區(qū)域經(jīng)濟(jì)周期和房地產(chǎn)周期分析【外文翻譯】(存儲(chǔ)版)
【正文】 的數(shù)據(jù)，每季度由美國(guó) OFHEO 的提供。這四個(gè)變量有：非農(nóng)業(yè)就業(yè)總數(shù)的機(jī)構(gòu)，制造業(yè)平均每周工時(shí)，失業(yè)率，實(shí)際工資和薪金支出。他們發(fā)現(xiàn)，在住房市場(chǎng)需求狀況和投機(jī) ， 房地產(chǎn)周期發(fā)揮 了 重要作用 。 薩切爾和達(dá)科 [24]發(fā)現(xiàn)，狀態(tài)轉(zhuǎn)換模型給出了更準(zhǔn)確的房地產(chǎn)市場(chǎng)不是簡(jiǎn)單的線性模型的表現(xiàn)情況。漢 密爾頓的估計(jì)日期恰逢由國(guó)家經(jīng)濟(jì)研究局證實(shí)了馬可夫轉(zhuǎn)換估計(jì)技術(shù)的衡量商業(yè)周期的轉(zhuǎn)折點(diǎn)所提供的日期 的有效性 。因此，這項(xiàng)研究有三個(gè)主要目標(biāo)。房地產(chǎn)市場(chǎng)的證券化是一個(gè)重要的趨勢(shì)，吸引了許多投資者進(jìn)入這個(gè)領(lǐng)域。本科畢業(yè)設(shè)計(jì)（論文） 外 文 翻 譯 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 micro and macro economics, but also in the field of finance and investment. Recently real estate became a lucrative investment option for investors (Leonhardt [23]。最近房地產(chǎn)成為投資者有利可圖的投資選擇。本文的重點(diǎn)，嚴(yán)格科學(xué)的房地產(chǎn)宏觀的角度，分析了房地產(chǎn)周期的模式。漢密爾頓 [20]用美國(guó) GDP 數(shù)據(jù)估計(jì)狀態(tài)空間馬爾可夫轉(zhuǎn)換技術(shù)，估計(jì)商業(yè)周期的轉(zhuǎn)折點(diǎn)。探索一門(mén)限自回歸（ TAR）的模型 。 對(duì)價(jià)格的反應(yīng)滯后供給的變化和住房市場(chǎng)投機(jī) ， 沃特 Malpezzi 提出了生成的房地產(chǎn)周期簡(jiǎn)單的模型。該數(shù)據(jù)集是由科龍發(fā)展 [12]估計(jì) 的 每四個(gè)潛伏狀態(tài)的動(dòng)態(tài)因素。 HPI 的涵蓋更多的交易和相對(duì)于其他兩個(gè)數(shù)據(jù)集的地理 區(qū)域。那些規(guī)模 以上的可能性，我們稱為國(guó)家的經(jīng)濟(jì)衰退。在許多情況下，房地產(chǎn)波動(dòng)可能在一些國(guó)家經(jīng)濟(jì)衰退的重要作用?？偣灿辛鶄€(gè)州 阿拉巴馬州，特拉華州，馬里蘭州，新墨西哥州，得克薩斯州和華盛頓 這個(gè) 地區(qū) 屬于此類。經(jīng)濟(jì)和房地產(chǎn)的波動(dòng)，房地產(chǎn)周期與遵循的四個(gè)國(guó)家級(jí)經(jīng)濟(jì)周期分別滯后四分之二十。第三集團(tuán)是一家領(lǐng)先和落后狀態(tài)的混合物。但是，迄今只有 19 個(gè)州 使 國(guó)家級(jí)經(jīng)濟(jì)進(jìn)入衰退的概率 較 高，但沒(méi)有國(guó)家經(jīng)濟(jì)衰退的跡象。根據(jù)圖 ，美國(guó)在 1979年經(jīng)歷了兩個(gè)主要的房地產(chǎn)衰退： 1979:IQ 到 2020:IQ。我們比較經(jīng)濟(jì)周期階段（例如，經(jīng)濟(jì)衰退和擴(kuò)張）。普惠制數(shù)據(jù)是在每年的基礎(chǔ) 上 ，但國(guó)家級(jí)經(jīng)濟(jì)衰退或擴(kuò)張可以在一年之內(nèi)開(kāi)始和結(jié)束。三組不同的數(shù)據(jù)用于研究： 1）美國(guó)五十個(gè)州同步指標(biāo) 。 英國(guó)房地產(chǎn)，從 1975 年 1 月至 1995 年 8 月 的 月度數(shù)據(jù) 對(duì) 國(guó)際英國(guó)房地產(chǎn)價(jià)格指數(shù)的性能進(jìn)行了測(cè)試。 利用 K 這個(gè)人對(duì)美國(guó)國(guó)家數(shù)據(jù)的估算技術(shù)， CRONE 根據(jù)美國(guó)地區(qū)經(jīng)濟(jì)周期的相似性將美國(guó)分成八個(gè)經(jīng)濟(jì)區(qū)。 本文的其余部分組織如下。 許多研究表明，房地產(chǎn) 對(duì) 周期戶，投資者，銀行系統(tǒng)，以及對(duì)國(guó)家經(jīng)濟(jì)有直接影響。 and 3) national business cycle turning dates. Following are the descriptions of the data sets we used for this study. The . fifty states’ monthly coincident indexes are provided by the Federal Reserve Bank of Philadelphia dating from 1979:IQ . 2020: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 and salary disbursements. This is one of the most prehensive 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 . states. GSP data are in 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 singlefamily house prices9, which 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 version of the CaseShiller geometric weighted repeatsales procedure (Calhoun, [4]). The HPI by OFHEO is more accurate and plete measure of housing price change pare to Samp。P/ CaseShiller indexes10 or Constant Quality Housing Price Index (CQHPI)11. The HPI covers more transactions and geographic areas pared to other two data sets. We used quarterly HPI for fifty . states from 1979:IQ
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