Point and Density Forecasts for the Euro Area Using Bayesian VARs

Berg, Thomas; Henzel, Steffen

doi:10.2139/ssrn.2425533

SSRN Journal

2014

DOI: 10.2139/ssrn.2425533

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Point and Density Forecasts for the Euro Area Using Bayesian VARs

Thomas Berg

Steffen Henzel

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Cited by 3 publications

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Evaluating Point and Density Forecasts of DSGE Models

Wolters

2015

J. Appl. Econ.

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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.

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Evaluating Point and Density Forecasts of DSGE Models

Wolters

2015

J. Appl. Econ.

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Inflation uncertainty revisited: a proposal for robust measurement

2014

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractAny measure of unobserved inflation uncertainty relies on specific assumptions which are most likely not fulfilled completely. This calls into question whether an individual measure delivers a reliable signal. To reduce idiosyncratic measurement error, we propose using common information contained in different measures derived from survey data, a variety of forecast models, and volatility models. We show that all measures are driven by a common component which constitutes an indicator for inflation uncertainty. Moreover, the idiosyncratic component of survey disagreement contains systematic measurement error during economic downturns. Finally, we study the Friedman-Ball hypothesis. Using the indicator, it turns out that higher inflation is followed by higher uncertainty. By contrast, we obtain contradictory results for the individual measures. We also document that, after an inflationary shock, uncertainty decreases in the first two months which is traceable to the energy component in CPI inflation.JEL Code: C53, E31, E37.

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Forecasting Euro Area Inflation Using Single-Equation and Multivariate VAR–Models

Gerdesmeier

Roffia

Reimers

2017

Folia Oeconomica Stetinensia

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Forecasting inflation is of key relevance for central banks, not least because the objective of low and stable inflation is embodied in most central banks’ mandates and the monetary policy transmission mechanism is well known to be subject to long and variable lags. To our best knowledge, central banks around the world use conditional as well as unconditional forecasts for such purposes. Turning to unconditional forecasts, these can be derived on the basis of structural and non-structural models. Among the latter, vector autoregressive (VAR)-models are among the most popular tools.This study aims at assessing and deriving a set of unconditional forecasts for euro area inflation based on several specifications which take into account the information content of, inter alia, monetary and credit variables. The models are ordered and based on their in-sample performance and the “best” model is selected accordingly. The results indicate that the inclusion of money and credit variables in the information set improves the quality of the forecasts over a horizon of one to eight quarters. This supports the view that central banks should regularly monitor developments in money and credit.

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Point and Density Forecasts for the Euro Area Using Bayesian VARs

Cited by 3 publications

References 30 publications

Evaluating Point and Density Forecasts of DSGE Models

Evaluating Point and Density Forecasts of DSGE Models

Inflation uncertainty revisited: a proposal for robust measurement

Forecasting Euro Area Inflation Using Single-Equation and Multivariate VAR–Models

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