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时变二阶矩下的平稳性检验AI 解读

In this paper we analyze the effects of a very general class of time-varying variances on well-known “stationarity” tests of the I(0) null hypothesis. Our setup allows, among other things, for both single and multiple breaks in variance, smooth transition variance breaks, and (piecewise-) linear trending variances. We derive representations for the limiting distributions of the test statistics under variance breaks in the errors of I(0), I(1), and near-I(1) data generating…

长记忆回归过程中的结构变化检验AI 解读

The paper considers tests for structural change in time series regression models where both regressors and residuals may exhibit long range dependence. The limiting distribution of the test statistic depends on unknown parameters and is approximated by a bootstrap procedure. The asymptotic validity of bootstrap is shown and performance of the testing procedure is examined in a simple Monte Carlo experiment.

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GARCH 模型残差的高阶矩部分和过程及其应用AI 解读

In this paper we construct high moment partial sum processes based on residuals of a GARCH model when the mean is known to be 0. We consider partial sums of kth powers of residuals, CUSUM processes and self-normalized partial sum processes. The kth power partial sum process converges to a Brownian process plus a correction term, where the correction term depends on the kth moment μk of the innovation sequence. If μk=0, then the correction term is 0 and, thus, the kth power…

允许横截面依赖的购买力平价面板平稳性检验AI 解读

We investigate the purchasing power parity (PPP) hypothesis for a group of 17 countries using a new panel-based test of stationarity that allows for arbitrary cross-sectional dependence. We treat the short-run time series dynamics nonparametrically and thus avoid the need to fit separate, and potentially misspecified, models for the individual series. The statistic is simple to compute and uses standard normal critical values, even in the presence of a wide range of…

永久与暂时创新切换的不可观测成分模型AI 解读

This article proposes an unobserved-component model in which component innovations are governed by a state variable that follows a Markov process. The proposed model is capable of describing both stationary and nonstationary behaviors of real data and allows the random innovations to have permanent and transitory effects in different periods. The model also permits a deterministic trend with or without breaks and hence bridges the gap between the trend-stationary model and a…

参数不稳定下嵌套模型选择的最优检验AI 解读

This paper develops optimal tests for model selection between two nested models in the presence of underlying parameter instability. These are joint tests for both parameter instability and a null hypothesis on a subset of the parameters. They modify the existing tests for parameter instability to allow the parameter vector to be unknown. These test statistics are useful if one is interested in testing a null hypothesis on some parameters but is worried about the possibility…

美国制造业部门的多层面板平滑转换自回归模型AI 解读

Abstract We introduce a multi‐level smooth transition model for a panel of time series, which can be used to examine the presence of common nonlinear business cycle features across many variables. The model is positioned in between a fully pooled model, which imposes such common features, and a fully heterogeneous model, which allows for unrestricted nonlinearity. We introduce a second‐stage model linking the parameters that determine the timing of the switches between…

股票收益的非平稳性AI 解读

The paper outlines a methodology for analyzing daily stock returns that relinquishes the assumption of global stationarity. Giving up this common working hypothesis reflects our belief that fundamental features of the financial markets are continuously and significantly changing. Our approach approximates the nonstationary data locally by stationary models. The methodology is applied to the S&P 500 series of returns covering a period of over seventy years of market activity.…

允许结构突变的面板平稳性检验：人均GDP应用AI 解读

This paper proposes a test statistic for the null hypothesis of panel stationarity that allows for the presence of multiple structural breaks. Two different specifications are considered depending on the structural breaks affecting the individual effects and/or the time trend. The model is flexible enough to allow the number of breaks and their position to differ across individuals. The test is shown to have a standard normal limit distribution with a good finite sample…

汇率与马尔可夫状态转换动态AI 解读

This article presents a systematic and extensive empirical study on the presence of Markov switching dynamics in three dollar-based exchange rates. A Monte Carlo approach is adopted to circumvent the statistical inference problem inherent to the test of regime-switching behavior. Two data frequencies, two sample periods, and various specifications are considered. Quarterly data yield inconclusive evidence; the test rejects neither random walk nor Markov switching. Monthly…

水平跃迁与经济时间序列中的长记忆假象AI 解读

When applied to time series processes containing occasional level shifts, the log-periodogram (GPH) estimator often erroneously finds long memory. For a stationary short-memory process with a slowly varying level, I show that the GPH estimator is substantially biased, and derive an approximation to this bias. The asymptotic bias lies on the (0, 1) interval, and its exact value depends on the ratio of the expected number of level shifts to a user-defined bandwidth parameter.…

马尔可夫状态切换向量误差修正模型中的货币政策AI 解读

Monetary policy vector autoregressions (VARs) typically presume stability of the long-run outcomes. We introduce the possibility of switches in the long-run equilibrium in a cointegrated VAR by allowing both the covariance matrix and weighting matrix in the error-correction term to switch. We find that monetary policy alternates between sustaining long-run growth and disinflationary regimes. Allowing state changes can also help explain the price puzzle and justify the use of…

马尔可夫切换因果性与货币—产出关系AI 解读

Abstract The causal link between monetary variables and output is one of the most studied issues in macroeconomics. One puzzle from this literature is that the results of causality tests appear to be sensitive with respect to the sample period that one considers. As a way of overcoming this difficulty, we propose a method for analysing Granger causality which is based on a vector autoregressive model with time‐varying parameters. We model parameter time‐variation so as to…

含时变成分的结构学习：追踪金融时间序列的横截面结构AI 解读

Summary When modelling multivariate financial data, the problem of structural learning is compounded by the fact that the covariance structure changes with time. Previous work has focused on modelling those changes by using multivariate stochastic volatility models. We present an alternative to these models that focuses instead on the latent graphical structure that is related to the precision matrix. We develop a graphical model for sequences of Gaussian random vectors when…

非预见估计在序贯分析与变点检测中的应用AI 解读

Suppose a process yields independent observations whose distributions belong to a family parameterized by θ∈Θ. When the process is in control, the observations are i.i.d. with a known parameter value θ0. When the process is out of control, the parameter changes. We apply an idea of Robbins and Siegmund [Proc. Sixth Berkeley Symp. Math. Statist. Probab. 4 (1972) 37–41] to construct a class of sequential tests and detection schemes whereby the unknown post-change parameters…

个体效应随时间参数化变化的面板数据模型估计AI 解读

This paper is an extension of Ahn, Lee and Schmidt (2001) to allow a parametric function for time-varying coefficients on the individual effects. It is shown that the main results of Ahn, Lee and Schmidt (2001) hold for our model too. Least squares is consistent, given white noise errors, but less efficient than a GMM estimator. An application of the GMM estimators to the measurement of cost efficiency of Spanish banks is also included. The empirical study shows the…

多元非平稳时间序列的 SLEX 分析AI 解读

We develop a procedure for analyzing multivariate nonstationary time series using the SLEX library (smooth localized complex exponentials), which is a collection of bases, each basis consisting of waveforms that are orthogonal and time-localized versions of the Fourier complex exponentials. Under the SLEX framework, we build a family of multivariate models that can explicitly characterize the time-varying spectral and coherence properties. Every model has a spectral…

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