Using Entropic Tilting to Combine BVAR Forecasts with External Nowcasts

Krüger, Fabian; Clark, Todd E.; Ravazzolo, Francesco

doi:10.26509/wp-201439

Cited by 14 publications

(27 citation statements)

References 4 publications

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“…8. Faust and Wright (2013) show that including accurate nowcast information dramatically improves infl ation forecasting performance, especially in the near term; see also the results in Krueger et al (2014). To this end, the infl ation nowcasting model developed by Knotek and Zaman (2014) could be used to generate nowcasts, and these nowcasts can be used to condition near-term infl ation outcomes with an eye toward improving longer-term infl ation forecast accuracy.…”

Section: Footnotesmentioning

confidence: 99%

Measuring Inflation Forecast Uncertainty

2015

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Looking across a range of statistical models, we consider the likely path of future inflation and the uncertainty surrounding the models' predictions. The models suggest that inflation is on a rising path, and while inflation forecast uncertainty is somewhat elevated relative to the norms of the last 20 years, core inflation uncertainty is relatively low. For both inflation rates, forecast uncertainty is much lower as of the first quarter of 2015 than it was around the Great Recession.

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Section: Footnotesmentioning

confidence: 99%

Measuring Inflation Forecast Uncertainty

2015

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“…For example, g(r t+1 ) may represent quantities such as the mean, g(r t+1 ) = r t+1 , the variance, g(r t+1 ) = (r t+1 −E[r t+1 ]) 2 , or higher moments of the predictive distribution; see e.g., Robertson et al (2005) for a very informative exposition. The information could be in the form of moment restrictions implied by economic theory, such as Euler conditions in Giacomini and Ragusa (2013), or could be coming from survey forecasts and model-based nowcasts as in Altavilla et al (2014) and Krüger et al (2015).…”

Section: General Methodsmentioning

confidence: 99%

Option-Implied Equity Premium Predictions via Entropic Tilting

Μεταξόγλου

Pettenuzzo

Smith

2018

Journal of Financial Econometrics

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We propose a new method to improve density forecasts of the equity premium using information from options markets. We tilt the predictive densities from standard econometric models suggested in the stock return predictability literature towards the second moment of the risk-neutral distribution implied by options prices. In so doing, we use a simple regression-based approach to remove the variance risk premium. By combining the backward-looking information contained in the econometric models with the forward-looking information from the options prices, tilting yields sharper predictive densities. Using density forecasts of the U.S. equity premium in Rapach and Zhou (2012), we find that tilting leads to more accurate predictions, both in terms of statistical and economic performance.

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“…Croushore (1993) and Lahiri and Wang (2013) provide discussions of the relative accuracy of SPF forecasts over short and long horizons. Hafer and Hein (1983) and Faust and Wright (2013) argue that SPF forecasts for inflation represent a difficult benchmark for time series models to beat, as do Kruger, Clark and Ravazzolo (2014) for nowcasting of both quarterly inflation and GDP growth. However, these authors use the quarterly point forecasts provided by the SPF, which are the medians of the survey respondents' forecasts of the level of each variable.…”

Section: Forecast Comparison With Spfmentioning

confidence: 99%

“…Croushore (1993) and Lahiri and Wang (2013) discuss the relative accuracy of SPF forecasts over various horizons. Hafer and Hein (1983), Faust and Wright (2013) and Kruger, Clark and Ravazzolo (2014) argue that SPF point forecasts perform well relative to existing time series models. We examine in detail the density forecasts for GDP growth over -2009, and inflation over 2009-2010 matching vintage data, we find the predictive distributions from the copula model exhibit a similar level of asymmetry and kurtosis to those produced in the quarterly reports, and differ substantially from those of the BVAR models.…”

Section: Introductionmentioning

confidence: 99%

Asymmetric Forecast Densities for U.S. Macroeconomic Variables from a Gaussian Copula Model of Cross-Sectional and Serial Dependence

Smith

Vahey

2015

SSRN Journal

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Most existing reduced-form macroeconomic multivariate time series models employ elliptical disturbances, so that the forecast densities produced are symmetric. In this paper, we use a copula model with asymmetric margins to produce forecast densities with the scope for severe departures from symmetry. Empirical and skew t distributions are employed for the margins, and a high-dimensional Gaussian copula is used to jointly capture cross-sectional and (multivariate) serial dependence. The copula parameter matrix is given by the correlation matrix of a latent stationary and Markov vector autoregression (VAR). We show that the likelihood can be evaluated efficiently using the unique partial correlations, and estimate the copula using Bayesian methods. We examine the forecasting performance of the model for four U.S. macroeconomic variables between 1975:Q1 and 2011:Q2 using quarterly real-time data. We find that the point and density forecasts from the copula model are competitive with those from a Bayesian VAR. During the recent recession the forecast densities exhibit substantial asymmetry, avoiding some of the pitfalls of the symmetric forecast densities from the Bayesian VAR. We show that the asymmetries in the predictive distributions of GDP growth and inflation are similar to those found in the probabilistic forecasts from the Survey of Professional Forecasters. Last, we find that unlike the linear VAR model, our fitted Gaussian copula models exhibit nonlinear dependencies between some macroeconomic variables.

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Using Entropic Tilting to Combine BVAR Forecasts with External Nowcasts

Cited by 14 publications

References 4 publications

Measuring Inflation Forecast Uncertainty

Measuring Inflation Forecast Uncertainty

Option-Implied Equity Premium Predictions via Entropic Tilting

Asymmetric Forecast Densities for U.S. Macroeconomic Variables from a Gaussian Copula Model of Cross-Sectional and Serial Dependence

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