nep-for New Economics Papers
on Forecasting
Issue of 2015‒05‒09
eleven papers chosen by
Rob J Hyndman
Monash University

  2. Forecasting in Nonstationary Environments: What Works and What Doesn’t in Reduced-Form and Structural Models By Raffaella Giacomini; Barbara Rossi
  3. Forecaster overconfidence and market survey performance By Deaves, Richard; Lei, Jin; Schröder, Michael
  4. CBO's Economic Forecasting Record: 2015 Update By Congressional Budget Office
  5. Application of periodic autoregressive process to the modeling of the Garonne river flows By PEREAU Jean-Christophe; URSU Eugen
  6. Revisiting the Grennbook's relative forecasting performance By Paul Hubert
  7. Prior selection for panel vector autoregressions By Korobilis, Dimitris
  9. On the Forecast Combination Puzzle By Wei Qian; Craig A. Rolling; Gang Cheng; Yuhong Yang
  10. Macroeconomic Uncertainty Indices Based on Nowcast and Forecast Error Distributions By Barbara Rossi; Tatevik Sekhposyan
  11. Mortality and Longevity Risks in the United Kingdom: Dynamic Factor Models and Copula-Functions By Helena Chuliá; Montserrat Guillén; Jorge M. Uribe

  1. By: Hans Christian Müller-Dröge (Handelsblatt Newspaper); Tara M. Sinclair (The George Washington University); Herman O. Stekler (The George Washington University)
    Abstract: In this paper we present an evaluation of forecasts of a vector of variables of the German economy made by different institutions. Our method permits one to evaluate the forecasts for each year and then if one is interested to combine the years. We use our method to determine an overall winner for a forecasting competition across twenty-five different institutions for a single time period using a vector of eight key economic variables. Typically forecasting competitions are judged on a variable-by-variable basis, but our methodology allows us to determine how each competitor performed overall. We find that the Bundesbank was the overall winner for 2013.
    Keywords: Mahalanobis Distance, forecasting competition, GDP components, German macroeconomic data
    JEL: C5 E2 E3
    Date: 2014–07
  2. By: Raffaella Giacomini; Barbara Rossi
    Abstract: This review provides an overview of forecasting methods that can help researchers forecast in the presence of non-stationarities caused by instabilities. The emphasis of the review is both theoretical and applied, and provides several examples of interest to economists. We show that modeling instabilities can help, but it depends on how they are modeled. We also show how to robustify a model against instabilities.
    Keywords: forecasting, instabilities, structural breaks
    Date: 2014–12
  3. By: Deaves, Richard; Lei, Jin; Schröder, Michael
    Abstract: We document using the ZEW panel of German stock market forecasters that weak forecasters tend to be overconfident in the sense that they provide extreme forecasts and their confidence intervals are less likely to contain eventual realizations. Moderate filters based on forecast accuracy over short rolling windows are somewhat successful in improving predictability. While poor performance can be due to various factors, a filter based on a prior tendency to provide extreme forecasts also improves predictability.
    Keywords: Overconfidence,Forecasting Performance,Stock Market
    JEL: G02 G17
    Date: 2015
  4. By: Congressional Budget Office
    Abstract: CBO regularly evaluates the quality of its economic forecasts by comparing them with the economy’s actual performance and with forecasts by the Administration and the Blue Chip consensus (an average of about 50 private-sector forecasts). CBO’s forecasts generally have been comparable in quality with those of the Administration and the Blue Chip consensus. When CBO’s projections have proved inaccurate by large margins, the errors have tended to reflect difficulties shared by other forecasters.
    JEL: C53
    Date: 2015–02–12
  5. By: PEREAU Jean-Christophe; URSU Eugen
    Abstract: Accurate forecasting of river flows is one of the most important applications in hydrology, especially for the management of reservoir systems. To capture the seasonal variations in river flow statistics, this paper develops a robust modeling approach to identify and estimate periodic autoregressive (PAR) model in the presence of additive outliers. Since the least squares estimators are not robust in the presence of outliers, we suggest a robust estimation based on residual autocovariances. A genetic algorithm with Bayes information criterion is used to identify the optimal PAR model. The method is applied to average monthly and quarter-monthly flow data (1959-2010) for the Garonne river in the southwest of France. Results show that forecasts are better off in the robust model rather than the unrobust model. The accuracy of the forecasts is also improved when the model is specified in quarter-monthly flows, especially for the dry seasons.
    Keywords: River flows analysis, periodic time series, robust estimation, genetic algorithms, Garonne river
    JEL: C22 C53 Q25
    Date: 2015
  6. By: Paul Hubert (OFCE)
    Abstract: Since Romer and Romer (2000), a large literature has dealt with the relative forecasting performance of Greenbook macroeconomic forecasts of the Federal Reserve. This paper empirically reviews the existing results by comparing the different methods, data and samples used previously. The sample period is extended compared to previous studies and both real-time and final data are considered. We confirm that the Fed has a superior forecasting performance on inflation but not on output. In addition, we show that the longer the horizon, the more pronounced the advantage of Fed on inflation and that this superiority seems to decrease but remains prominent in the more recent period. The second objective of this paper is to underline the potential sources of this superiority. It appears that it may stem from better information rather than from a better model of the economy.
    Keywords: monetary policy; greenbook; forecasts
    Date: 2015–04
  7. By: Korobilis, Dimitris
    Abstract: There is a vast literature that specifies Bayesian shrinkage priors for vector autoregressions (VARs) of possibly large dimensions. In this paper I argue that many of these priors are not appropriate for multi-country settings, which motivates me to develop priors for panel VARs (PVARs). The parametric and semi-parametric priors I suggest not only perform valuable shrinkage in large dimensions, but also allow for soft clustering of variables or countries which are homogeneous. I discuss the implications of these new priors for modelling interdependencies and heterogeneities among different countries in a panel VAR setting. Monte Carlo evidence and an empirical forecasting exercise show clear and important gains of the new priors compared to existing popular priors for VARs and PVARs.
    Keywords: Bayesian model selection; shrinkage; spike and slab priors; forecasting; large vector autoregression
    JEL: C11 C32 C33 C52
    Date: 2015–04
  8. By: Constantin Burgi (The George Washington University)
    Abstract: The forecast combination literature has optimal combination methods, however, empirical studies have shown that the simple average is notoriously difficult to improve upon. This paper introduces a novel way to choose a subset of forecasters who might have specialized knowledge to improve upon the simple average over all forecasters in the SPF. In particular, taking the average of forecasters that recently beat the simple average more than the calibrated threshold of 52.5% of times can statistically significantly outperform the simple average for 10-year treasury bond yields, CPI inflation and unemployment at some horizons.
    Keywords: Forecast combination; Forecast evaluation; Multiple model comparisons; Real-time data; Survey of Professional Forecasters
    JEL: C22 C52 C53
    Date: 2015–03
  9. By: Wei Qian; Craig A. Rolling; Gang Cheng; Yuhong Yang
    Abstract: It is often reported in forecast combination literature that a simple average of candidate forecasts is more robust than sophisticated combining methods. This phenomenon is usually referred to as the "forecast combination puzzle". Motivated by this puzzle, we explore its possible explanations including estimation error, invalid weighting formulas and model screening. We show that existing understanding of the puzzle should be complemented by the distinction of different forecast combination scenarios known as combining for adaptation and combining for improvement. Applying combining methods without consideration of the underlying scenario can itself cause the puzzle. Based on our new understandings, both simulations and real data evaluations are conducted to illustrate the causes of the puzzle. We further propose a multi-level AFTER strategy that can integrate the strengths of different combining methods and adapt intelligently to the underlying scenario. In particular, by treating the simple average as a candidate forecast, the proposed strategy is shown to avoid the heavy cost of estimation error and, to a large extent, solve the forecast combination puzzle.
    Date: 2015–05
  10. By: Barbara Rossi; Tatevik Sekhposyan
    Abstract: We propose new indices to measure macroeconomic uncertainty. The indices measure how unexpected a realization of a representative macroeconomic variable is relative to the unconditional forecast error distribution. We use forecast error distributions based on the nowcasts and forecasts of the Survey of Professional Forecasters. We further compare the new indices with those proposed in the literature and assess their macroeconomic impact.
    JEL: C18 E01 E30
    Date: 2015–04
  11. By: Helena Chuliá (Department of Econometrics, Riskcenter-IREA, Universitat de Barcelona); Montserrat Guillén (Department of Econometrics, Riskcenter-IREA, Universitat de Barcelona); Jorge M. Uribe (Facultad de Ciencias Sociales y Economicas, Universidad del Valle)
    Abstract: We present a methodology to forecast mortality rates and estimate longevity and mortality risks. The methodology uses Generalized Dynamic Factor Models fitted over the differences of the log-mortality rates. We compare prediction performance with models previously proposed in the literature, such as the traditional Static Factor Model fitted over the level of log-mortality rates. We also construct risk measures by the means of vinecopulae simulations, taking into account the dependence between the idiosyncratic components of the mortality rates. The methodology is implemented to project the mortality rates of the United Kingdom, for which we consider a portfolio and study longevity and mortality risks.
    Keywords: Longevity, mortality forecasting, factor models, vine-copulae, Value at Risk.
    Date: 2015–03

This nep-for issue is ©2015 by Rob J Hyndman. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.