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Estimating Vector Autoregressions with Panel Data

Douglas Holtz-Eakin
Whitney Newey
Harvey S. Rosen

Abstract

This paper considers estimation and testing of vector autoregression coefficients in panel data, and applies the techniques to analyze the dynamic relationships between wages and hours worked in two samples of American males. The model allows for nonstationary individual effects, and is estimated by applying instrumental variables to the quasi-differenced autoregressive equations. Particular attention is paid to specifying lag lengths, forming convenient test statistics, and testing for the presence of measurement error. The empirical results suggest the absence of lagged hours in the wage of forecasting equation. Our results also show that lagged hours is important in the hours equation, which is consistent with alternatives to the simple labor supply model that allow for costly hours adjustment or preferences that are not time separable.