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

25 Pages Posted: 11 May 2014 Last revised: 18 Jun 2017

See all articles by Goodness Aye

Goodness Aye

University of Pretoria - Department of Economics

Stephen M. Miller

University of Nevada, Las Vegas - Department of Economics; University of Connecticut - Department of Economics

Rangan Gupta

University of Pretoria - Department of Economics

Mehmet Balcilar

Eastern Mediterranean University

Date Written: May 9, 2014

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.

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

JEL Classification: C32, E22, E27

Suggested Citation

Aye, Goodness and Miller, Stephen M. and Gupta, Rangan and Balcilar, Mehmet, Forecasting US Real Private Residential Fixed Investment Using a Large Number of Predictors (May 9, 2014). Empirical Economics, December 2016, Available at SSRN: https://ssrn.com/abstract=2435299 or http://dx.doi.org/10.2139/ssrn.2435299

Goodness Aye

University of Pretoria - Department of Economics ( email )

Lynnwood Road
Hillcrest
Pretoria, 0002
South Africa

Stephen M. Miller (Contact Author)

University of Nevada, Las Vegas - Department of Economics ( email )

4505 S. Maryland Parkway
Box 456005
Las Vegas, NV 89154
United States
702-895-3776 (Phone)
702-895-1354 (Fax)

HOME PAGE: http://faculty.unlv.edu/smiller/

University of Connecticut - Department of Economics

365 Fairfield Way, U-1063
Storrs, CT 06269-1063
United States

Rangan Gupta

University of Pretoria - Department of Economics ( email )

Lynnwood Road
Hillcrest
Pretoria, 0002
South Africa

Mehmet Balcilar

Eastern Mediterranean University ( email )

Gazimagusa
Turkey

HOME PAGE: http://www.mbalcilar.net

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