1,721,058 research outputs found
Comparing density forecasts of aggregated time series via bootstrap
Full text linkWhen forecasting aggregated time series, several options are available. For example, the multivariate series or the individual time series might be predicted and then aggregated, or one may choose to forecast the aggregated series directly. While in theory an optimal disaggregated forecast will generally be superior (or at least not inferior) to forecasts based on aggregated information, this is not necessarily true in practical situations. The main reason is that the true data generating process is usually unknown and models need to be specified and estimated on the basis of the available information. This paper describes a bootstrap-based procedure, in the context of vector autoregressive models, for ranking the different forecasting approaches for contemporaneous time series aggregates. Uncertainty due to parameter estimation will be considered and the ranking will be based not only on the mean squared forecast error, but more in general on the performance of the forecast distribution. The forecasting procedures are applied to the United States aggregate inflation
Forecasting the distribution of aggregated time series: a bootstrap approach
Full text linkWhen forecasting aggregated time series, several options are available. For example, the multivariate series or the individual time series might be predicted and then aggregated, or one may choose to forecast the aggregated series directly. While in theory an optimal disaggregated forecast will generally be superior (or at least not inferior) to forecasts based on aggregated information, this is not necessarily true in practical situations. The main reason is that the true data generating process is usually unknown and models need to be specified and estimated on the basis of the available information. This paper describes a bootstrap-based procedure, in the context of vector autoregressive models, for ranking the different forecasting approaches for contemporaneous time series aggregates. Uncertainty due to parameter estimation will be considered and the ranking will be based not only on the mean squared forecast error, but more in general on the performance of the forecast distribution. The forecasting procedures are applied to the United States aggregate inflation
Bootstrap prediction regions for multivariate autoregressive processes
No full textTwo new methods for improving prediction regions in the context of vector
autoregressive (VAR) models are proposed. These methods, which are based
on the bootstrap technique, take into account the uncertainty associated with the estimation of the model order and parameters. In particular, by exploiting an independence property of the prediction error, we will introduce a bootstrap procedure that allows for better estimates of the forecasting distribution, in the sense that the variability of its quantile estimators is substantially reduced, without requiring additional bootstrap replications. The proposed methods have a good performance
even if the disturbances distribution is not Gaussian. An application to a real data set is presented
Bootstrap prediction intervals for autoregressions: some alternatives.
No full textA new method is proposed to obtain interval forecasts for autoregressive models taking into account the variability due to the estimation of the order and the parameters. The procedure improves that introduced by Masarotto (1990), allows a substantial reduction of the variance of the predictive distribution percentile estimators and should thus be considered as a useful alternative to the classic Box and Jenkins interval forecast. The method uses the bootstrap technique and is distribution-free. An empirical application is considered
Weighted transformed hazard ANOVA for censored and truncated data
No full textIn this paper ANOVA test procedures based on weighted transformations of the cumulative hazard are discussed. These procedures may be applied in situations where the observations are censored and/or truncated. Besides, the techniques examined are flexible thanks to the choice of different transformations and weight functions. The popular logrank test is used as a yardstick in the performance evaluation
Bootstrap prediction intervals for autoregressive models fitted to non-autoregressive processes
No full textAutoregressive processes, interval forecasts, bootstrap, robustness,
Misspecification testing for the conditional distribution model in GARCH-type processes.
No full textLooking for skewness in financial time series
No full textIn this paper, we study marginal and conditional skewness in financial returns for nine time series of major international stock indices. For this purpose, we develop a new variant of the GARCH model with dynamic skewness and kurtosis. Our empirical results indicate that there is no evidence of marginal asymmetry in the nine time series under consideration. We do however find significant time-varying conditional skewness. The economic significance of conditional skewness is analysed in terms of Value-at-Risk measures and Market Risk Capital Requirements set by the Basel Accord
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