Forecasts are routinely revised, and these revisions are often the subject of informal analysis and discussion. This paper argues 1) that forecast revisions are analyzed because they help forecasters and forecast users to evaluate forecasts and forecasting procedures, and 2) that these analyses can be sharpened by using the forecasting model to systematically express its forecast revision as the sum of components identified with specific data revisions and forecast errors. An algorithm for this purpose is explained and illustrated.

This paper puts forward a method for simulating an existing macroeconometric model while maintaining the additional assumption that individuals form their expectations rationally. This simulation technique is a first response to Lucas' criticism that standard econometric policy evaluation allows policy rules to change but doesn't allow expectations rules to change as economic theory predicts they will. The technique is applied to a version of the St. Louis Federal Reserve Model with interesting results. The rational expectations version of the St. Louis Model exhibits the same neutrality with respect to certain policy rules as small, analytic rational expectations models considered by Lucas, Sargent, and Wallace.

This paper puts forward a method for improving the forecasting accuracy of an existing macroeconometric model without changing its policy response characteristics. The procedure is an extension and formalization of the practice of additive adjustments currently used by most forecasters. The method should be of special interest to forecasters who use models built by other investigators because it does not involve reestimation of the original model and uses only information routinely included in the documentation available to model users. The paper ends with a demonstration of the prediction improvement realized by application of this method to a version of the MIT-Penn-SSRC (MPS) model.

Forecasts are routinely revised, and these revisions are often the subject of informal analysis and discussion. This paper argues (1) that forecast revisions are analyzed because they help forecasters and forecast users to evaluate forecasts and forecasting procedures, and (2) that these analyses can be sharpened by using the forecasting model to systematically express its forecast revision as the sum of components identified with specific subsets of new information, such as data revisions and forecast errors. An algorithm for this purpose is explained and illustrated.

The method proposed here includes two innovations which should improve the accuracy of econometric forecasting. First, it replaces the subjective, judgmental adjustments commonly used with a more formal, objective econometric procedure. Second, it includes a methodology for testing the usefulness of subperiod data which forecasters often inspect when choosing intercept adjustments. A sample application to the MIT-Penn-SSRC Model demonstrates that the procedure is both feasible and potentially helpful in the context of a large macroeconometric model.

This paper puts forward a method of policy simulation with an existing macroeconometric model under the maintained assumption that individuals form their expectations rationally. This new simulation technique grows out of Lucas’ criticism that standard econometric policy evaluation permits policy rules to change but doesn’t allow expectations mechanisms to respond as economic theory predicts they will. The technique is applied to versions of the St. Louis Federal Reserve model and the Federal Reserve-MIT-Penn (FMP) model to simulate the effects of different constant money growth policies. The results of these simulations indicate that the problem identified by Lucas may be of great quantitative importance in the econometric analysis of policy alternatives.

No abstract available.

Doan, Litterman, and Sims (DLS) have suggested using conditional forecasts to do policy analysis with Bayesian vector autoregression (BVAR) models. Their method seems to violate the Lucas critique, which implies that coefficients of a BVAR model will change when there is a change in policy rules. In this paper we construct a BVAR macro model and attempt to determine whether the Lucas critique is important quantitatively. We find evidence following two candidate policy rule changes of significant coefficient instability and of a deterioration in the performance of the DLS method.