Is the Permanent Income Hypothesis Really Well-Suited for Forecasting?

This paper first tests the restrictions implied by Hall's [1978] version of the permanent income hypothesis (PIH) obtained from a bivariate system of labor income and savings, using quarterly data over the period of 1947:01–2008:03 for the US economy, and then uses the model to forecast changes in labor income over the period of 1991:01–2008:03, using 1947:01–1990:04 as the in-sample. First, our results indicate the overwhelming rejection of the restrictions on the data implied by the PIH. Second, we found that, when compared to univariate and bivariate versions of classical and Bayesian Vector Autoregressive (BVAR) models, the PIH model, in general, is outperformed by all other models in terms of the average Root Mean Squared Errors for one- to eight-quarters-ahead forecasts for the changes in labor income. Finally, as far as forecasting is concerned, we found the most tight Gibbs-sampled univarite BVAR to perform the best. In sum, we do not find evidence for the US data to be consistent with the PIH, neither does the PIH model perform better relative to alternative atheoretical models in forecasting changes in labor income over an out-of-sample horizon that was characterized by high degree of volatility for the variable of interest.