A critical value function approach, with an application to persistent time-series
Data(s) |
13/06/2016
13/06/2016
06/06/2016
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Resumo |
Researchers often rely on the t-statistic to make inference on parameters in statistical models. It is common practice to obtain critical values by simulation techniques. This paper proposes a novel numerical method to obtain an approximately similar test. This test rejects the null hypothesis when the test statistic islarger than a critical value function (CVF) of the data. We illustrate this procedure when regressors are highly persistent, a case in which commonly-used simulation methods encounter dificulties controlling size uniformly. Our approach works satisfactorily, controls size, and yields a test which outperforms the two other known similar tests. Researchers often rely on the t-statistic to make inference on parameters in statistical models. It is common practice to obtain critical values by simulation techniques. This paper proposes a novel numerical method to obtain an approximately similar test. This test rejects the null hypothesis when the test statistic islarger than a critical value function (CVF) of the data. We illustrate this procedure when regressors are highly persistent, a case in which commonly-used simulation methods encounter dificulties controlling size uniformly. Our approach works satisfactorily, controls size, and yields a test which outperforms the two other known similar tests. |
Identificador |
0104-8910 |
Idioma(s) |
en_US |
Publicador |
Fundação Getulio Vargas. Escola de Pós-graduação em Economia |
Relação |
Ensaios Econômicos;778 |
Palavras-Chave | #t-statistic #bootstrap #subsampling #similar tests #Estatística |
Tipo |
Working Paper |