Forecasting with DSGE models

In this issue we have Forecasting with DSGE models, Predicting Opening Gaps from Overnight Foreign Stock Price Patterns, Forecasting from Mis-specified Models in the Presence of Unanticipated Location Shifts, Analyzing and Forecasting Volatility Spillovers, Asymmetries and Hedging in Major Oil Markets, and more.

    1. Date:
    By: Kai Christoffel (Directorate General Research, European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany.)
    Günter Coenen (Directorate General Research, European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany.)
    Anders Warne (Directorate General Research, European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany.)
    In this paper we review the methodology of forecasting with log-linearised DSGE models using Bayesian methods. We focus on the estimation of their predictive distributions, with special attention being paid to the mean and the covariance matrix of h-step ahead forecasts. In the empirical analysis, we examine the forecasting performance of the New Area-Wide Model (NAWM) that has been designed for use in the macroeconomic projections at the European Central Bank. The forecast sample covers the period following the introduction of the euro and the out-of-sample performance of the NAWM is compared to nonstructural benchmarks, such as Bayesian vector autoregressions (BVARs). Overall, the empirical evidence indicates that the NAWM compares quite well with the reduced-form models and the results are therefore in line with previous studies. Yet there is scope for improving the NAWM’s forecasting performance. For example, the mo del is not able to explain the moderation in wage growth over the forecast evaluation period and, therefore, it tends to overestimate nominal wages. As a consequence, both the multivariate point and density forecasts using the log determinant and the log predictive score, respectively, suggest that a large BVAR can outperform the NAWM. JEL Classification: C11, C32, E32, E37.
    Keywords: Bayesian inference, DSGE models, euro area, forecasting, open-economy macroeconomics, vector autoregression.
    1. Date:
    By: Jan G. de Gooijer (University of Amsterdam)
    Cees G.H. Diks (University of Amsterdam)
    Lukasz T. Gatarek (Erasmus University Rotterdam)
    This paper describes a forecasting exercise of close-to-open returns on major global stock indices, based on price patterns from foreign markets that have become available overnight. As the close-to-open gap is a scalar response variable to a functional variable, it is natural to focus on functional data analysis. Both parametric and non-parametric modeling strategies are considered, and compared with a simple linear benchmark model. The overall best performing model is nonparametric, suggesting the presence of nonlinear relations between the overnight price patterns and the opening gaps. This effect is mainly due to the European and Asian markets. The North-American and Australian markets appear to be informationally more efficient in that linear models using only the last available information perform well.
    Keywords: Close-to-open gap forecasting; Functional data analysis; International stock markets; Nonparametric modeling
    JEL: C14
    1. Date:
    By: Michael P. Clements
    David F. Hendry
    This chapter describes the issues confronting any realistic context for economic forecasting, which is inevitably based on unknowingly mis-specified models, usually estimated from mis-measured data, facing intermittent and often unanticipated location shifts. We focus on mitigating the systematic forecast failures that result in such settings, and describe the background to our approach, the difficulties of evaluating forecasts, and the devices that are more robust when change occurs.
    Keywords: Economic forecasting, Location shifts, Mis-specified models, Robust forecasts
    JEL: C51
    1. Date:
    By: Chia-Lin Chang,
    Michael McAleer (University of Canterbury)
    Roengchai Tansuchat
    Crude oil price volatility has been analyzed extensively for organized spot, forward and futures markets for well over a decade, and is crucial for forecasting volatility and Value-at-Risk (VaR). There are four major benchmarks in the international oil market, namely West Texas Intermediate (USA), Brent (North Sea), Dubai/Oman (Middle East), and Tapis (Asia-Pacific), which are likely to be highly correlated. This paper analyses the volatility spillover and asymmetric effects across and within the four markets, using three multivariate GARCH models, namely the constant conditional correlation (CCC), vector ARMA-GARCH (VARMA-GARCH) and vector ARMA-asymmetric GARCH (VARMA-AGARCH) models. A rolling window approach is used to forecast the 1-day ahead conditional correlations. The paper presents evidence of volatility spillovers and asymmetric effects on the conditional variances for most pairs of series. In addition, the forec ast conditional correlations between pairs of crude oil returns have both positive and negative trends. Moreover, the optimal hedge ratios and optimal portfolio weights of crude oil across different assets and market portfolios are evaluated in order to provide important policy implications for risk management in crude oil markets.
    Keywords: Volatility spillovers; multivariate GARCH; conditional correlation; asymmetries; hedging
    JEL: C22
    1. Date:
    By: Yin Liao
    Heather Anderson
    Farshid Vahid
    Realized volatility of stock returns is often decomposed into two distinct components that are attributed to continuous price variation and jumps. This paper proposes a tobit multivariate factor model for the jumps coupled with a standard multivariate factor model for the continuous sample path to jointly forecast volatility in three Chinese Mainland stocks. Out of sample forecast analysis shows that separate multivariate factor models for the two volatility processes outperform a single multivariate factor model of realized volatility, and that a single multivariate factor model of realized volatility outperforms univariate models.
    JEL: C13
    1. Date:
    By: Antonello D’Agostino (Central Bank of Ireland)
    Kieran McQuinn (Central Bank of Ireland)
    Karl Whelan (University College Dublin)
    In any dataset with individual forecasts of economic variables, some forecasters will perform better than others. However, it is possible that these ex post differences reflect sampling variation and thus overstate the ex ante differences between forecasters. In this paper, we present a simple test of the null hypothesis that all forecasters in the US Survey of Professional Forecasters have equal ability. We construct a test statistic that reflects both the relative and absolute performance of the forecaster and use bootstrap techniques to compare the empirical results with the equivalents obtained under the null hypothesis of equal forecaster ability. Results suggests limited evidence for the idea that the best forecasters are actually innately better than others, though there is evidence that a relatively small group of forecasters perform very poorly.
    Keywords: Forecasting, Bootstrap
    1. Date:
    By: Gogas, Periklis (Democritus University of Thrace, Department of International Economic Relations and Development)
    Pragidis, Ioannis (Democritus University of Thrace, Department of International Economic Relations and Development)
    Several studies have established the predictive power of the yield curve in terms of real economic activity. In this paper we use data for a variety of E.U. countries: both EMU (Germany, France, Italy) and non-EMU members (Sweden and the U.K.). The data used range from 1991:Q1 to 2009:Q1. For each country, we extract the long run trend and the cyclical component of real economic activity, while the corresponding interbank interest rates of long and short term maturities are used for the calculation of the country specific yield spreads. We also augment the models tested with non monetary policy variables: the countries’ unemployment rates and stock indices. The methodology employed in the effort to forecast real output, is a probit model of the inverse cumulative distribution function of the standard distribution, using several formal forecasting and goodness of fit evaluation tests. The results show that the yield curv e augmented with the non-monetary variables has significant forecasting power in terms of real economic activity but the results differ qualitatively between the individual economies examined raising non-trivial policy implications.
    Keywords: GDP; Probit; Forecasting; Yield Curve
    JEL: C53
  • Taken from the NEP-FOR mailing list edited by Rob Hyndman.