【正文】 economy. He concluded that the Stock and Watson’s [20], [20] experimental business cycles indicators based on Kalman Filter algorithm and Hamilton’s Markov Switching [20] estimation technique outperforms all other business cycles dating methods. Crone [12], [13] used Kalman Filter estimation technique on the . state level data and grouped . into eight economic regions based on regional business cycles similarities. Using Hamilton’s 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 . state level business cycles. Both studies show that the . state level business cycles do not necessarily coincide with the national business cycles. A recent study by Crone [14] also estimates the . 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 autoregressive (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 pare the behavior of the . and the real estate market. To measure the . real estate market performance the authors used monthly data of the Real Estate Investment Trust (REIT) from December 1972 to March 1995. The . real estate performance was measured by the monthly data of International . property Price index from January 1975 to August 1995. They found distinct real estate regimes in the . and in the . Thus they concluded that the real interest rate plays a significant role as an indicator of real estate performance in both countries, ., 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 moary authorities has differential effect on regions throughout the United State. The regions specialized in construction, housing, or real estate based industries get affected differently pared 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 major role in housing market and real estate cycles. Further, they showed that the price elasticity of supply is the dominant ponent of speculation. The largest effects of speculation were observed when supply is inelastic. Three different data sets are used in this study: 1) the . fifty states coincident indexes。三，未來研究人員將 對 各國的經(jīng)濟結(jié)構(gòu) 產(chǎn)生 生動的了解和較好的房地產(chǎn)周期行為的理解。他的研究結(jié)論是擴散指數(shù)數(shù)據(jù)集，以更好地跟蹤預測區(qū)域或商業(yè)周期的轉(zhuǎn)折點。 美國五十個州每月的一致指數(shù)是由聯(lián)邦儲備銀行從 1979:IQ . 2020:IIIQ。因此，如果在 概率的周期，我們稱 為 規(guī)模擴張狀態(tài) 。然而，循環(huán)模式不是為 了 解釋變量，同類房地產(chǎn)的背后，除了房地產(chǎn)以外的充分條件，更適合國家級經(jīng)濟周期的形成，這可能最終影響到這些國家房地產(chǎn)領(lǐng)域的投資。這組狀態(tài)有時面臨著領(lǐng)先的房地產(chǎn)周期，有時甚至面臨著落后的房地產(chǎn)周期相比，這些國家的商業(yè)周期。一開始在 1981 年 IIQ和 1985 年結(jié)束： IQ,，而第二個在 1989 年開始： IVQ 并持續(xù)到 1999 年： IIIQ。 在這項研究中所使用的房屋價格指數(shù)（ HPI）數(shù)據(jù)是由聯(lián)邦住房企業(yè)監(jiān)督（ OFHEO 的） 8 辦公室公布 的 。他們發(fā)現(xiàn)美國和英國的房地 產(chǎn)制度 的不同，因此，他們得出結(jié)論，實際