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This contribution draws on two recent publications in which the macroeconomic model data base (www.macromodelbase.com) is employed for model comparisons. The comparative approach is used to base policy analysis on a systematic evaluation of the different implications that a certain economic policy can have when submitted to different modeling approaches. In this manner, policy recommendations are more robust to modeling uncertainty. By extending the comparative approach to forecasting, the authors investigate the accuracy of different forecasting models and obtain more reliable mean forecasts.
This dissertation introduces in chapter 1 a new comparative approach to model-based research and policy analysis by constructing an archive of business cycle models. It includes many well-known models used in academia and at policy institutions. A computational platform is created that allows straightforward comparisons of models’ implications for monetary and fiscal stabilization policies. Chapter 2 applies business cycle models to forecasting. Several New Keynesian models are estimated on historical U.S. data vintages and forecasts are computed for the five most recent recessions. The extent of forecast heterogeneity for models and professional forecasts is analysed. Chapter 3 extends the forecasting analysis to a long sample and to the evaluation of density forecasts. Weighted forecasts are computed using a variety of weighting schemes. The accuracy of forecasts is evaluated and compared to professional forecasts and forecasts from nonstructural time series methods. Chapter 4 adds a new feature to existing business cycle models. Specifically, a medium-scale New Keynesian model is constructed that allows for strategic complementarities in price-setting. The role of trade integration for monetary policy transmission is explored. A new dimension of the exchange rate channel is highlighted by which monetary policy directly impacts domestic inflation. Chapter 5 tests whether simple symmetric monetary policy rules used in most business cycle models are a sufficient description of reality. I use quantile regressions to estimate policy parameters and find asymmetric reactions to inflation, the output gap and past interest rates.
In the aftermath of the global financial crisis, the state of macroeconomic modeling and the use of macroeconomic models in policy analysis has come under heavy criticism. Macroeconomists in academia and policy institutions have been blamed for relying too much on a particular class of macroeconomic models. This paper proposes a comparative approach to macroeconomic policy analysis that is open to competing modeling paradigms. Macroeconomic model comparison projects have helped produce some very influential insights such as the Taylor rule. However, they have been infrequent and costly, because they require the input of many teams of researchers and multiple meetings to obtain a limited set of comparative findings. This paper provides a new approach that enables individual researchers to conduct model comparisons easily, frequently, at low cost and on a large scale. Using this approach a model archive is built that includes many well-known empirically estimated models that may be used for quantitative analysis of monetary and fiscal stabilization policies. A computational platform is created that allows straightforward comparisons of models’ implications. Its application is illustrated by comparing different monetary and fiscal policies across selected models. Researchers can easily include new models in the data base and compare the effects of novel extensions to established benchmarks thereby fostering a comparative instead of insular approach to model development.