房地產景氣特性之再確認－多變量馬可夫轉換之應用

Reconfirmation of the Turning Points of Real Estate Cycle Indicators in the Taiwan Real Estate Market-An Application of Markov-Switching Vector Auto-Regression Models

10.6375/JHS.200906.0023

馬毓駿(Yu-Chun Ma)；林秋瑾(Vickey Chiu-Chin Lin)

房地產景氣 ； 轉折點確認 ； 多變量馬可夫轉換模型 ； real estate cycle ； turning points confirmation ； markov-switching vector auto-regression model mode

住宅學報

18卷1期（2009 / 06 / 01）

23 - 37

繁體中文

探討房地產景氣的變化一向是房地產領域關注的重要議題，精確而有效的模型不僅有助於瞭解房地產與總體經濟變數波動特性的差異，更有利於相關房地產政策制訂時的依據。在研究景氣變化的計量模型中，馬可夫轉換模型是較常被利用來進行此一研究的工具，然現階段的實證結果卻無法完全令人滿意，主要原因是單變數的馬可夫轉換模型無法完全反應房地產景氣變化的特性所致。本文則嘗試採用多變量的馬可夫轉換模型，直接對構成房地產指數的多個內容變數同時進行分析，藉由多變量的架構連結變數間的相互關係來確認景氣轉折與持續期間。由本文實證結果我們將發現，不論是領先指標或基準循環指標下掌握轉折的能力有顯著改善外，更重要的是，對於房地產景氣的擴張期與收縮期的特性捕捉，多變量馬可夫模型更能顯示其優越之處，此一實證結果將可有效提供決策當局制訂房地產政策時的參考。

How to precisely confirm the features of real estate cycles has always been an important issue in the real estate research field. An unerring description of a business cycle outline is not just a helpful tool for the authority that tries to formulate policies in response to future economic change, but it also provides a useful message for people who are currently facing buying/renting decisions. Unfortunately, we are still lacking a good theoretical model that can support us when making such decisions. While a one-variable Markov switching model performs a good prediction in terms of capturing the turning point in a business cycle, it still fails to estimate the cycle's duration, a problem that has not been overcome. This research attempts to employ a multi-structure Markov switching model to revise the duration problem. Based on the confirmation purpose, we compare several settings of Markov switching structures, and adopt new measurements to evaluate each kind of model. Besides, we simultaneously test the performance of leading indicators and reference series. The empirical results show that, when the Markov-switching vector auto-regression model is used, the ability to estimate the cycle's duration really outperforms that of the single series Markov switching model.

1. Cai, J.(1994).A Markov Model of Switching-Regime ARCH.Journal of Business & Economic Statistics,12,309-316.
2. Diebold, F. X.,G. D. Rudebusch(1996).Measuring Business Cycle: a Modern Perspective.Review of Economics and Statistics,78(1),67-77.
3. Diebold, F. X.,G. D. Rudebusch(1999).Business Cycles Duration, Dynamics, and Forecasting.New Jersey:Princeton University Press.
4. Diebold, F. X.,R. Mariano(1995).Comparing Predictive Accuracy.Journal of Business and Economic Statistics,13,253-263.
5. Estrella, A.,F. S. Mishkin.(1998).Predicting US Recession: Financial Variables as Leading Indicators.Review of Economics and Statistics,80,45-61.
6. Gray, S. F.(1996).Modeling the Conditional Distribution of Interest Rates as a Regime Switching Process.Journal of Financial Economics,42,27-62.
7. Hamilton, J. D.(1994).Time Series Analysis.New Jersey:Princeton University Press.
8. Hamilton, J. D.(1989).A New Approach to the Economic Analysis of Nonstationary Time Series and the Business cycle.Econometrica.,57,357-384.
9. Hamilton, J. D.,R. Susmel(1994).Autoregressive Conditional Heteroscedasticity and Changes in Regime.Journal of Econometrics,64,307-333.
10. Krolzig, H. M.(1997).Markov Switching Vector Autogressions: Modelling, Statistical Inference and Application to Business Cycle Analysis.Heidelberg:Springer Berlin.
11. Toda, H. Y.,T. Yamamoto(1995).Statistical Inference in Vector Autoregressions with Possibly Integrated Processes.Journal of Econometrics,66,225-250.
12. 周幸蓉、李春長(2007)。台灣房地產景氣循環轉折點之認定。2007年全國不動產經營與管理實務學術研討會
13. 林秋瑾(2004)。房地產景氣指標衡量之再確認－馬可夫轉換狀態空間模型之應用。2004年海峽兩岸土地學術研討會，會議主題：現代地政與土地管理資訊化，台北:
14. 徐之強、黃裕烈(2005)。行政院經濟建設委員會研究報告，編號：(94)040.210行政院經濟建設委員會研究報告，編號：(94)040.210,未出版

1. 王柏元(2021)。Does Taiwan Need a Macro-Prudential Policy? How to Do It? How to Coordinate with Monetary Policy?。經濟論文，49(1)，1-40。
2. 游士儀、林祖嘉(2018)。總體經濟對房地產景氣循環不對稱影響之研究－中國大陸之實證分析。住宅學報，27(1)，23-46。
3. 游舜德、林詩榕(2011)。台灣上市櫃建設公司股票報酬率影響因素之研究。臺灣土地研究，14(2)，1-38。
4. 鄭漢亮、陳南光(2012)。外部融資溢酬、台灣房屋市場與景氣波動。經濟論文，40(3)，307-341。