Forecasting euro area variables with German pre‐EMU data

Brüggemann, Ralf; Lütkepohl, Helmut; Marcellino, Massimiliano

doi:10.1002/for.1064

Cited by 15 publications

(16 citation statements)

References 26 publications

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“…These results are similar to those found by Maurel (1998), IMF (2000) or Karmand and Weimann (2004). Furthermore, in a recent paper, Brüggemann et al (2008) suggest that it may be reasonable to consider the German pre-EMU data for studying economic problems in the euro area, because data for the aggregate euro area may pose some problems. Similarly, our results also suggest that German data are preferable when analysing Economic and Monetary Union (EMU) accession countries.…”

Section: Resultsmentioning

confidence: 99%

Unemployment Convergence in Central and Eastern European Countries: Driving Forces and Cluster Behavior

Cuestas

Monfort

Ordóñez

2015

Emerging Markets Finance and Trade

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Employing a nonlinear logistic smooth transition autoregression system and co-movement analysis, we find that the German business cycle has acted as a common driver affecting the cyclical behaviour of unemployment rates in Central and Eastern European countries. In addition, we identify two convergence clubs in unemployment dynamics. The first comprises the Baltic States, Hungary and Poland, and the second group of countries is composed of the Czech Republic and Slovakia. Interestingly, this classification matches the labour market policies and institutional divergences observed among these countries.JEL classification: C22, E32, F15.

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Section: Resultsmentioning

confidence: 99%

Unemployment Convergence in Central and Eastern European Countries: Driving Forces and Cluster Behavior

Cuestas

Monfort

Ordóñez

2015

Emerging Markets Finance and Trade

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“…The variable forecasts for the small and medium scale models are estimated for the out-of-sample testing period 1997:Q1 -2010:Q4. The forecasting investigation for the quarterly US data is performed over the one-, two-, three-and four-quarterahead horizon with a rolling estimation sample, based on the works of Marcellino (2004) and Brüggemann et al (2008) for datasets of quarterly frequency. In particular, the models are re-estimated each quarter over the forecast horizon to update the estimate of the coe¢ cients, before producing the quarter-ahead forecasts.…”

Section: Resultsmentioning

confidence: 99%

Bayesian forecasting with small and medium scale factor-augmented vector autoregressive DSGE models

Bekiros

Paccagnini

2014

Computational Statistics & Data Analysis

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Publication information Computational Statistics and Data Analysis, 71 : 298-323Publisher Elsevier Item record/more information http://hdl.handle.net/10197/7322 Publisher's statementThis is the author's version of a work that was accepted for publication in Computational Statistics and Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computational Statistics and Data Analysis (VOL 71, ISSUE March 2014, (2014 AbstractAdvanced Bayesian methods are employed in estimating dynamic stochastic general equilibrium (DSGE) models. Although policymakers and practitioners are particularly interested in DSGE models, these are typically too stylized to be taken directly to the data and often yield weak prediction results. Hybrid models can deal with some of the DSGE model misspeci…cations. Major advances in Bayesian estimation methodology could allow these models to outperform well-known time series models and e¤ectively deal with more complex real-world problems as richer sources of data become available. A comparative evaluation of the out-of-sample predictive performance of many di¤erent speci…cations of estimated DSGE models and various classes of VAR models is performed, using datasets from the US economy. Simple and hybrid DSGE models are implemented, such as DSGE-VAR and Factor Augmented DSGEs and tested against standard, Bayesian and Factor Augmented VARs. Moreover, small scale models including the real gross domestic product, the harmonized consumer price index and the nominal short-term federal funds interest rate, are comparatively assessed against medium scale models featuring additionally sticky nominal prices, wage contracts, habit formation, variable capital utilization and investment adjustment costs. The investigated period spans 1960:Q4 to 2010:Q4 and forecasts are produced for the out-of-sample testing period 1997:Q1-2010:Q4. This comparative validation can be useful to monetary policy analysis and macro-forecasting with the use of advanced Bayesian methods.

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“…The out-of-sample period is 2001:1-2009:4. The forecasting investigation is performed over the one-to …ve-quarter-ahead horizon with a rolling estimation sample, based on the works of Marcellino (2004) and Brüggemann et al (2008) for datasets of quarterly frequency. In particular, the models are re-estimated each quarter over the forecast horizon to update the estimate of the coe¢ cients, before producing the one-to …ve-quarters-ahead forecasts.…”

Section: Resultsmentioning

confidence: 99%

On the predictability of time-varying VAR and DSGE models

Bekiros

Paccagnini

2012

Empir Econ

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Over the last few years, there has been a growing interest in DSGE modelling for predicting macroeconomic ‡uctuations and conducting quantitative policy analysis. Hybrid DSGE models have become popular for dealing with some of the DSGE misspeci…cations as they are able to solve the tradeo¤ between theoretical coherence and empirical …t. However, these models are still linear and they do not consider time-variation for parameters. The time-varying properties in VAR or DSGE models capture the inherent nonlinearities and the adaptive underlying structure of the economy in a robust manner. In this paper, we present a state space time-varying parameter VAR model. Moreover, we focus on the DSGE-VAR that combines a micro-founded DSGE model with the ‡exibility of a VAR framework. All the aforementioned models as well simple DSGEs and Bayesian VARs are used in a comparative investigation of their out-of-sample predictive performance regarding the US economy. The results indicate that while in general the classical VAR and BVARs provide with good forecasting results, in many cases the TVP-VAR and the DSGE-VAR outperform the other models.

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Forecasting euro area variables with German pre‐EMU data

Cited by 15 publications

References 26 publications

Unemployment Convergence in Central and Eastern European Countries: Driving Forces and Cluster Behavior

Unemployment Convergence in Central and Eastern European Countries: Driving Forces and Cluster Behavior

Bayesian forecasting with small and medium scale factor-augmented vector autoregressive DSGE models

On the predictability of time-varying VAR and DSGE models

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