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Forecasting US Real Private Residential Fixed Investment Using a Large Number of Predictors

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  • Goodness C. Aye

    (University of Pretoria)

  • Rangan Gupta

    (University of Pretoria)

  • Stephen M. Miller

    (University of Nevada, Las Vegas and University of Connecticut)

  • Mehmet Balcilar

    (Eastern Mediterranean University)

Abstract

This paper employs classical bivariate, factor augmented (FA), slab-and-spike variable selection (SSVS)-based, and Bayesian semi-parametric shrinkage (BSS)-based predictive regression models to forecast US real private residential fixed investment over an out-of-sample period from 1983:Q1 to 2011:Q2, based on an in-sample estimates for 1963:Q1 to 1982:Q4. Both large-scale (188 macroeconomic series) and small-scale (20 macroeconomic series) FA, SSVS, and BSS predictive regressions, as well as 20 bivariate regression models, capture the influence of fundamentals in forecasting residential investment. We evaluate the ex-post out-of-sample forecast performance of the 26 models using the relative average Mean Square Error for one-, two-, four-, and eight-quarters-ahead forecasts and test their significance based on the McCracken (2004, 2007) MSE-F statistic. We find that, on average, the SSVS-Large model provides the best forecasts amongst all the models. We also find that one of the individual regression models, using house for sale (H4SALE) as a predictor, performs best at the four- and eight-quarters-ahead horizons. Finally, we use these two models to predict the relevant turning points of the residential investment, via an ex-ante forecast exercise from 2011:Q3 to 2012:Q4. The SSVS-Large model forecasts the turning points more accurately, although the H4SALE model does better toward the end of the sample. Our results suggest that economy-wide factors, in addition to specific housing market variables, prove important when forecasting in the real estate market.

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Bibliographic Info

Paper provided by University of Connecticut, Department of Economics in its series Working papers with number 2014-10.

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Length: 26 pages
Date of creation: May 2014
Date of revision:
Handle: RePEc:uct:uconnp:2014-10

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Keywords: Private residential investment; predictive regressions; factor-augmented models; Bayesian shrinkage; forecasting;

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References

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Citations

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Cited by:
  1. Beatrice D. Simo-Kengne & Stephen M. Miller & Rangan Gupta, 2014. "Evolution of Monetary Policy in the US: The Role of Asset Prices," Working Papers 2014-459, Department of Research, Ipag Business School.
  2. Vasilios Plakandaras & Rangan Gupta & Periklis Gogas & Theophilos Papadimitriou, 2014. "Forecasting the U.S. Real House Price Index," Working Papers 2014-473, Department of Research, Ipag Business School.
  3. Goodness C. Aye & Stephen M. Miller & Rangan Gupta & Mehmet Balcilar, 2014. "Forecasting US Real Private Residential Fixed Investment Using a Large Number of Predictors," Working Papers 2014-465, Department of Research, Ipag Business School.
  4. Mohamed Arouri & Shawkat Hammoudeh & Fredj Jawadi & Duc Khuong Nguyen, 2014. "Financial Linkages between U.S. Sector Credit Default Swaps Markets," Working Papers 2014-553, Department of Research, Ipag Business School.
  5. Wendy Nyakabawo & Stephen M. Miller & Mehmet Balcilar & Sonali Das & Rangan Gupta, 2013. "Temporal Causality between House Prices and Output in the U. S.: A Bootstrap Rolling-Window Approach," Working Papers 201329, University of Pretoria, Department of Economics.
  6. Furkan Emirmahmutoglu & Nicholas Apergis & Beatrice D. Simo-Kengne & Tsangyao Chang & Rangan Gupta, 2014. "Causal relationship between asset prices and output in the US: Evidence from state-level panel Granger causality test," Working Papers 201411, University of Pretoria, Department of Economics.

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