Robust Stationarity Tests in Seasonal Time Series Processes

This article builds on the existing literature on (stationarity) tests of the null hypothesis of deterministic seasonality in a univariate time series process against the alternative of unit root behavior at some or all of the zero and seasonal frequencies. This article considers the case where, in testing for unit roots at some proper subset of the zero and seasonal frequencies, there are unattended unit roots among the remaining frequencies. Monte Carlo results are presented that demonstrate that in this case, the stationarity tests tend to distort below nominal size under the null and display an associated (often very large) loss of power under the alternative. A modification to the existing tests, based on data prefiltering, that eliminates the problem asymptotically is suggested. Monte Carlo evidence suggests that this procedure works well in practice, even at relatively small sample sizes. Applications of the robustified statistics to various seasonally unadjusted time series measures of U.K. consumers' expenditure are considered; these yield considerably more evidence of seasonal unit roots than do the existing stationarity tests.     

F-tests for seasonal differencing with a break-point

The classification between stochastic trend stationarity and deterministic broken trend stationarity is important because incorrect inferences can follow if a stationary series with a broken trend is incorrectly classified as integrated. In this paper, we consider joint tests for regular and seasonal unit roots null hypothesis against broken trend stationarity alternatives where the location of the break is known or unknown. Based on the F-test proposed by Hasza and Fuller (1982, Ann. Statist. 10, 1209–1216), we develop testing procedures for distinguishing these two types of process. The asymptotic distributions of test statistics are derived as functions of Wiener processes. A response surface regression analysis directed to relating the finite sample distributions and the breaking position is studied. Simulation experiments suggest that the power of the test is reasonable. The testing procedure is illustrated by the Canadian consumer price index series.     

The Performance of Panel Unit Root and Stationarity Tests: Results from a Large Scale Simulation Study

This paper presents results on the size and power of first generation panel unit root and stationarity tests obtained from a large scale simulation study. The tests developed in the following papers are included: Levin et al. (2002), Harris and Tzavalis (1999), Breitung (2000), Im et al. (1997, 2003), Maddala and Wu (1999), Hadri (2000), and Hadri and Larsson (2005). Our simulation set-up is designed to address inter alia the following issues. First, we assess the performance as a function of the time and the cross-section dimensions. Second, we analyze the impact of serial correlation introduced by positive MA roots, known to have detrimental impact on time series unit root tests, on the performance. Third, we investigate the power of the panel unit root tests (and the size of the stationarity tests) for a variety of first order autoregressive coefficients. Fourth, we consider both of the two usual specifications of deterministic variables in the unit root literature.     

A Short-Cut Derivation for the Solution of Autoregressive Models from Sharp Algebraic Arguments

This article uses algebraic arguments to cast light on the solution of vector autoregressive models in the presence of unit roots. First, the linear case and then the multi-lag specification are investigated. Clear-cut representations of the model solutions are obtained, closed-form expressions of the coefficient matrices are provided, and integration features and cointegration mechanisms for stationarity recovery are elucidated.     

TESTS FOR SEASONAL DIFFERENCING WITH AN UNKNOWN BREAK-POINT

Some Lagrange multiplier tests for seasonal differencing are proposed; their main objective is to avoid over-differencing due to structural change. The null hypothesis is either the presence of both regular and seasonal unit roots or the presence of a seasonal unit root. Alternative hypotheses allow for stationarity around a possible structural change where the break-point is unknown. The location of the structural change is estimated using the proposed procedures, the asymptotic distribution of the test statistics under the null hypothesis is derived and some useful percentiles are tabulated. An illustrative example based on the Canadian Consumer Price Index is presented.     

A Locally Optimal Seasonal Unit-Root Test

This article proposes a locally best invariant test of the null hypothesis of seasonal stationarity against the alternative of seasonal unit roots at all or individual seasonal frequencies. An asymptotic distribution theory is derived and the finite-sample properties of the test are examined in a Monte Carlo simulation. My test is also compared with the Canova and Hansen test. The proposed test is superior to the Canova and Hansen test in terms of both size and power.     

Bayesian analysis of panel data using an MTAR model

Bayesian analysis of panel data using a class of momentum threshold autoregressive (MTAR) models is considered. Posterior estimation of parameters of the MTAR models is done by using a simple Markov Chain Monte Carlo (MCMC) algorithm. Selection of appropriate differenced variables, test for asymmetry and unit roots are recast as model selections and a simple way of computing posterior probabilities of the candidate models is proposed. The proposed method is applied to the yearly unemployment rates of 51 US states and the results show strong evidence of stationarity and asymmetry.     

Confidence Sets for Cointegrating Coefficients Based on Stationarity Tests

Standard methods for inference in cointegrating systems require all the variables to have exact unit roots and are not at all robust even to slight violations of this condition. In this article, I consider an alternative approach to inference in a cointegrating system. This involves testing the hypothesis that a cointegrating vector takes on a specified value by testing for the stationarity of the associated residual. Confidence sets for the cointegrating vector can be constructed by exploiting the equivalence between tests and confidence sets. This method has the advantage that it remains valid even if the regressors have roots that are not exactly equal to unity.     

On the nearly nonstationary seasonal time series

A time series is said to be nearly nonstationary if some of its characteristic roots are close to the unit circle. For a seasonal time series, such a notion of near-nonstationarity is studied in a double-array setting. This approach not only furnishes a natural transition between stationarity and nonstationarity, but also unifies the corresponding asymptotic theories in a seasonal-time-series context. The general theory is expressed in terms of functionals of independent diffusion processes. The asymptotic results have applications to estimation and testing in a nearly nonstationary situation and serve as a useful alternative to the common practice of seasonal adjustment.     

Testing stationarity and trend stationarity against the unit root hypothesis

In this paper we propose a family of relativel simple nonparametrics tests for a unit root in a univariate time series. Almost all the tests proposed in the literature test the unit root hypothesis against the alternative that the time series involved is stationarity or trend stationary. In this paper we take the (trend) stationarity hypothesis as the null and the unit root hypothesis as the alternative. The order differnce with most of the tests proposed in the literature is that in all four cases the asymptotic null distribution is of a well-known type, namely standard Cauchy. In the first instance we propose four Cauchy tests of the stationarity hypothesis against the unit root hypothesis. Under H1 these four test statistics involved, divided by the sample size n, converge weakly to a non-central Cauchy distribution, to one, and to the product of two normal variates, respectively. Hence, the absolute values of these test statistics converge in probability to infinity 9at order n). The tests involved are therefore consistent against the unit root hypothesis. Moreover, the small sample performance of these test are compared by Monte Carlo simulations. Furthermore, we propose two additional Cauchy tests of the trend stationarity hypothesis against the alternative of a unit root with drift.     

Unit root econometrics and economic nonlinearities

This paper concentrates on some shortcomings of contemporary unit root econometric methodology (testing for cointegration, common roots and stationarity) where the dynamics of an economy are described by a nonlinear process. It is shown that, in such circumstances, traditionally applied unit root econometrics may not lead to interpretable or statistically significant results. Two cases of such nonlinearities are discussed: (i) a stochastically nonlinear data generating process and (ii) a time-varying parameters cointegrating relation, typical of an economic reform process. It is shown that case (i) consists of a wide family of economic processes and in most such cases the results of standard unit root tests are not directly interpretable. Case (ii) does not result in a (conventionally understood) error-correction representation of a cointegrated process. Some Monte Carlo experiments evaluate the validity of cointegration tests in situations where there is a change in the cointegration parameter and from cointegration regime to noncointegration and vice versa. A simple method of estimation through simulation is proposed and its finite-sample properties examined.     

我国费雪效应的非参数检验

本文基于我国1990:01—2007:04期间的名义利率与通货膨胀率月度数据非线性变化的特征,应用非参数单位根和非参数协整理论检验我国是否存在费雪效应, 进而应用非参数局部线性变窗宽估计计算我国的费雪系数。由此产生的结论为：第一,非参数单位根检验发现我国名义利率与通货膨胀率都是非平稳的单位根过程;第二,非参数协整检验的结论为, 我国名义利率与通胀变化率之间存在长期的非线性协整关系, 这一结论表明我国至少存在弱的费雪效应;第三,非参数局部线性变窗宽估计计算的费雪效应(系数)的均值为0.4055,这一结果进一步支持我国存在弱的费雪效应,其隐含的意义为,当前加息对稳定通胀将产生正面效应,进一步, 如适时适度的调整利率, 很可能抑制当前较高的CPI向高通胀的转化。     

Unit roots and double smooth transitions

Techniques for testing the null hypothesis of difference stationarity against stationarity around some deterministic function have received much attention. In particular, unit root tests where the alternative is stationarity around a smooth transition in a linear trend have recently been proposed to permit the possibility of non-instantaneous structural change. In this paper we develop tests extending such an approach in order to admit more than one structural change. The analysis is motivated by time series that appear to undergo two smooth transitions in the linear trend, and the application of the new tests to two such series (average global temperature and US consumer prices) highlights the benefits of this double transition extension.     

Structural changes and unit roots in non-stationary time series

The problem of testing hypotheses of a unit root and a structural change in one-dimensional time series is considered. A non-parametric two-step method for solution of the problem is proposed. The method is based upon the modified Kolmogorov-Smirnov statistic. At the first step of this method the hypothesis of stationarity of an obtained sample is tested against a unified alternative of a statistical non-stationarity of a time series (a unit root or a structural change). At the second step of the proposed method, in case of rejecting the stationarity hypothesis at the first step, the hypothesis of an unknown structural change is tested against the alternative of a unit root. We prove that probabilities of errors (false classification of hypotheses) of the proposed method converge to zero as the sample size tends to infinity.     

Testing for stationarity in large panels with cross-dependence,and US evidence on unit labor cost

A new stationarity test for heterogeneous panel data with large cross-sectional dimension is developed and used to examine a panel with growth rates of unit labor cost in the USA. The test allows for strong cross-unit dependence in the form of unbounded long-run correlation matrices, for which a simple parameterization is proposed. A KPSS-type distribution results asymptotically if letting T→∞ be followed by N→∞. Some evidence against stationarity (short memory) is found for the examined series.     

Non-Parametric Testing of Conditional Variance Functions in Time Series

This paper proposes a non‐parametric test for examining hypotheses about variance functions under stationarity and ergodicity conditions. Special cases of nonlinear time series models are studied, and it is found that under mild conditions the test is consistent. Its power is examined in a simulation study.     

Recursive and Sequential Tests of the Unit-Root and Trend-Break Hypotheses: Theory and International Evidence

This article investigates the possibility, raised by Perron and by Rappoport and Reichlin, that aggregate economic time series can be characterized as being stationary around broken trend lines. Unlike those authors, we treat the break date as unknown a priori. Asymptotic distributions are developed for recursive, rolling, and sequential tests for unit roots and/or changing coefficients in time series regressions. The recursive and rolling tests are based on changing subsamples of the data. The sequential statistics are computed using the full data set and a sequence of regressors indexed by a “break” date. When applied to data on real postwar output from seven Organization for Economic Cooperation and Development countries, these techniques fail to reject the unit-root hypothesis for five countries (including the United States) but suggest stationarity around a shifted trend for Japan.     

A BINAR(1) time-series model with cross-correlated COM–Poisson innovations

This article proposes a bivariate integer-valued autoregressive time-series model of order 1 (BINAR(1) with COM–Poisson marginals to analyze a pair of non stationary time series of counts. The interrelation between the series is induced by the correlated innovations, while the non stationarity is captured through a common set of time-dependent covariates that influence the count responses. The regression and dependence effects are estimated using generalized quasi-likelihood (GQL) approach. Simulation experiments are performed to assess the performance of the estimation algorithms. The proposed BINAR(1) process is applied to analyze a real-life series of day and night accidents in Mauritius.     

On the Performance of Popular Unit-Root Tests Against Various Nonlinear Dynamic Models: A Simulation Study

ABSTRACT

There is a widespread perception that standard unit-root tests have poor discriminatory power when they are applied to time series with nonlinear dynamics. Via Monte Carlo simulations this study re-examines the finite sample properties of selected univariate tests for unit-root and stationarity under a broad class of nonlinear dynamic models. Our simulation experiments produce a couple of interesting findings. First, performance of tests is driven by the degree of underlying persistence rather than the nonlinear dynamics per se. Tests under study exhibit reasonable performance for nonlinear models with mild persistence, while the accuracy of inference deteriorates substantially when the models are highly persistent regardless of the linearity. Second, when it comes to deciding which one to identify first between linearity and stationarity, our results suggest to conduct linearity test first to enhance the reliability of test inference.     

A kernel smoothing method of adjusting for unit non‐response in sample surveys

Non‐response is a common problem in survey sampling and this phenomenon can only be ignored at the risk of invalidating inferences from a survey. In order to adjust for unit non‐response, the authors propose a weighting method in which kernel regression is used to estimate the response probabilities. They show that the adjusted estimator is consistent and they derive its asymptotic distribution. They also suggest a means of estimating its variance through a replication‐based technique. Furthermore, a Monte Carlo study allows them to illustrate the properties of the non‐response adjustment and its variance estimator.