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Forecasting Ability (forecasting + ability)
Selected AbstractsDiscussion of Self-Selection and the Forecasting Abilities of Female Equity AnalystsJOURNAL OF ACCOUNTING RESEARCH, Issue 2 2010PAOLA SAPIENZA First page of article [source] The term structure of interest rates and the Mexican economyCONTEMPORARY ECONOMIC POLICY, Issue 3 2000JG. Gonzalez Can the yield spread, which has been found to predict with surprising accuracy the movement of key macroeconomic variables of developed countries, also predict such variables for a developing country experiencing economic turmoil? This article presents empirical results that suggest significant forecasting ability for the yield spread for segments of the Mexican economy during the 1995,1997 period of economic volatility. The actual and predicted variable changes sometimes conflict with those experienced by developed countries in part because of the unusually close relationship between the Mexican Treasury and the Banco de México. Consequently, analysts and policy officials may exploit the forecast potential of the yield spread, but only in the context of evolving institutional considerations. [source] Marketing Category Forecasting: An Alternative of BVAR-Artificial Neural Networks¶DECISION SCIENCES, Issue 4 2000James J. Jiang ABSTRACT Analyzing scanner data in brand management activities presents unique difficulties due to the vast quantity of the data. Time series methods that are able to handle the volume effectively often are inappropriate due to the violation of many statistical assumptions in the data characteristics. We examine scanner data sets for three brand categories and examine properties associated with many time series forecasting methods. Many violations are found with respect to linearity, normality, autocorrelation, and heteroscedasticity. With this in mind we compare the forecasting ability of neural networks that require no assumptions to two of the more robust time series techniques. Neural networks provide similar forecasts to Bayesian vector autoregression (BVAR), and both outperform generalized autoregressive conditional herteroscedasticty (GARCH) models. [source] Forecasting the Direction of Policy Rate Changes: The Importance of ECB WordsECONOMIC NOTES, Issue 1-2 2009Carlo Rosa This paper evaluates the predictive power of different information sets for the European Central Bank (ECB) interest-rate-setting behaviour. We employ an ordered probit model, i.e. a limited dependent variable framework, to take into account the discreteness displayed by policy rate changes. The results show that the forecasting ability of standard Taylor-type variables, such as inflation and output gap, is fairly low both in-sample and out-of-sample, and is comparable to the performance of the random walk model. Instead by using broader information sets that include measures of core inflation, exchange rates, monetary aggregates and financial conditions, the accuracy of the forecasts about ECB future actions substantially improves. Moreover, ECB rhetoric considerably contributes to a better understanding of its policy reaction function. Finally, we find that that the ECB has been fairly successful in educating the public to anticipate the overall future direction of its monetary policy, but has been less successful in signalling the exact timing of rate changes. [source] Deterministic and Stochastic Methods for Estimation of Intra-day Seasonal Components with High Frequency DataECONOMIC NOTES, Issue 2 2001Andrea Beltratti We introduce a model for the analysis of intra-day volatility based on unobserved components. The stochastic seasonal component is essential to model time-varing intra-day effects. The model is estimated with high frequency data for Deutsche mark,US dollar for 1993 and 1996. The model performs well in terms of coherence with the theoretical aggregation properties of GARCH models, it is effective in terms of both forecasting ability and describing reactions to macroeconomic news. (J.E.L.: C14, C53, F31). [source] Economic Sentiment and Yield Spreads in EuropeEUROPEAN FINANCIAL MANAGEMENT, Issue 2 2008Eva Ferreira G12; E43 Abstract According toHarvey (1988), the forecasting ability of the term spread on economic growth is due to the fact that interest rates reflect investors' expectations about the future economic situation when deciding their plans for consumption and investment. Past literature has used ex post data on output or consumption growth as proxies for their expected value. In this paper, we employ a direct measure of economic agents' expectations, the Economic Sentiment Indicator elaborated by the European Commission, to test this hypothesis. Our results indicate that a linear combination of European yield spreads explains a surprising 93.7\% of the variability of the Economic Sentiment Indicator. This ability of yield spreads to capture economic agent expectations may be the actual reason for the predictive power of yield spreads about future business cycle. [source] TESTING LONG-HORIZON PREDICTIVE ABILITY WITH HIGH PERSISTENCE, AND THE MEESE,ROGOFF PUZZLE*INTERNATIONAL ECONOMIC REVIEW, Issue 1 2005Barbara Rossi A well-known puzzle in international finance is that a random walk predicts exchange rates better than economic models. I offer a potential explanation. When exchange rates and fundamentals are highly persistent, long-horizon forecasts of economic models are biased by the estimation error. When this bias is big, a random walk will forecast better, even if the economic model is true. I propose a test for equal predictability in the presence of high persistence. It shows that the poor forecasting ability of economic models does not imply that the models are not good descriptions of the data. [source] Optimal sampling frequency for volatility forecast models for the Indian stock marketsJOURNAL OF FORECASTING, Issue 1 2009Malay Bhattacharyya Abstract This paper evaluates the performance of conditional variance models using high-frequency data of the National Stock Index (S&P CNX NIFTY) and attempts to determine the optimal sampling frequency for the best daily volatility forecast. A linear combination of the realized volatilities calculated at two different frequencies is used as benchmark to evaluate the volatility forecasting ability of the conditional variance models (GARCH (1, 1)) at different sampling frequencies. From the analysis, it is found that sampling at 30 minutes gives the best forecast for daily volatility. The forecasting ability of these models is deteriorated, however, by the non-normal property of mean adjusted returns, which is an assumption in conditional variance models. Nevertheless, the optimum frequency remained the same even in the case of different models (EGARCH and PARCH) and different error distribution (generalized error distribution, GED) where the error is reduced to a certain extent by incorporating the asymmetric effect on volatility. Our analysis also suggests that GARCH models with GED innovations or EGRACH and PARCH models would give better estimates of volatility with lower forecast error estimates. Copyright © 2008 John Wiley & Sons, Ltd. [source] Can forecasting performance be improved by considering the steady state?JOURNAL OF FORECASTING, Issue 1 2008An application to Swedish inflation, interest rate Abstract This paper investigates whether the forecasting performance of Bayesian autoregressive and vector autoregressive models can be improved by incorporating prior beliefs on the steady state of the time series in the system. Traditional methodology is compared to the new framework,in which a mean-adjusted form of the models is employed,by estimating the models on Swedish inflation and interest rate data from 1980 to 2004. Results show that the out-of-sample forecasting ability of the models is practically unchanged for inflation but significantly improved for the interest rate when informative prior distributions on the steady state are provided. The findings in this paper imply that this new methodology could be useful since it allows us to sharpen our forecasts in the presence of potential pitfalls such as near unit root processes and structural breaks, in particular when relying on small samples.,,Copyright © 2008 John Wiley & Sons, Ltd. [source] Forecasting interest rate swap spreads using domestic and international risk factors: evidence from linear and non-linear modelsJOURNAL OF FORECASTING, Issue 8 2007Ilias Lekkos Abstract This paper explores the ability of factor models to predict the dynamics of US and UK interest rate swap spreads within a linear and a non-linear framework. We reject linearity for the US and UK swap spreads in favour of a regime-switching smooth transition vector autoregressive (STVAR) model, where the switching between regimes is controlled by the slope of the US term structure of interest rates. We compare the ability of the STVAR model to predict swap spreads with that of a non-linear nearest-neighbours model as well as that of linear AR and VAR models. We find some evidence that the non-linear models predict better than the linear ones. At short horizons, the nearest-neighbours (NN) model predicts better than the STVAR model US swap spreads in periods of increasing risk conditions and UK swap spreads in periods of decreasing risk conditions. At long horizons, the STVAR model increases its forecasting ability over the linear models, whereas the NN model does not outperform the rest of the models.,,Copyright © 2007 John Wiley & Sons, Ltd. [source] Forecasting volatility by means of threshold modelsJOURNAL OF FORECASTING, Issue 5 2007M. Pilar Muñoz Abstract The aim of this paper is to compare the forecasting performance of competing threshold models, in order to capture the asymmetric effect in the volatility. We focus on examining the relative out-of-sample forecasting ability of the SETAR-Threshold GARCH (SETAR-TGARCH) and the SETAR-Threshold Stochastic Volatility (SETAR-THSV) models compared to the GARCH model and Stochastic Volatility (SV) model. However, the main problem in evaluating the predictive ability of volatility models is that the ,true' underlying volatility process is not observable and thus a proxy must be defined for the unobservable volatility. For the class of nonlinear state space models (SETAR-THSV and SV), a modified version of the SIR algorithm has been used to estimate the unknown parameters. The forecasting performance of competing models has been compared for two return time series: IBEX 35 and S&P 500. We explore whether the increase in the complexity of the model implies that its forecasting ability improves. Copyright © 2007 John Wiley & Sons, Ltd. [source] Forecasting euro area inflation using dynamic factor measures of underlying inflationJOURNAL OF FORECASTING, Issue 7 2005Gonzalo Camba-Mendez Abstract Standard measures of prices are often contaminated by transitory shocks. This has prompted economists to suggest the use of measures of underlying inflation to formulate monetary policy and assist in forecasting observed inflation. Recent work has concentrated on modelling large data sets using factor models. In this paper we estimate factors from data sets of disaggregated price indices for European countries. We then assess the forecasting ability of these factor estimates against other measures of underlying inflation built from more traditional methods. The power to forecast headline inflation over horizons of 12 to 18 months is adopted as a valid criterion to assess forecasting. Empirical results for the five largest euro area countries, as well as for the euro area itself, are presented. Copyright © 2005 John Wiley & Sons, Ltd. [source] Can cointegration-based forecasting outperform univariate models?JOURNAL OF FORECASTING, Issue 5 2002An application to Asian exchange rates Abstract Conventional wisdom holds that restrictions on low-frequency dynamics among cointegrated variables should provide more accurate short- to medium-term forecasts than univariate techniques that contain no such information; even though, on standard accuracy measures, the information may not improve long-term forecasting. But inconclusive empirical evidence is complicated by confusion about an appropriate accuracy criterion and the role of integration and cointegration in forecasting accuracy. We evaluate the short- and medium-term forecasting accuracy of univariate Box,Jenkins type ARIMA techniques that imply only integration against multivariate cointegration models that contain both integration and cointegration for a system of five cointegrated Asian exchange rate time series. We use a rolling-window technique to make multiple out of sample forecasts from one to forty steps ahead. Relative forecasting accuracy for individual exchange rates appears to be sensitive to the behaviour of the exchange rate series and the forecast horizon length. Over short horizons, ARIMA model forecasts are more accurate for series with moving-average terms of order >1. ECMs perform better over medium-term time horizons for series with no moving average terms. The results suggest a need to distinguish between ,sequential' and ,synchronous' forecasting ability in such comparisons. Copyright © 2002 John Wiley & Sons, Ltd. [source] Modelling the development of supply-restricted telecommunications marketsJOURNAL OF FORECASTING, Issue 4 2001Towhidul Islam Abstract A large proportion of the world telecommunications market can be characterized as supply restricted. In ITU (1999) official waiting lists numbered about 50 million worldwide with an average waiting time of two years. More than 100 countries had not eliminated the waiting list for telephone connections and hence a supply restricted market prevailed in all of these countries. Only about 25 countries have succeeded in eradicating their waiting list for basic telephone service. In terms of the pattern of diffusion, the subscriber's flow from waiting applicants to adopters is controlled by supply restrictions adding an important dimension that needs to be addressed when modeling and forecasting demand. An empirical analysis of the diffusion of main telephones in 46 supply-restricted countries is presented to demonstrate the usefulness of a three-stage Bass model that has been proposed to capture the dynamics of supply restrictions. We also compare the forecasting ability of different approaches to estimation when panel data are available. Copyright © 2001 John Wiley & Sons, Ltd. [source] A convection scheme for data assimilation: Description and initial testsTHE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 606 2005Philippe Lopez Abstract A new simplified parametrization of subgrid-scale convective processes has been developed and tested in the framework of the ECMWF Integrated Forecasting System for the purpose of variational data assimilation, singular vector calculations and adjoint sensitivity experiments. Its formulation is based on the full nonlinear convection scheme used in ECMWF forecasts, but a set of simplifications has been applied to substantially improve its linear behaviour. These include the specification of a single closure assumption based on convective available potential energy, the uncoupling of the equations for the convective mass flux and updraught characteristics and a unified formulation of the entrainment and detrainment rates. Simplified representations of downdraughts and momentum transport are also included in the new scheme. Despite these simplifications, the forecasting ability of the new convective parametrization is shown to remain satisfactory even in seasonal integrations. A detailed study of its Jacobians and the validity of the linear hypothesis is presented. The new scheme is also tested in combination with the new simplified parametrization of large-scale clouds and precipitation recently developed at ECMWF. In contrast with the simplified convective parametrization currently used in ECMWF's operational 4D-Var, its tangent-linear and adjoint versions account for perturbations of all convective quantities including convective mass flux, updraught characteristics and precipitation fluxes. Therefore the new scheme is expected to be beneficial when combined with radiative calculations that are directly affected by condensation and precipitation. Examples are presented of applications of the new moist physics in 1D-Var retrievals using microwave brightness temperature measurements and in adjoint sensitivity experiments. Copyright © 2005 Royal Meteorological Society. [source] | ||||||||||