Forecast Densities for Economic Aggregates from Disaggregate Ensembles

Ravazzolo, Francesco; Vahey, Shaun P.

doi:10.2139/ssrn.1601253

Cited by 26 publications

(38 citation statements)

References 51 publications

(39 reference statements)

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“…We also demonstrate forecast accuracy improvements for density forecasts using our inflation in parts framework, consistent with Ravazzolo and Vahey (2014) who generate forecasts of inflation that are aggregated from sub-components of inflation.…”

supporting

confidence: 74%

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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supporting

confidence: 74%

Forecasting Inflation: Phillips Curve Effects on Services Price Measures

Tallman

Zaman

2016

Working Paper (Federal Reserve Bank of Cleveland)

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“…This CRPS circumvents some of the drawbacks of the LS, as the latter does not reward values from the predictive density that are close but not equal to the realization (see, e.g., Gneiting and Raftery [2007]) and it is very sensitive to outliers; see Gneiting and Ranjan [2011], Groen et al [2012] and Ravazzolo and Vahey [2012] for applications to inflation density forecasts. The CRPS for the model k measures the average absolute distance between the empirical cumulative distribution function (CDF) of y t+h , which is simply a step function in y t+h , and the empirical CDF that is associated with model k's predictive density:…”

Section: Comparing Combination Schemesmentioning

confidence: 99%

Time-varying combinations of predictive densities using nonlinear filtering

Billio¹,

Casarin²,

Ravazzolo

et al. 2013

Journal of Econometrics

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138

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“…But the CRPS is thought to have advantages over the log score in that it is less sensitive to outliers and more sensitive to predictions that are close to but not equal to the outcome. Useful references on these density forecast measures include Mitchell and Hall (2005), Gneiting and Raftery (2007), Geweke and Amisano (2010), Gneiting and Ranjan (2011), and Ravazzolo and Vahey (2014).…”

Section: Forecast Evaluationmentioning

confidence: 99%

Oil-price density forecasts of US GDP

Ravazzolo¹,

Rothman

2016

Studies in Nonlinear Dynamics &Amp; Econometrics

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We carry out a pseudo out-of-sample density forecasting study for U.S. GDP with an autoregressive benchmark and alternatives to the benchmark than include both oil prices and stochastic volatility. The alternatives to the benchmark produce superior density forecasts. This comparative density performance appears to be driven more by stochastic volatility than by oil prices. We use our density forecasts to compute a recession risk indicator around the Great Recession. The alternative model that includes the real price of oil generates the earliest strong signal of a recession; but it also shows increased recession risk after the Great Recession.

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Forecast Densities for Economic Aggregates from Disaggregate Ensembles

Cited by 26 publications

References 51 publications

Forecasting Inflation: Phillips Curve Effects on Services Price Measures

Forecasting Inflation: Phillips Curve Effects on Services Price Measures

Time-varying combinations of predictive densities using nonlinear filtering

Oil-price density forecasts of US GDP

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