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Using Leading Indicators to Forecast U.S. Home Sales in a Bayesian Vector Autoregressive Framework
Authors:Dua  Pami  Miller  Stephen M.  Smyth  David J.
Affiliation:(1) Center for Development Economics, Delhi School of Economics, Delhi, 11007, India;(2) Department of Economics, University of Connecticut, Storrs, CT, 06269-1063;(3) Business School, Middlesex University, London, 4NW 4BT, United Kingdom
Abstract:This article uses Bayesian vector autoregressive models to examine the usefulness of leading indicators in predicting U.S. home sales. The benchmark Bayesian model includes home sales, price of homes, mortgage rate, real personal disposable income, and unemployment rate. We evaluate the forecasting performance of six alternative leading indicators by adding each, in turn, to the benchmark model. Out-of-sample forecast performance over three periods shows that the model that includes building permits authorized consistently produces the most accurate forecasts. Thus, the intention to build in the future provides good information with which to predict U.S. home sales. Another finding suggests that leading indicators with longer leads outperform the short-leading indicators.
Keywords:Bayesian vector autoregressive models  home sales  leading indicators  forecast accuracy
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