We consider the bias of the two-stage least squares (2SLS) estimator in linear instrumental variable regression with only one endogenous regressor. By using asymptotic expansion techniques, we approximate the 2SLS coefficient estimation bias under various scenarios regarding the number and...

This paper considers a semi-nonparametric cointegration test. The test uses the LM-testing principle. The score function needed for the LM-test is estimated from the data using an expansion of the density around a Student t distribution. In this way, we capture both the possible fat-tailedness...

This paper provides an extensive Monte-Carlo comparison of several contemporary cointegration tests. Apart from the familiar Gaussian based tests of Johansen, we also consider tests based on non-Gaussian quasi-likelihoods. Moreover, we compare the performance of these parametric tests with tests...

Kourogenis and Pittis (2008) show that the presence of a variance shift implies that the OLS t-statistic in a triangular cointegrated model displays asymptotic size distortions. For the same model, this paper provides two simple solutions to the size problems, the first based on White (1980)...

This paper considers a semi-nonparametric cointegration test. The test uses the LM-testing principle. The score function needed for the LM-test is estimated from the data using an expansion of the density around a Student t distribution. In this way, we capture both the possible fat-tailedness...

This paper provides an extensive Monte-Carlo comparison of several contemporary cointegration tests. Apart from the familiar Gaussian based tests of Johansen, we also consider tests based on non-Gaussian quasi-likelihoods. Moreover, we compare the performance of these parametric tests with tests...

The maximum likelihood estimator of the adjustment coefficient in a cointegrated vector autoregressive model (CVAR) is generally biased. For the case where the cointegrating vector is known in a first-order CVAR with no intercept, we derive a condition for the unbiasedness of the maximum...