Forecasting House Prices in the United States with Multiple Structural Breaks
The boom-bust cycle in U.S. house prices has been a fundamental determinant of the recent financial crisis leading up to the Great Recession. The risky financial innovations in the housing market prior to the recent crisis fueled the speculative housing boom. In this backdrop, the main objectives...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Econometric Research Association
2014-04-01
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| Series: | International Econometric Review |
| Subjects: | |
| Online Access: | http://www.era.org.tr/makaleler/15040090.pdf |
| _version_ | 1797913258889314304 |
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| author | Mahua Barari Nityananda Sarkar Srikanta Kundu Kushal Banik Chowdhury |
| author_facet | Mahua Barari Nityananda Sarkar Srikanta Kundu Kushal Banik Chowdhury |
| author_sort | Mahua Barari |
| collection | DOAJ |
| description | The boom-bust cycle in U.S. house prices has been a fundamental determinant of the
recent financial crisis leading up to the Great Recession. The risky financial innovations
in the housing market prior to the recent crisis fueled the speculative housing boom. In
this backdrop, the main objectives of this empirical study are to i) detect the possibility of
multiple structural breaks in the US house price data during 1995-2010, exhibiting very
sharp upturns and downturns; ii) endogenously determine the break points and iii)
conduct house price forecasting exercises to see how models with structural breaks fare
with competing time series models – linear and nonlinear. Using a very general
methodology (Bai-Perron, 1998, 2003), we found four break points in the trend in the
S&P/Case-Shiller 10 city aggregate house-price index series. Next, we compared the
forecasting performance of the model with structural breaks to four competing models –
namely, Random Acceleration (RA), Autoregressive Moving Average (ARMA), SelfExciting Threshold Autoregressive (SETAR), and Smooth Transition Autoregressive
(STAR). Our findings suggest that house price series not only has undergone structural
changes but also regime shifts. Hence, forecasting models that assume constant
coefficients such as ARMA may not accurately capture house price dynamics. |
| first_indexed | 2024-04-10T12:09:06Z |
| format | Article |
| id | doaj.art-6793579b9713449583863ccb8e292c6e |
| institution | Directory Open Access Journal |
| issn | 1308-8793 1308-8815 |
| language | English |
| last_indexed | 2024-04-10T12:09:06Z |
| publishDate | 2014-04-01 |
| publisher | Econometric Research Association |
| record_format | Article |
| series | International Econometric Review |
| spelling | doaj.art-6793579b9713449583863ccb8e292c6e2023-02-15T16:16:05ZengEconometric Research AssociationInternational Econometric Review1308-87931308-88152014-04-0161123Forecasting House Prices in the United States with Multiple Structural BreaksMahua Barari0Nityananda Sarkar1Srikanta Kundu2Kushal Banik Chowdhury3Missouri State UniversityIndian Statistical InstituteIndian Statistical InstituteIndian Statistical InstituteThe boom-bust cycle in U.S. house prices has been a fundamental determinant of the recent financial crisis leading up to the Great Recession. The risky financial innovations in the housing market prior to the recent crisis fueled the speculative housing boom. In this backdrop, the main objectives of this empirical study are to i) detect the possibility of multiple structural breaks in the US house price data during 1995-2010, exhibiting very sharp upturns and downturns; ii) endogenously determine the break points and iii) conduct house price forecasting exercises to see how models with structural breaks fare with competing time series models – linear and nonlinear. Using a very general methodology (Bai-Perron, 1998, 2003), we found four break points in the trend in the S&P/Case-Shiller 10 city aggregate house-price index series. Next, we compared the forecasting performance of the model with structural breaks to four competing models – namely, Random Acceleration (RA), Autoregressive Moving Average (ARMA), SelfExciting Threshold Autoregressive (SETAR), and Smooth Transition Autoregressive (STAR). Our findings suggest that house price series not only has undergone structural changes but also regime shifts. Hence, forecasting models that assume constant coefficients such as ARMA may not accurately capture house price dynamics.http://www.era.org.tr/makaleler/15040090.pdfStructural BreakHouse PricesForecastingNon-linear ModelsNonstationarity |
| spellingShingle | Mahua Barari Nityananda Sarkar Srikanta Kundu Kushal Banik Chowdhury Forecasting House Prices in the United States with Multiple Structural Breaks International Econometric Review Structural Break House Prices Forecasting Non-linear Models Nonstationarity |
| title | Forecasting House Prices in the United States with Multiple Structural Breaks |
| title_full | Forecasting House Prices in the United States with Multiple Structural Breaks |
| title_fullStr | Forecasting House Prices in the United States with Multiple Structural Breaks |
| title_full_unstemmed | Forecasting House Prices in the United States with Multiple Structural Breaks |
| title_short | Forecasting House Prices in the United States with Multiple Structural Breaks |
| title_sort | forecasting house prices in the united states with multiple structural breaks |
| topic | Structural Break House Prices Forecasting Non-linear Models Nonstationarity |
| url | http://www.era.org.tr/makaleler/15040090.pdf |
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