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采用数据依赖模型选择规则的修正平稳性检验AI 解读

We describe some simple methods for improving the performance of stationarity tests (i.e., tests that have a stationary null and a unit-root alternative). Specifically, we increase the rate of convergence of the test under the unit-root alternative from O p(T) to O p (T 2), then suggest an optimal method of selecting the order of the autoregressive component in the fitted autoregressive integrated moving average model on which the test is based. Simulation evidence suggests…

经济时间序列能通过差分变得平稳吗？AI 解读

This paper considers a class of nonstationary varying coefficient autoregressive models which allow stochastic variability in the autoregressive root. It is argued that such models provide a better description of the behaviour of macroeconomic variables than fixed unit root autoregressive models as they allow more general forms of nonstationarity. We construct a test of the null hypothesis of a fixed unit root against the alternative of a fixed unit root against the…

一种一致的单位根检验AI 解读

This article investigates several U.S. macroeconomic time series for the presence of a unit root using a newly developed test. This test has stationarity as its null hypothesis and the alternative is a unit-root process. The test is shown to be consistent and its asymptotic null distribution is determined. The authors' findings contrast sharply with those obtained via the standard unit-root tests.

维多利亚时代后期英法经济增长转折：证据的重新评估AI 解读

Abstract The paper investigates the question of whether the trend rate of growth of GDP slowed down in Britain or France in the nineteenth century. We establish that real GDP belongs to the trend‐stationary process class of time‐series in each case rather than the difference‐stationary process and using Kalman filter techniques, analyse trends in a model incorporating time‐varying parameters without specifying breakpoints in advance. We find, contrary to the literature, that…

系数恒定性检验统计量的分布AI 解读

This paper investigates the distribution of the locally best invariant test for coefficient constancy, against the random walk alternative, in a regression model which may contain nonvarying coefficients. Unfortunately, the distribution of the locally best invariant test depends critically on the exogenous variables of the model, even asymptotically. A modification is suggested which has a known asymptotic distribution regardless of the regression effect.

若干系数恒定性检验统计量的分布AI 解读

This paper investigates the distribution of the locally best invariant test for coefficient constancy, against the random walk alternative, in a regression model which may contain nonvarying coefficients. Unfortunately, the distribution of the locally best invariant test depends critically on the exogenous variables of the model, even asymptotically. A modification is suggested which has a known asymptotic distribution regardless of the regression effect.