Testing exogeneity in the bivariate probit model:

, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. A Monte Carlo (MC) experiment

This paper considers the properties of the tests of the null hypothesis of a random walk against the alternative of a stochastic unit root when the data are generated by a process related to, but not exactly equal to, the processes under the null and alternative. The extensive Monte Carlo

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institution for postgraduate education and research in economics and the social sciences in Austria.

sample performance of the proposed estimator in terms of efficiency and testing of hypothesis. The Monte Carlo evidence suggests that the proposed estimator performs adequately under different data generated processes, especially for small samples that are the most practical situations.

Abstract Monte Carlo evidence is provided that suggests that the continuous updating estimator might have a moment problem. Its performance in terms of sample median and standard deviation is virtually identical to the one of the limited information maximum likelihood estimator.

We conduct an extensive Monte Carlo experiment to examine the finite samples properties of maximum likelihood based inference in the bivariate probit model with endogenous dummy. We analyse the relative performance of alternative exogeneity tests, the impact of distributional mis

Least squares (LS) and maximum likelihood (ML) estimation are considered for unit root processes with GARCH (1, 1) errors. The asymptotic distributions of LS and ML estimators are derived under the condition α+β < 1. The former has the usual unit root distribution and the latter is a functional of a bivariate Brownian motion, as in Ling and Li (1998). Several unit root tests based on LS estimators, ML estimators, and mixing LS and ML estim-tors, are constructed. Simulation results show that tests based on mixing LS and ML estimators perform better than Dickey-Fuller tests which are based on LS estimators, and that tests based on the ML estimators perform better than the mixed estimators.