Refine
Document Type
- Working Paper (14)
- Report (2)
- Doctoral Thesis (1)
Language
- English (17)
Has Fulltext
- yes (17)
Is part of the Bibliography
- no (17)
Keywords
- DSGE models (5)
- forecasting (5)
- model uncertainty (4)
- monetary policy (4)
- real-time data (4)
- Greenbook (3)
- density forecasts (3)
- forecast combination (3)
- Geldpolitik (2)
- Model Uncertainty (2)
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.
This paper investigates the accuracy of point and density forecasts of four DSGE models for inflation, output growth and the federal funds rate. Model parameters are estimated and forecasts are derived successively from historical U.S. data vintages synchronized with the Fed’s Greenbook projections. Point forecasts of some models are of similar accuracy as the forecasts of nonstructural large dataset methods. Despite their common underlying New Keynesian modeling philosophy, forecasts of different DSGE models turn out to be quite distinct. Weighted forecasts are more precise than forecasts from individual models. The accuracy of a simple average of DSGE model forecasts is comparable to Greenbook projections for medium term horizons. Comparing density forecasts of DSGE models with the actual distribution of observations shows that the models overestimate uncertainty around point forecasts.
The paper illustrates based on an example the importance of consistency between the empirical measurement and the concept of variables in estimated macroeconomic models. Since standard New Keynesian models do not account for demographic trends and sectoral shifts, the authors proposes adjusting hours worked per capita used to estimate such models accordingly to enhance the consistency between the data and the model. Without this adjustment, low frequency shifts in hours lead to unreasonable trends in the output gap, caused by the close link between hours and the output gap in such models.
The retirement wave of baby boomers, for example, lowers U.S. aggregate hours per capita, which leads to erroneous permanently negative output gap estimates following the Great Recession. After correcting hours for changes in the age composition, the estimated output gap closes gradually instead following the years after the Great Recession.
This paper investigates the accuracy of point and density forecasts of four DSGE models for inflation, output growth and the federal funds rate. Model parameters are estimated and forecasts are derived successively from historical U.S. data vintages synchronized with the Fed’s Greenbook projections. Point forecasts of some models are of similar accuracy as the forecasts of nonstructural large dataset methods. Despite their common underlying New Keynesian modeling philosophy, forecasts of different DSGE models turn out to be quite distinct. Weighted forecasts are more precise than forecasts from individual models. The accuracy of a simple average of DSGE model forecasts is comparable to Greenbook projections for medium term horizons. Comparing density forecasts of DSGE models with the actual distribution of observations shows that the models overestimate uncertainty around point forecasts.
This paper investigates the accuracy of forecasts from four DSGE models for inflation, output growth and the federal funds rate using a real-time dataset synchronized with the Fed’s Greenbook projections. Conditioning the model forecasts on the Greenbook nowcasts leads to forecasts that are as accurate as the Greenbook projections for output growth and the federal funds rate. Only for inflation the model forecasts are dominated by the Greenbook projections. A comparison with forecasts from Bayesian VARs shows that the economic structure of the DSGE models which is useful for the interpretation of forecasts does not lower the accuracy of forecasts. Combining forecasts of several DSGE models increases precision in comparison to individual model forecasts. Comparing density forecasts with the actual distribution of observations shows that DSGE models overestimate uncertainty around point forecasts.
This paper investigates the accuracy and heterogeneity of output growth and inflation forecasts during the current and the four preceding NBER-dated U.S. recessions. We generate forecasts from six different models of the U.S. economy and compare them to professional forecasts from the Federal Reserve’s Greenbook and the Survey of Professional Forecasters (SPF). The model parameters and model forecasts are derived from historical data vintages so as to ensure comparability to historical forecasts by professionals. The mean model forecast comes surprisingly close to the mean SPF and Greenbook forecasts in terms of accuracy even though the models only make use of a small number of data series. Model forecasts compare particularly well to professional forecasts at a horizon of three to four quarters and during recoveries. The extent of forecast heterogeneity is similar for model and professional forecasts but varies substantially over time. Thus, forecast heterogeneity constitutes a potentially important source of economic fluctuations. While the particular reasons for diversity in professional forecasts are not observable, the diversity in model forecasts can be traced to different modeling assumptions, information sets and parameter estimates. JEL Classification: C53, D84, E31, E32, E37 Keywords: Forecasting, Business Cycles, Heterogeneous Beliefs, Forecast Distribution, Model Uncertainty, Bayesian Estimation
In the aftermath of the global financial crisis, the state of macroeconomicmodeling 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
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.
We examine both the degree and the structural stability of inflation persis tence at different quantiles of the conditional inflation distribution. Previous research focused exclusively on persistence at the conditional mean of the inflation rate. Economic theory, however, provides various reasons -for example downward wage rigidities or menu costs- to expect higher inflation persistence at the upper than at the lower tail of the conditional inflation distribution.
Based on post-war US data we indeed find slower mean reversion in response to positive than to negative shocks. We find robust evidence for a structural break in persistence at all quantiles of the inflation process in the early 1980s. Inflation persistence has decreased and become more homogeneous across quantiles. Persistence at the conditional mean became more informative about the degree of persistence across the entire conditional inflation distribution. While prior to the 1980s inflation was not mean reverting in response to large positive shocks, our evidence strongly suggests that since the end of the Volcker disinflation the unit root can be rejected at every quantile including the upper tail of the conditional inflation distribution.