Asymptotic normal and bootstrap inference in structural VAR analysis

Article Abstract:

The performance of the bootstrap and asymptotic parametric inference methods in structural vector autoregressive analysis is examined using the Monte Carlo method. Results suggest that the two approaches are at par with each other in almost all cases. However, the bootstrap method performs better in terms of the length of the confidence interval and coverage in the presence of highly nonlinear statistics. The asymptotic method, however, outperforms the bootstrap when it comes to distribution of errors.

Usage, Vector analysis

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A bootstrap simulation study in ARMA (p,q) structures

Article Abstract:

The boostrap non-parametric computer-intensive statistics technique allows the variability of a statistic to be described based on a unique finite sample. It was first introduced in 1979 to remedy the problems associated with the finite sample and asymptotic theories. Although widely applied to various statistical problems, the technique has rarely been used for time series. The application of the bootstrap to a simulation study where artificial time series were generated is studied.

Statistics, Statistics (Data)

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A re-evaluation of the quasi-Bayes approach to the linear combination of forecasts

Article Abstract:

A study was conducted on the use of outperformance, quasi-Bayes and optimal combination in the linear combination of forecasts. The methodologies were employed to petroleum price predictions of Petrobras. Results affirmed that linear combining forecast models are effective tools especially when results are desired in terms of the smallest mean squared errors.

Analysis, Bayesian statistical decision theory, Bayesian analysis, Forecasting

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