nep-for New Economics Papers
on Forecasting
Issue of 2010‒05‒29
fifteen papers chosen by
Rob J Hyndman
Monash University

  1. Decomposing bias in expert forecast By Franses, Ph.H.B.F.
  2. VARs, Cointegration and Common Cycle Restrictions By Heather M Anderson; Farshid Vahid
  3. Are Forecast Updates Progressive? By Chang, C-L.; Franses, Ph.H.B.F.; McAleer, M.J.
  4. Testing the asset pricing model of exchange rates with survey data By Anna Naszodi
  5. On the Forecasting Accuracy of Multivariate GARCH Models By Sébastien Laurent; Jeroen V.K. Rombouts; Francesco Violante
  6. Forecasting Realized Volatility with Linear and Nonlinear Univariate Models By Michael McAleer; Marcelo C. Medeiros
  7. Fiscal sustainability and the accuracy of macroeconomic forecasts: do supranational forecasts rather than government forecasts make a difference? By Carlos Fonseca Marinheiro
  8. Macroeconomic forecasting using business cycle leading indicators = Macro-economisch voorspellen op basis van voorlopende conjunctuurindicatoren. By Reijer, Adrianus Hendrikus Johannes den
  9. Modelling and Forecasting Noisy Realized Volatility By Manuabu Asai; Michael McAleer; Marcelo C. Medeiros
  10. Time Varying Dimension Models By Joshua C.C. Chan; Garry Koop; Roberto Leon Gonzales; Rodney W. Strachan
  11. Are Some Forecasters Really Better Than Others? By D'Agostino, Antonello; McQuinn, Kieran; Whelan, Karl
  12. Maximum likelihood estimation of factor models on data sets with arbitrary pattern of missing data By Marta Bańbura; Michele Modugno
  13. Do Analysts Tell the Truth? Do Shareholders Listen? An Experimental Study of Analysts' Forecasts and Shareholder Reaction By Timothy Shields
  14. Speculation and Hedging in the Currency Futures Markets: Are They Informative to the Spot Exchange Rates By Aaron Tornell; Chunming Yuan
  15. Predicting Instability By Razzak, Weshah

  1. By: Franses, Ph.H.B.F.
    Abstract: Forecasts in the airline industry are often based in part on statistical models but mostly on expert judgment. It is frequently documented in the forecasting literature that expert forecasts are biased but that their accuracy is higher than model forecasts. If an expert forecast can be approximated by the weighted sum of a part that can be replicated by an analyst and a non-replicable part containing managerial intuition, the question arises which of two causes the bias. This paper advocates a simple regression-based strategy to decompose bias in expert forecasts. An illustration of the method to a unique database on airline revenues shows how it can be used to improve their experts’ forecasts.
    Keywords: expert forecasts;forecast bias;airline revenues
    Date: 2010–04–29
  2. By: Heather M Anderson; Farshid Vahid
    Abstract: This paper argues that VAR models with cointegration and common cycles can be usefully viewed as observable factor models. The factors are linear combinations of lagged levels and lagged differences, and as such, these observable factors have potential for forecasting. We illustrate this forecast potential in both a Monte Carlo and empirical setting, and demonstrate the difficulties in developing forecasting "rules of thumb" for forecasting in multivariate systems.
    Keywords: Common factors, Cross equation restrictions, Multivariate forecasting, Reduced rank models.
    JEL: C32 C53 E37
    Date: 2010–05
  3. By: Chang, C-L.; Franses, Ph.H.B.F.; McAleer, M.J.
    Abstract: Macro-economic forecasts typically involve both a model component, which is replicable, as well as intuition, which is non-replicable. Intuition is expert knowledge possessed by a forecaster. If forecast updates are progressive, forecast updates should become more accurate, on average, as the actual value is approached. Otherwise, forecast updates would be neutral. The paper proposes a methodology to test whether forecast updates are progressive and whether econometric models are useful in updating forecasts. The data set for the empirical analysis are for Taiwan, where we have three decades of quarterly data available of forecasts and updates of the inflation rate and real GDP growth rate. The actual series for both the inflation rate and the real GDP growth rate are always released by the government one quarter after the release of the revised forecast, and the actual values are not revised after they have been released. Our empirical results suggest that the forecast updates for Taiwan are progressive, and can be explained predominantly by intuition. Additionally, the one-, two- and three-quarter forecast errors are predictable using publicly available information for both the inflation rate and real GDP growth rate, which suggests that the forecasts can be improved.
    Keywords: macro-economic forecasts;econometric models;intuition;initial forecast;primary forecast;revised forecast;actual value;progressive forecast updates;forecast errors
    Date: 2010–04–29
  4. By: Anna Naszodi (Magyar Nemzeti Bank, 1054 Szabadság tér 8/9, 1850 Budapest, Hungary.)
    Abstract: This paper proposes a new test for the asset pricing model of the exchange rate. It examines whether the way market analysts generate their forecasts is closer to the one implied by the asset pricing model, or to any of those implied by some alternative models. The asset pricing model is supported by the test since it has significantly better out-ofsample fit on survey data than simpler models including the random walk. The traditional test based on forecasting ability is applied as well. The asset pricing model proves to have better forecast accuracy in case of some exchange rates and forecast horizons than the random walk. JEL Classification: F31, F36, G13.
    Keywords: asset pricing exchange rate model, present value model of exchange rate, survey data.
    Date: 2010–05
  5. By: Sébastien Laurent; Jeroen V.K. Rombouts; Francesco Violante
    Abstract: This paper addresses the question of the selection of multivariate GARCH models in terms of variance matrix forecasting accuracy with a particular focus on relatively large scale problems. We consider 10 assets from NYSE and NASDAQ and compare 125 model based one-step-ahead conditional variance forecasts over a period of 10 years using the model confidence set (MCS) and the Superior Predictive Ability (SPA) tests. Model performances are evaluated using four statistical loss functions which account for different types and degrees of asymmetry with respect to over/under predictions. When considering the full sample, MCS results are strongly driven by short periods of high market instability during which multivariate GARCH models appear to be inaccurate. Over relatively unstable periods, i.e. dot-com bubble, the set of superior models is composed of more sophisticated specifications such as orthogonal and dynamic conditional correlation (DCC), both with leverage effect in the conditional variances. However, unlike the DCC models, our results show that the orthogonal specifications tend to underestimate the conditional variance. Over calm periods, a simple assumption like constant conditional correlation and symmetry in the conditional variances cannot be rejected. Finally, during the 2007-2008 financial crisis, accounting for non-stationarity in the conditional variance process generates superior forecasts. The SPA test suggests that, independently from the period, the best models do not provide significantly better forecasts than the DCC model of Engle (2002) with leverage in the conditional variances of the returns.
    Keywords: Variance matrix, forecasting, multivariate GARCH, loss function, model confidence set, superior predictive ability
    JEL: C10 C32 C51 C52 C53 G10
    Date: 2010
  6. By: Michael McAleer (University of Canterbury); Marcelo C. Medeiros
    Abstract: In this paper we consider a nonlinear model based on neural networks as well as linear models to forecast the daily volatility of the S&P 500 and FTSE 100 futures. As a proxy for daily volatility, we consider a consistent and unbiased estimator of the integrated volatility that is computed from high frequency intra-day returns. We also consider a simple algorithm based on bagging (bootstrap aggregation) in order to specify the models analyzed.
    Keywords: Financial econometrics; volatility forecasting; neural networks; nonlinear models; realized volatility; bagging
    Date: 2010–05–01
  7. By: Carlos Fonseca Marinheiro (GEMF/Faculdade de Economia, Universidade de Coimbra, Portugal)
    Abstract: Credible fiscal plans that aim at restoring fiscal sustainability will be essential to counter the present increase in debt levels all across Europe. The macroeconomic scenario of such plans will be crucial. This paper assesses whether there is any advantage in delegating (part of) such power to supra-national forecasts. The evidence on the relative performance of the European Commission’s (EC) growth forecast is rather mixed, with considerable variation at the country level. Some national government forecasts (France, Italy, and Portugal) perform worse in terms of descriptive statistics than the EC forecast for all forecast horizons. For the year ahead the EC growth forecast is better than the official forecasts for almost ¾ of the EU-15 countries. All in all, since the EC forecast appears to be a good benchmark, in order to reduce the (optimistic) forecast bias, national governments could be forced to justify any large (optimistic) deviation from this benchmark when presenting their respective national stability and growth programmes.
    Keywords: Sustainability of public debt; Fiscal policy; Stability and Growth Pact; Fiscal forecasting; forecast evaluation; real-time data.
    JEL: H68 E17 E61 E62 H6
    Date: 2010–05
  8. By: Reijer, Adrianus Hendrikus Johannes den (Maastricht University)
    Date: 2010
  9. By: Manuabu Asai; Michael McAleer (University of Canterbury); Marcelo C. Medeiros
    Abstract: Several methods have recently been proposed in the ultra high frequency financial literature to remove the effects of microstructure noise and to obtain consistent estimates of the integrated volatility (IV) as a measure of ex-post daily volatility. Even bias-corrected and consistent (modified) realized volatility (RV) estimates of the integrated volatility can contain residual microstructure noise and other measurement errors. Such noise is called “realized volatility error”. Since such measurement errors are ignored, we need to take account of them in estimating and forecasting IV. This paper investigates through Monte Carlo simulations the effects of RV errors on estimating and forecasting IV with RV data. It is found that: (i) neglecting RV errors can lead to serious bias in estimators due to model misspecification; (ii) the effects of RV errors on one-step ahead forecasts are minor when consistent estimators are used and when the number of intraday observations is large; and (iii) even the partially corrected recently proposed in the literature should be fully corrected for evaluating forecasts. This paper proposes a full correction of , which can be applied to linear and nonlinear, short and long memory models. An empirical example for S&P 500 data is used to demonstrate that neglecting RV errors can lead to serious bias in estimating the model of integrated volatility, and that the new method proposed here can eliminate the effects of the RV noise. The empirical results also show that the full correction for is necessary for an accurate description of goodness-of-fit.
    Keywords: Realized volatility; diffusion; financial econometrics; measurement errors; forecasting; model evaluation; goodness-of-fit
    Date: 2010–05–01
  10. By: Joshua C.C. Chan; Garry Koop; Roberto Leon Gonzales; Rodney W. Strachan
    Abstract: Abstract: Time varying parameter (TVP) models have enjoyed an increasing popularity in empirical macroeconomics. However, TVP models are parameter-rich and risk over-fitting unless the dimension of the model is small. Motivated by this worry, this paper proposes several Time Varying dimension (TVD) models where the dimension of the model can change over time, allowing for the model to automatically choose a more parsimonious TVP representation, or to switch between different parsimonious representations. Our TVD models all fall in the category of dynamic mixture models. We discuss the properties of these models and present methods for Bayesian inference. An application involving US in.ation forecasting illustrates and compares the different TVD models. We find our TVD approaches exhibit better forecasting performance than several standard benchmarks and shrink towards parsimonious specifications.
    Date: 2010–05
  11. By: D'Agostino, Antonello (Central Bank and Financial Services Authority of Ireland); McQuinn, Kieran (Central Bank and Financial Services Authority of Ireland); Whelan, Karl (University College Dublin)
    Abstract: 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.
    JEL: C53 E27 E37
    Date: 2010–04
  12. By: Marta Bańbura (European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany.); Michele Modugno (European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany.)
    Abstract: In this paper we propose a methodology to estimate a dynamic factor model on data sets with an arbitrary pattern of missing data. We modify the Expectation Maximisation (EM) algorithm as proposed for a dynamic factor model by Watson and Engle (1983) to the case with general pattern of missing data. We also extend the model to the case with serially correlated idiosyncratic component. The framework allows to handle efficiently and in an automatic manner sets of indicators characterized by different publication delays, frequencies and sample lengths. This can be relevant e.g. for young economies for which many indicators are compiled only since recently. We also show how to extract a model based news from a statistical data release within our framework and we derive the relationship between the news and the resulting forecast revision. This can be used for interpretation in e.g. nowcasting applications as it allows to determine the sign and size of a news as well as its contribution to the revision, in particular in case of simultaneous data releases. We evaluate the methodology in a Monte Carlo experiment and we apply it to nowcasting and backdating of euro area GDP. JEL Classification: C53, E37.
    Keywords: Factor Models, Forecasting, Large Cross-Sections, Missing data, EM algorithm.
    Date: 2010–05
  13. By: Timothy Shields (The George L. Argyros School of Business and Economics, Chapman University)
    Abstract: This work experimentally examines forecasting and trading behavior. Subjects play the role of both analyst and shareholder over the course of experiments consisting of a series of repeated games with or absent con icts of interest. In a stylized trading setting, I test whether standard equilibrium, normative behavior, or limited strategic reasoning best predicts behavior. In the presence of con icts of interest a substantial proportion of subjects' behavior appears non-skeptical in the role of shareholder, though the same subject is deceptive in the role of analyst. Absent con icts of interest, subjects behavior in the role of shareholder is nearer a best response to the same subject's behavior as analyst. The results are consistent with limited strategic reasoning and suggest that simply disclosing con icts of interest does not evoke skepticism of forecasting, nor does the elimination of con icts of interest in itself induce honesty.
    Keywords: Experimental finance, under-reaction, overreaction, behavior, price inertia, risk aversion
    Date: 2010–01
  14. By: Aaron Tornell (University of California Los Angeles); Chunming Yuan (University of Maryland, Baltimore Couty)
    Abstract: This paper presents an empirical analysis investigating the relationship between the futures trading activities of speculators and hedgers and the potential movements of major spot exchange rates. A set of trader position measures are employed as regression predictors, including the level and change of net positions, an investor sentiment index, extremely bullish/bearish sentiments, and the peak/trough indicators. We find that the peaks and troughs of net positions are generally useful predictors to the evolution of spot exchange rates but other trader position measures are less correlated with future market movements. In addition, speculative position measures usually forecast price-continuations in spot rates while hedging position measures forecast price-reversals in these markets.
    Keywords: Spot Exchange Rates; Currency Futures; Speculation; Hedging; Commitments of Traders
    JEL: F31 F37 G13 G15
    Date: 2009–11–01
  15. By: Razzak, Weshah
    Abstract: Unanticipated shocks could lead to instability, which is reflected in statistically significant changes in distributions of independent Gaussian random variables. Changes in the conditional moments of stationary variables are predictable. We provide a framework based on a statistic for the Sample Generalized Variance, which is useful for interrogating real time data and to predicting statistically significant sudden and large shifts in the conditional variance of a vector of correlated macroeconomic variables. Central banks can incorporate the framework in the policy making process.
    Keywords: Sample Generalized Variance; Conditional Variance; Sudden and Large Shifts in the Moments
    JEL: E66 C3 C1
    Date: 2010–05–19

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