區(qū)域經(jīng)濟(jì)周期和房地產(chǎn)周期分析【外文翻譯】-全文預(yù)覽
【正文】 description, fifth we state the results by presenting parison of business cycles and real estate cycles, thus categorize states depending on the formation of real estate cycles. To give some idea how the . 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 cycles are calculated and dated by the National Bureau of Economic Research (NBER). Hamilton [20] used state space Markov Switching estimation technique on the . GDP data to estimate business cycle turning points. Hamilton’s 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] pared with different business cycles turning point dating methods in the . 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
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