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存在漂移时单位根的插值检验
This paper considers tests of the null hypothesis of a unit root in an observed time series against the alternative that the series is level stationary. Interpolation tests whose critical values depend upon the value of the drift parameter are compared to the standard Dickey-Fuller tests in Monte Carlo simulations. The interpolation test performs quite well especially against one-sided alternatives.
This paper considers tests of the null hypothesis of a unit root in an observed time series against the alternative that the series is level stationary. Interpolation tests whose critical values depend upon the value of the drift parameter are compared to the standard Dickey-Fuller tests in Monte Carlo simulations. The interpolation test performs quite well especially against one-sided alternatives.
含滞后内生变量时一阶向量自回归误差的检验
This paper derives an asymptotically valid test for first-order autoregressive errors. The test is derived in a simultaneous system of equations context, and allows lagged endogenous variables to be present in the model.
具有向量自回归误差的似乎不相关回归估计
We derive an asymptotically efficient method of estimating the coefficients of systems of seemingly unrelated regression equations when a first-order vector autoregressive error specification is hypothesized. This error specification is shown to contain several commonly used specifications as special cases. A Monte Carlo experiment was performed to examine the finite sample performance of our estimator in comparison with several other estimators. The general results were…
向量自回归误差下的似不相关回归估计
We derive an asymptotically efficient method of estimating the coefficients of systems of seemingly unrelated regression equations when a first-order vector autoregressive error specification is hypothesized. This error specification is shown to contain several commonly used specifications as special cases. A Monte Carlo experiment was performed to examine the finite sample performance of our estimator in comparison with several other estimators. The general results were…