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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 is larger 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 difficulties controlling size uniformly. Our approach works satisfactorily, controls size, and yields a test which outperforms the two other known similar tests.
Authors
Marcelo Moreira
Humberto Moreira
Rafael Mourão
Working Paper details
- DOI
- 10.1920/wp.cem.2016.1002
- Publisher
- IFS
Suggested citation
M, Moreira and H, Moreira and R, Mourão. (2016). A critical value function approach, with an application to persistent time-series. London: IFS. Available at: https://ifs.org.uk/publications/critical-value-function-approach-application-persistent-time-series (accessed: 2 July 2024).
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