The time-varying-parameter model for modeling changing conditional variance: The case of the lucas hypothesis

The main econometric issue in testing the Lucas (1973) hypothesis in a time series context is estimation of the forecast-error variance conditional on past information. The conditional variance may vary through time as monetary policy evolves and agents are obliged to infer its present state. Under the assumption that a monetary policy regime is continuously changing, a time- varying-parameter model is proposed for the monetary-growth function. Based on Kalman- filtering estimation of recursive forecast errors and their conditional variances, the Lucas hypothesis is tested for the U.S. economy (1964:1-1985:4) using monetary growth as aggregate demand variable. The Lucas hypothesis is rejected in favor of Friedman’s (1977) hypothesis— the conditional variance of monetary growth affects real output directly, not through the coefficients on the forecast-error term in the Lucas-type output equation.