Estimating linear rational expectations models requires replacing the expectations of future, endogenous variables either with forecasts from a fully solved model, or with the instrumented actual values, or with forecast survey data. Extending the methods of McCallum (1976) and Gottfries and Persson (1988), I show how to pool these methods and also use actual, future values of these variables to improve statistical efficiency. The method is illustrated with an application using SPF survey data in the US Phillips curve, where the output gap plays a significant role but lagged inflation plays none.
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Paper provided by Queen's University, Department of Economics in its series Working Papers with number
1129.
Find related papers by JEL classification: E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
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