acrt-package {acrt}R Documentation

Autocorrelation Robust Testing

Description

The package acrt provides functions for testing affine restrictions on the regression coefficient vector in linear models with autocorrelated errors. The methods implemented in acrt are based on the article Pötscher and Preinerstorfer (2016). In particular, the package can be used to compute various autocorrelation robust test statistics; to compute critical values that provide size control when the error process is Gaussian (or, more generally, elliptically symmetric) and autocorrelated; and to compute the size of a test that is obtained from an autocorrelation robust test statistic and a user-supplied critical value.

Details

acrt provides three functions:

  1. The function F.type.test.statistic can be used to compute test statistics of the form T_w or T_{E, \mathsf{W}} as defined in Pötscher and Preinerstorfer (2016). The class of test statistics of the form T_w or T_{E, \mathsf{W}} includes F-type tests based on covariance estimators with data-independent bandwidth parameters and without prewhitening as considered in, e.g., Newey and West (1987), Andrews (1991), Kiefer and Vogelsang (2002, 2005), cf. also Preinerstorfer and Pötscher (2016).

  2. The function critical.value provides an implementation of Algorithm 1 in Pötscher and Preinerstorfer (2016), and can be used to determine size-controlling critical values for test statistics of the form T_w or T_{E, \mathsf{W}}.

  3. The function size provides an implementation of Algorithm 2 in Pötscher and Preinerstorfer (2016), and can be used to determine the size of tests that are based on test statistics of the form T_w or T_{E, \mathsf{W}} and a user-supplied critical value (e.g., obtained from asymptotic theory).

We refer the user to Pötscher and Preinerstorfer (2016) for details concerning the framework, the test statistics, the algorithms, and the underlying theoretical results.

References

Andrews, D. W. K. (1991). Heteroskedasticity and autocorrelation consistent covariance matrix estimation. Econometrica, 59 817-858.

Kiefer, N. M. and Vogelsang, T. J. (2002). Heteroskedasticity - autocorrelation robust standard errors using the Bartlett kernel without truncation. Econometrica, 70 2093-2095.

Kiefer, N. M. and Vogelsang, T. J. (2005). A new asymptotic theory for heteroskedasticity - autocorrelation robust tests. Econometric Theory, 21 1130-1164.

Newey, W. K. and West, K. D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55 703-708.

Pötscher, B.M. and Preinerstorfer, D. (2016). Controlling the size of autocorrelation robust tests. https://arxiv.org/abs/1612.06127/

Preinerstorfer, D. and Pötscher, B. M. (2016). On size and power of heteroskedasticity and autocorrelation robust tests. Econometric Theory, 32 261-358.


[Package acrt version 1.0.1 Index]