Bootstrapping a stable ad model: weak vs strong exogeneity

Article Abstract:

The accuracy of bootstraping statistical inference procedures in small samples is examined in autoregressive distributed-lag models having non-stationary regressors. A comparison of the finite sample consequences of the strongly and weakly exogenous regressors, in four varying bootstrap procedures, is made using the Monte Carlo method. A conditional model is involved in two of these procedures, which are found to surmount the small sample problems justifiably well. A resampling of the marginal model also provides good results.

Methods, Statistical sampling, Sampling (Statistics)

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Bootstrapping the trace statistic in VAR models: Monte Carlo results and applications

Article Abstract:

A study was conducted to investigate the robustness of inference about the number of cointegrating relations using the trace statistic based on two bootstrap implementations in error correction models. Findings indicated that the use of the bootstrap, since the bootstrap simulates the distribution of the test statistic under the null for each particular sample. Inference based on the ordinary bootstrap, in the Monte Carlo experiments, performs as well as inference based on asymptotic critical values.

Vector analysis

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Bootstrapping the trace statistic in VAR models: Monte Carlo results and applications

Article Abstract:

The power and size features of a bootstrapped trace statistic in two prototypical data generating processes are examined. Monte Carlo experiments are used to determine the size and power properties. Monte Carlo data suggest that ordinary bootstraps share size and power properties with inference procedures based on asymptotic critical values. The stationary bootstrap is suggested as a diagnostic tool.

Analysis, Statistics, Economics

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