Forecasting With DSGE Models

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. Abstract Out-of-sample forecasting tests of DSGE models against time-series benchmarks such as an unrestricted VAR are increasingly used to check a) the speci…cation b) the forecasting capacity of these models. We carry out a Monte Carlo experiment on a widely-used DSGE model to investigate the power of these tests. We …nd that in speci…cation testing they have weak power relative to an in-sample indirect inference test; this implies that a DSGE model may be badly mis-speci…ed and still improve forecasts from an unrestricted VAR. In testing forecasting capacity they also have quite weak power, particularly on the lefthand tail. By contrast a model that passes an indirect inference test of speci…cation will almost de…nitely also improve on VAR forecasts. Terms of use: Documents in JEL categories: E10, E17

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“…1 Christoffel, Coenen, and Warne (2010) compare the forecast accuracy of the New Area-Wide Model (NAWM), designed and used by the European Central Bank for macroeconomic forecasts, with Bayesian DSGE-VARs, BVARs and reduced-form models. They find that the DSGE-VAR model outperforms DSGE, NAWM, VAR, and BVAR models in forecasting output.…”

Section: The Forecasting Process: Institutional Backgrounds and Data mentioning

confidence: 99%

Central Bank Macroeconomic Forecasting During the Global Financial Crisis: The European Central Bank and Federal Reserve Bank of New York Experiences

Alessi

¹

,

Ghysels

²

,

Onorante

³

et al. 2014

Journal of Business & Economic Statistics

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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 EconStor may AbstractThis paper documents macroeconomic forecasting during the global financial crisis by two key central banks: the European Central Bank and the Federal Reserve Bank of New York. The paper is the result of a collaborative effort between staff at the two institutions, allowing us to study the time-stamped forecasts as they were made throughout the crisis. The analysis does not exclusively focuses on point forecast performance. It also examines methodological contributions, including how financial market data could have been incorporated into the forecasting process.Keywords: Forecast evaluation; Mixed frequency data sampling. However, our analysis also goes beyond the traditional forecast evaluation exercises and discusses methodological advances which most likely will shape the future of the macroeconomic forecasting process at central banks. Since the main trigger of the crisis was a meltdown of the US subprime mortgage market, we pay special attention to whether financial market signals were fully accounted for in the central banks forecasting process. Considering that the crisis was first and foremost a crisis caused by financial market turmoil, we address the question of whether central banks could have done a better job at reading financial market signals.The issue requires one to think about mixed frequency data since financial market data are intrinsically high frequency (in our case daily) whereas the forecasts pertain to low frequency macroeconomic phenomena such as GDP growth. We propose to use MIDAS regression models. We find that simple MIDAS regressions using financial series could have improved the forecast accuracy both in the U.S. and in the euro area, and provide some evidence that the usefulness of financial series increased during the crisis.We also describe some new conceptual and methodological frameworks for prediction and ECB Working Paper 1688, July 2014 2 policy guidance which grew out of the challenges faced in particular at the FRBNY. We identify two key ingredients: (1) the emphasis on what might be called scenario-driven forecasting schemes, and (2) the recognition that one should pay attention to distributional features beyond point forecasts, in line with general notions of macroeconomic risk.Overall, we reach the following conclusions.• First, macroeconomic forecasting during the global financial crisis was a challenging task faced by the Federal Reserve Bank of New York and the...

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“…The main message of our paper is that the ability of DSGE models to forecast real exchange rates, highlighted for the euro area by Adolfson et al (2007b) and Christoffel et al (2011), should not be overplayed as other models that conform with the same principles perform equally well. We have shown that there is indeed not an appreciable difference in the forecasting performance of the DSGE, "AR fixed" and MBVAR models at medium-term horizons.…”

Section: Ecb Working Paper 1905 May 2016 24mentioning

confidence: 96%

“…The MBVAR model is instead more competitive at longer horizons. This highlights that the conclusions of Adolfson et al (2007b) and Christoffel et al (2011) no longer hold when confronted with more competitive benchmarks.…”

Section: Introductionmentioning

confidence: 97%

“…Long-term PPP is an intrinsic feature of open economy DSGE models and therefore should give them an edge in an exchange rate forecasting race with respect to the RW. Some indication of this was found in the studies by Adolfson et al (2007b) and Christoffel et al (2011), who showed that, at least for the case of the euro, the real exchange rate can be forecasted more accurately with an open-economy DSGE model than with the RW or BVAR models. To the extent that this result stays robust for other currencies, a longer sample span or tougher benchmarks and could be extended to nominal exchange rates, it would be clearly an important step forward.…”

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

Exchange rate forecasting with DSGE models

Zorzi

¹

,

Kolasa

²

,

Rubaszek

³

2017

Journal of International Economics

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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 EconStor may AbstractWe run a real exchange rate forecasting "horse race", which highlights that two principles hold. First, forecasts should not replicate the high volatility of exchange rates observed in sample. Second, models should exploit the mean reversion of the real exchange rate over long horizons. Abiding by these principles, an open-economy DSGE model performs well in real exchange rate forecasting. However, it fails to forecast nominal exchange rates better than the random walk. We find that the root cause is its inability to predict domestic and foreign inflation. This shortcoming leads us toward simpler ways to outperform the random walk. Non-technical summaryEconomic theory provides policymakers with clear guidance on how the competitiveness channel operates in the aftermath of a wide set of disturbances, such as monetary, productivity, risk premium or foreign shocks. However, there is a cloud hanging over this aspect of international economics, namely that these conjectures may have limited empirical significance, given the systematic failure of macro models to beat even the naïve random walk (RW) in exchange rate forecasting (the "exchange rate disconnect puzzle"). The question then naturally arises of whether international macro models are rich enough to be meaningful. Layers of complexity are typically added to improve their realism. For example, including in the features of the model the currency of trade invoicing may help the model to capture better the degree of exchange rate pass-through. Similarly, distinguishing the currency of denomination of asset and liabilities, may lead to a better description of the dynamics of external debt, which may be essential to better understand real exchange rate movements in emerging countries. On the other hand, imposing too many restrictions on the data generating process, either theoretically or in the estimation phase, may prove disadvantageous from a pure forecasting perspective given the higher number of estimated parameters. Every cloud has a silver lining, however. The exchange rate disconnect puzzle has spurred economists to look for new directions of research with success. Openeconomy dynamic stochastic general equilibrium (DSGE) models are clearly a major accomplishment from the theoretical perspective. The empirical literature has also shown why, by properly accounting for estimation error, exchange rate models may be better than we usually think. The consen...

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Forecasting With DSGE Models

Cited by 56 publications

References 88 publications

How Good are Out of Sample Forecasting Tests on DSGE Models?

How Good are Out of Sample Forecasting Tests on DSGE Models?

Central Bank Macroeconomic Forecasting During the Global Financial Crisis: The European Central Bank and Federal Reserve Bank of New York Experiences

Exchange rate forecasting with DSGE models

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