nep-for New Economics Papers
on Forecasting
Issue of 2008‒09‒05
six papers chosen by
Rob J Hyndman
Monash University

  1. Averaging forecasts from VARs with uncertain instabilities By Todd E. Clark; Michael W. McCracken
  2. Improving forecast accuracy by combining recursive and rolling forecasts By Todd E. Clark; Michael W. McCracken
  3. Tests of equal predictive ability with real-time data By Todd E. Clark; Michael W. McCracken
  4. The cyclical component factor model By Christian M. Dahl; Henrik Hansen; John Smidt
  5. Tourism Forecasting: Accuracy of Alternative Econometric Models Revisited By Haiyan Song; Egon Smeral; Gang Li; Jason L. Chen
  6. Modelling and Forecasting Multivariate Realized Volatility By Roxana Chiriac; Valeri Voev

  1. By: Todd E. Clark; Michael W. McCracken
    Abstract: Recent work suggests VAR models of output, inflation, and interest rates may be prone to instabilities. In the face of such instabilities, a variety of estimation or forecasting methods might be used to improve the accuracy of forecasts from a VAR. The uncertainty inherent in any single representation of instability could mean that combining forecasts from a range of approaches will improve forecast accuracy. Focusing on models of U.S. output, prices, and interest rates, this paper examines the effectiveness of combining various models of instability in improving VAR forecasts made with real-time data.
    Keywords: Econometric models ; Economic forecasting
    Date: 2008
  2. By: Todd E. Clark; Michael W. McCracken
    Abstract: This paper presents analytical, Monte Carlo, and empirical evidence on combining recursive and rolling forecasts when linear predictive models are subject to structural change. Using a characterization of the bias-variance tradeoff faced when choosing between either the recursive and rolling schemes or a scalar convex combination of the two, we derive optimal observation windows and combining weights designed to minimize mean square forecast error. Monte Carlo experiments and several empirical examples indicate that combination can often provide improvements in forecast accuracy relative to forecasts made using the recursive scheme or the rolling scheme with a fixed window width.
    Keywords: Economic forecasting ; Econometric models
    Date: 2008
  3. By: Todd E. Clark; Michael W. McCracken
    Abstract: This paper examines the asymptotic and finite-sample properties of tests of equal forecast accuracy applied to direct, multi-step predictions from both non-nested and nested linear regression models. In contrast to earlier work in the literature, our asymptotics take account of the real-time, revised nature of the data. Monte Carlo simulations indicate that our asymptotic approximations yield reasonable size and power properties in most circumstances. The paper concludes with an examination of the real-time predictive content of various measures of economic activity for inflation.
    Keywords: Economic forecasting ; Real-time data
    Date: 2008
  4. By: Christian M. Dahl; Henrik Hansen; John Smidt (School of Economics and Management, University of Aarhus, Denmark)
    Abstract: Forecasting using factor models based on large data sets have received ample attention due to the models’ ability to increase forecast accuracy with respect to a range of key macroeconomic variables in the US and the UK. However, forecasts based on such factor models do not uniformly outperform the simple autoregressive model when using data from other countries. In this paper we propose to estimate the factors based on the pure cyclical components of the series entering the large data set. Monte Carlo evidence and an empirical illustration using Danish data shows that this procedure can indeed improve on pseudo real time forecast accuracy.
    Keywords: Factor model, Cyclical components, Estimation, Real time forecasting
    JEL: C13 C22 C52
    Date: 2008–09–02
  5. By: Haiyan Song; Egon Smeral (WIFO); Gang Li; Jason L. Chen
    Abstract: This study evaluates the forecasting accuracy of five alternative econometric models in the context of predicting the quarterly international tourism demand in 25 countries or country groupings. Tourism demand is measured in terms of tourist expenditure by inbound international visitors in a destination. Two univariate time series models are included in the forecasting comparison as benchmarks. Accuracy is assessed in terms of error magnitude. Seasonality is an important feature of forecasting models and requires careful handling. For each of the 25 destinations, individual models are estimated over the 1980Q1-2005Q1 period, and forecasting performance is assessed using data covering the 2005Q2-2007Q1 period. The empirical results show that the time-varying parameter (TVP) model provides the most accurate short-term forecasts, whereas the naïve (no-change) model performs best in long-term forecasting up to two years. This study provides new evidence of the TVP model's outstanding performance in short-term forecasting. Through the incorporation of a seasonal component into the model, the TVP model forecasts short-run seasonal tourism demand well.
    Keywords: tourism forecasting, econometric models, time series models, forecasting accuracy
    Date: 2008–08–13
  6. By: Roxana Chiriac; Valeri Voev (School of Economics and Management, University of Aarhus, Denmark)
    Abstract: This paper proposes a methodology for modelling time series of realized covariance matrices in order to forecast multivariate risks. The approach allows for flexible dynamic dependence patterns and guarantees positive definiteness of the resulting forecasts without imposing parameter restrictions. We provide an empirical application of the model, in which we show by means of stochastic dominance tests that the returns from an optimal portfolio based on the model’s forecasts second-order dominate returns of portfolios optimized on the basis of traditional MGARCH models. This result implies that any risk-averse investor, regardless of the type of utility function, would be better-off using our model.
    Keywords: Forecasting, Fractional integration, Stochastic dominance, Portfolio optimization, Realized covariance
    JEL: C32 C53 G11
    Date: 2008–09–02

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