素。这四个变量有：非农业就业总数的机构，制