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周期结构与季节单位根检验AI 解读

The standard testing procedures for seasonal unit roots developed so far have been based mainly on time-invariant autoregressive integrated moving average (ARIMA) processes with AR polynomials involving seasonal differencing. One attractive alternative is to use periodic ARMA models. Convenient procedures are presented for testing for the presence of unit roots at the zero and seasonal frequencies in periodic time series. The limiting distributions are derived and tabulated…

基于数据预检验选模的时间序列单位根检验AI 解读

In this article we examine the impact of data-based lag-length estimation on the behavior of the augmented Dickey–Fuller (ADF) test for a unit root. We derive conditions under which the ADF test converges to the distribution tabulated by Dickey and Fuller and verify that these conditions are satisfied by several commonly employed lag-selection strategies. Simulation evidence indicates that the performance of the ADF test is considerably improved when the lag length is…

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部分调整模型中调整速度的估计AI 解读

We reexamine a recent controversy in the inventory literature regarding the impact of estimator choice on estimated speeds of adjustment in partial adjustment models. We compare the properties of two asymptotically equivalent estimators, the residual-adjusted Aitken (RAA) and nonlinear least squares (NLS) estimators. Our results indicate that (a) the RAA estimator is very sensitive to the quality of the instrument used in first-round estimation, and (b) model…

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针对半非参数备择的正态性拉格朗日乘子检验AI 解读

In this article, I derive the Lagrange multiplier test of the null hypothesis that a stationary random vector has a (possibly heteroscedastic) normal distribution against the alternative that the distribution is a member of the family with seminonparametric probability density functions considered by Gallant and Tauchen (1989). The test is shown to contain special case of the moment tests proposed by Newey (1985) and Tauchen (1985). Evidence from a small simulation study is…

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移动平均误差下的单位根检验AI 解读

In this paper we propose a new approach to testing for unit roots in a time series {yt} with moving average innovations based on an instrumental variable estimator. If {yt} is a random walk with moving average innovations, we derive the limiting distribution of the instrumental variable estimator when the estimated model is either (i) the true model, (ii) a random walk with shift in mean, or (iii) a random walk with shift in mean and a linear time trend. These distributions…