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摘要暂缺

摘要暂缺

不稳定解释变量回归中普通最小二乘估计量的渐近有效性AI 解读

For regression models with general unstable regressors having characteristic roots on the unit circle and general stationary errors independent of the regressors, sufficient conditions are investigated under which the ordinary least squares estimator (OLSE) is asymptotically efficient in that it has the same limiting distribution as the generalized least squares estimator (GLSE) under the same normalization. A key condition for the asymptotic efficiency of the OLSE is that…

可能非平稳时间序列的非对称性检验AI 解读

Tests for asymmetric adjustment in possibly nonstationary, nearly nonstationary, or stationary time series data are developed. The asymmetry is modeled by the momentum threshold autoregressive model of Enders and Granger and an extension of it. The tests are t-type tests and Wald tests based on instrumental-variable estimators and are asymptotically normal or chi-squared regardless of stationarity/nonstationarity of data-generating processes. This is in contrast to the fact…

摘要暂缺

自回归过程的柯西估计量及单位根检验与置信区间AI 解读

For autoregressive processes, we propose new estimators whose pivotal statistics have the standard normal limiting distribution for all ranges of the autoregressive parameters. The proposed estimators are approximately median unbiased. For seasonal time series, the new estimators give us unit root tests that have limiting normal distribution regardless of period of the seasonality. Using the estimators, confidence intervals of the autoregressive parameters are constructed. A…

基于自适应最大似然估计的单位根检验AI 解读

Adaptive maximum likelihood estimators of unit roots in autoregressive processes with possibly non-Gaussian innovations are considered. Unit root tests based on the adaptive estimators are constructed. Limiting distributions of the test statistics are derived, which are linear combinations of two functionals of Brownian motions. A Monte Carlo simulation reveals that the proposed tests have improved powers over the classical Dickey–Fuller tests when the distribution of the…

基于对称估计量的双重单位根半参数检验AI 解读

The authors develop new semiparametric tests for double unit roots under a weakly dependent error structure of Phillips for tests for a unit root. The tests are based on symmetric estimation of Sen and Dickey. Through Monte Carlo simulations, the new tests are compared with the tests of Haldrup and of Dickey and Pantula. Their tests have empirical sizes close to the nominal size even when the innovations follow a negatively autocorrelated moving average, for which the…