nep-for New Economics Papers
on Forecasting
Issue of 2016‒04‒16
five papers chosen by
Rob J Hyndman
Monash University

  1. The role of economic policy uncertainty in predicting U.S. recessions: A mixed-frequency Markov-switching vector autoregressive approach By Balcilar, Mehmet; Gupta, Rangan; Segnon, Mawuli
  2. Evaluating Multi-Step System Forecasts with Relatively Few Forecast-Error Observations By David Hendry; Andrew B. Martinez
  3. Rethinking Performance Evaluation By Campbell R. Harvey; Yan Liu
  4. Bootstrap prediction intervals for factor models By Sílvia Gonçalves; Benoit Perron; Antoine Djogbenou
  5. Spatial Ensemble Post-Processing with Standardized Anomalies By Markus Dabernig; Georg J. Mayr; Jakob W. Messner; Achim Zeileis

  1. By: Balcilar, Mehmet; Gupta, Rangan; Segnon, Mawuli
    Abstract: This paper analyzes the performance of the monthly economic policy uncertainty (EPU) index in predicting recessionary regimes of the (quarterly) U.S. GDP. In this regard, the authors apply a mixed-frequency Markov-switching vector autoregressive (MF-MSVAR) model, and compare its in-sample and out-of-sample forecasting performances to those of a Markov-switching vector autoregressive model (MS-VAR, where the EPU is averaged over the months to produce quarterly values) and a Markov-switching autoregressive (MS-AR) model. The results show that the MF-MS-VAR fits the different recession regimes, and provides out-of-sample forecasts of recession probabilities which are more accurate than those derived from the MS-VAR and MS-AR models. The results highlight the importance of using high-frequency values of the EPU, and not averaging them to obtain quarterly values, when forecasting recessionary regimes for the U.S. economy.
    Keywords: business cycles,economic policy uncertainty,mixed frequency,Markov-switching VAR models
    JEL: E32 E37 C32
    Date: 2016
  2. By: David Hendry; Andrew B. Martinez
    Abstract: Abstract: This paper develops a new approach for evaluating multi-step system forecasts with relatively few forecast-error observations. It extends Clements and Hendry (1993a) using Abadir et al.(2014) to generate "design-free" estimates of the general matrix of the forecast-error second-moment when there are relatively few forecast-error observations. Simulations show that the usefulness of alternative methods deteriorates when their assumptions are violated. The new approach compares well against these methods and provides correct forecast rankings.
    Keywords: Invariance, Forecast Evaluation, Forecast Error, Moment Matrices, MSFE, GFESM
    JEL: C22 C32 C53
    Date: 2016–03–08
  3. By: Campbell R. Harvey; Yan Liu
    Abstract: We show that the standard equation-by-equation OLS used in performance evaluation ignores information in the alpha population and leads to severely biased estimates for the alpha population. We propose a new framework that treats fund alphas as random effects. Our framework allows us to make inference on the alpha population while controlling for various sources of estimation risk. At the individual fund level, our method pools information from the entire alpha distribution to make density forecast for the fund's alpha, offering a new way to think about performance evaluation. In simulations, we show that our method generates parameter estimates that universally dominate the OLS estimates, both at the population and at the individual fund level. While it is generally accepted that few if any mutual funds outperform, we find that the fraction of funds that generate positive alphas is accurately estimated at over 10%. An out-of-sample forecasting exercise also shows that our method generates superior alpha forecasts.
    JEL: G10 G11 G12 G14 G23
    Date: 2016–03
  4. By: Sílvia Gonçalves; Benoit Perron; Antoine Djogbenou
    Abstract: We propose bootstrap prediction intervals for an observation h periods into the future and its conditional mean. We assume that these forecasts are made using a set of factors extracted from a large panel of variables. Because we treat these factors as latent, our forecasts depend both on estimated factors and estimated regression coefficients. Under regularity conditions, Bai and Ng (2006) proposed the construction of asymptotic intervals under Gaussianity of the innovations. The bootstrap allows us to relax this assumption and to construct valid prediction intervals under more general conditions. Moreover, even under Gaussianity, the bootstrap leads to more accurate intervals in cases where the cross-sectional dimension is relatively small as it reduces the bias of the OLS estimator as shown in a recent paper by Gonçalves and Perron (2014).
    Keywords: factor model, bootstrap, forecast, conditional mean,
    Date: 2016–04–11
  5. By: Markus Dabernig; Georg J. Mayr; Jakob W. Messner; Achim Zeileis
    Abstract: To post-process ensemble predictions to a particular location, often statistical methods are used, especially in complex terrain such as the Alps. When expanded to several stations, the post-processing has to be repeated at every station individually thus losing information about spatial coherence and increasing computational cost. Therefore, we transform observations and predictions to standardized anomalies. Site- and season-specific characteristics are eliminated by subtracting a climatological mean and dividing by the climatological standard deviation from both observations and numerical forecasts. Then ensemble post-processing can be applied simultaneously at multiple locations. Furthermore, this method allows to forecast even at locations where no observations are available. The skill of these forecasts is comparable to forecasts post-processed individually at every station, and even better on average.
    Keywords: statistical post-processing, ensemble post-processing, spatial, temperature, standardized anomalies, climatology, generalized additive model
    JEL: C53 C61 Q50
    Date: 2016–04

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