Nowcasting GDP in Real Time: A Density Combination Approach

Aastveit, Knut Are; Gerdrup, Karsten R.; Jore, Anne Sofie; Thorsrud, Leif Anders

doi:10.1080/07350015.2013.844155

Cited by 114 publications

(100 citation statements)

References 45 publications

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“…In particular, we follow Aastveit et al (2011) as well as Geweke andAmisano (2011, 2012) and calculate in each period T that vector w * T −1 which maximizes the historical (log) predictive likelihood:…”

Section: Bvar Averaging With Optimal Weightsmentioning

confidence: 99%

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Point and density forecasts for the euro area using Bayesian VARs

Berg

Henzel

2015

International Journal of Forecasting

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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. Recent articles suggest that a Bayesian vector autoregression (BVAR) with shrinkage is a good forecast device even when the number of variables is large. In this paper we evaluate different variants of the BVAR with respect to their forecast accuracy for euro area real GDP growth and HICP inflation. We consider BVAR averaging, Bayesian factor augmented VARs (BFAVARs), and large BVARs, which differ in the way information is condensed and shrinkage is implemented. We find that: (a) large BVARs produce accurate point forecasts but show a poor performance when the entire density is considered; (b) BVAR averaging shows the opposite pattern; (c) BFAVARs perform well under both evaluation criteria; (d) choosing the degree of shrinkage optimally does not improve forecast accuracy; (e) all variants except the large BVAR tend to be well calibrated for inflation but poorly calibrated for real GDP growth; (f) these findings are robust to several features of the forecast experiment. Terms of use: Documents inJEL-Code: C110, C520, C530, E370.

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Section: Bvar Averaging With Optimal Weightsmentioning

confidence: 99%

“…Thus, the linear combination of predictive densities is again a predictive density (see Hall and Mitchell, 2007;Aastveit, Gerdrup, Jore, and Thorsrud, 2011;Geweke andAmisano, 2011, 2012, among others).…”

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confidence: 99%

Point and density forecasts for the euro area using Bayesian VARs

Berg

Henzel

2015

International Journal of Forecasting

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“…Garratt, Mitchell, Vahey, and Wakerly (2011) and Aastveit, Gerdrup, Jore, and Thorsrud (2014) show that these recursive weighting schemes perform well when combining density forecasts of inflation and GDP respectively. The latter study also finds that this scheme performs better in terms of point forecast evaluation than standard point forecast combinations.…”

Section: Introductionmentioning

confidence: 99%

Combining density forecasts using focused scoring rules

Opschoor

Dijk

Wel

2017

J of Applied Econometrics

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SummaryWe investigate the added value of combining density forecasts focused on a specific region of support. We develop forecast combination schemes that assign weights to individual predictive densities based on the censored likelihood scoring rule and the continuous ranked probability scoring rule (CRPS) and compare these to weighting schemes based on the log score and the equally weighted scheme. We apply this approach in the context of measuring downside risk in equity markets using recently developed volatility models, including HEAVY, realized GARCH and GAS models, applied to daily returns on the S&P 500, DJIA, FTSE and Nikkei indexes from 2000 until 2013. The results show that combined density forecasts based on optimizing the censored likelihood scoring rule significantly outperform pooling based on equal weights, optimizing the CRPS or log scoring rule. In addition, 99% Value-at-Risk estimates improve when weights are based on the censored likelihood scoring rule.

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“…Recently, policy makers and central bankers in particular have shown increasing interest in probabilistic information for policy (see Aastveit et al (2014), Clark (2011). As such we evaluate the density forecast performance of our model against models viewed as accurate benchmarks in the inflation forecasting literature.…”

Section: Pseudo Out-of-sample Forecasting Results: Density Forecastsmentioning

confidence: 99%

Forecasting Inflation: Phillips Curve Effects on Services Price Measures

Tallman

Zaman

2016

Working Paper (Federal Reserve Bank of Cleveland)

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We estimate an empirical model of infl ation that exploits a Phillips Curve relationship between a measure of unemployment and a subaggregate measure of infl ation (services). We generate an aggregate infl ation forecast from forecasts of the goods subcomponent separate from the services subcomponent, and compare the aggregated forecast to the leading time-series univariate and standard Phillips curve forecasting models. Our results indicate marked improvements in point and density forecasting accuracy statistics for models that exploit relationships between services infl ation and the unemployment rate.

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Nowcasting GDP in Real Time: A Density Combination Approach

Cited by 114 publications

References 45 publications

Point and density forecasts for the euro area using Bayesian VARs

Point and density forecasts for the euro area using Bayesian VARs

Combining density forecasts using focused scoring rules

Forecasting Inflation: Phillips Curve Effects on Services Price Measures

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