Bounds,Breaks and Unit Root Tests

The paper addresses the unit root testing when the range of the time series is limited and considering the presence of multiple structural breaks. The structural breaks can affect the level and/or the boundaries of the time series. The paper proposes five unit root test statistics, whose limiting distribution is shown to depend on the number and position of the structural breaks. The performance of the statistics is investigated by means of Monte Carlo simulations.     

Seasonal Unit Root Tests Under Structural Breaks*

Abstract. In this paper, several seasonal unit root tests are analysed in the context of structural breaks at known time and a new break corrected test is suggested. We show that the widely used HEGY test, as well as an LM variant thereof, are asymptotically robust to seasonal mean shifts of finite magnitude. In finite samples, however, experiments reveal that such tests suffer from severe size distortions and power reductions when breaks are present. Hence, a new break corrected LM test is proposed to overcome this problem. Importantly, the correction for seasonal mean shifts bears no consequence on the limiting distributions, thereby maintaining the legitimacy of canonical critical values. Moreover, although this test assumes a breakpoint a priori, it is robust in terms of misspecification of the time of the break. This asymptotic property is well reproduced in finite samples. Based on a Monte‐Carlo study, our new test is compared with other procedures suggested in the literature and shown to hold superior finite sample properties.     

Testing Monthly Seasonal Unit Roots With Monthly and Quarterly Information

Abstract. This paper proposes a method for testing seasonal unit roots that combines monthly and quarterly Hylleberg, Engle, Granger and Yoo (HEGY) tests. The new approach is more powerful than the method that does not use quarterly information, i.e. the monthly HEGY test. An empirical illustration of the proposed approach is given for monthly US Industrial Production.     

Long-Memory Errors in Time Series Regressions with a Unit Root

This paper is concerned with estimation and inference in univariate time series regression with a unit root when the error sequence exhibits long-range temporal dependence. We consider generating mechanisms for the unit root process which include models with or without a drift term and we study the limit behavior of least squares statistics in regression models without drift and trend, with drift but no time trend, and with drift and time trend. We derive the limit distribution and rate of convergence of the ordinary least squares (OLS) estimator of the unit root, the intercept and the time trend in the three regression models and for the two different data-generating processes. The limiting distributions for the OLS estimator differ from those obtained under the hypothesis of weakly dependent errors not only in terms of the limiting process involved but also in terms of functional form. Further, we characterize the asymptotic behavior of both the t statistics for testing the unit root hypothesis and the t statistic for the intercept and time trend coefficients. We find that t ratios either diverge to infinity or collapse to zero. The limiting behavior of Phillips's Z _α and Z _t semiparametric corrections is also analyzed and found to be similar to that of standard Dickey– Fuller tests. Our results indicate that misspecification of the temporal dependence features of the error sequence produces major effects on the asymptotic distribution of estimators and t ratios and suggest that alternative approaches might be more suited to testing for a unit root in time series regression.     

Locally Optimal Tests Against Unit Roots in Seasonal Time Series Processes

This paper builds on the existing literature on tests of the null hypothesis of deterministic seasonality in a univariate time-series process. Under the assumption of independent Gaussian errors, we derive the class of locally weighted mean most powerful invariant tests against unit roots at the zero and/or seasonal frequencies in a seasonally observed process. Representations for the limiting distributions of the proposed test statistics under sequences of local alternatives are derived, and the relationship with tests for corresponding moving average unit roots is explored. We also propose nonparametric modifications of these test statistics designed to have limit distributions which are free of nuisance parameters under weaker conditions on the errors. Our tests are shown to contain existing stationarity tests as special cases and to extend these tests in a number of useful directions.     

Likelihood Ratio Tests for Seasonal Unit Roots

This paper proposes regression-based likelihood ratio or F tests for the seasonal unit root hypothesis which fully incorporate the implicit restrictions on the parameters associated with the deterministics. These statistics are similar both exactly and asymptotically with respect to initial values and seasonal drift parameters. The limiting representations of the statistics are presented for a general seasonal aspect. These limiting representations allow those for other scenarios concerning the deterministics to be simply obtained and provide an explanation for the similarity between critical values in apparently quite different cases of interest. We re-examine the seasonal unit root properties of the logarithm of monthly seasonally unadjusted real industrial production in Canada.     

Seasonal Moving-average Unit Root Tests in the Presence of a Linear Trend

To distinguish stochastic from deterministic seasonality, test procedures are developed for a unit root in the integrated seasonal moving-average (SMA) model when an underlying deterministic trend is present. Locally best invariant unbiase (LBIU) and point optimal invariant tests are considered. Their asymptotic distributions are developed and are found to differ from those for the no-linear-trend case. The limiting distribution of the LBIU statistic is expressed as a functional of Brownian motions. The procedures are extended to more general seasonal autoregressive moving-average (ARMA) models, and to the inclusion of exogenous regressors. Finite-sample distributions are also derived for the SMA(1) model. Simulations suggest that these distributions provide accurate approximations for more general ARMA models. A numerical example is included to illustrate the tests.     

On the use of Sub‐sample Unit Root Tests to Detect Changes in Persistence

Abstract. We investigate the behaviour of rolling and recursive augmented Dickey–Fuller (ADF) tests against processes which display changes in persistence. We show that the power of the tests depend crucially on the window width and warm up parameter for the rolling and recursive procedures respectively, on whether forward or reverse recursive sequences of tests are computed, and on the persistence change process generating the data. To ameliorate these dependencies we extend the available critical values for these tests, and propose a number of new sub‐sample unit root tests for which finite sample and asymptotic critical values are also provided. An empirical illustration on OECD real output data is also provided.     

Testing the Null Hypothesis of Stationarity Against an Autoregressive Unit Root Alternative

We propose a new test for the null hypothesis that a time series is stationary around a deterministic trend. The test is valid under general conditions on stationarity. Asymptotic distributions of the test statistic are derived under both the null and the alternative hypothesis of a unit root. It is shown that the limiting distribution has the classical Kolmogoroff– Smirnoff form. Critical values for the null distribution are calculated. Consistency of the tests is proved. The tests provide a useful complement to the conventional unit root tests.     

recursive Mean Adjustment for Unit Root Tests

For unit root tests, we propose a new mean adjustment scheme, called recursive mean adjustment. For adjusting the mean of an observation at a time t , instead of the global sample mean, we use the recursive sample mean which is the sample mean of the observations up to the time t . The approach is simple and can be applied to a wide class of unit root tests. The recursive mean adjustment gives us tests with substantially higher powers compared with the tests based on the ordinary mean adjustment.     

Bayesian Unit Root Test in Nonnormal AR(1) Model

In this paper, we approximate the distribution of disturbances by the Edgeworth series distribution and propose a Bayesian analysis in a nonnormal AR(1) model. We derive the posterior distribution of the autocorrelation and the posterior odds ratio for unit roots hypothesis in the AR(1) model when the first four cumulants of the Edgeworth series distribution are finite and the higher order cumulants are negligible. We also apply the posterior analysis to eight real exchange rates and investigate whether these exchange rates behave like a random walk or not.     

The Limiting Density of Unit Root Test Statistics: A Unifying Technique

In this note we introduce a simple principle to derive a constructive expression for the density of the limiting distribution, under the null hypothesis, of unit root statistics for an AR(1)-process in a variety of situations. We consider the case of unknown mean and reconsider the well-known situation where the mean is zero. For long-range dependent errors we indicate how the principle might apply again. We also show that in principle the method also works for a near unit root case. Weak convergence and subsequent Karhunen-Loeve expansion of the weak limit of the partial sum process of the errors plays an important role, along with the evaluation of a certain normal type integral with complex mean and variance. For independent and long range dependent errors this weak limit is ordinary and fractional Brownian motion respectively.
AMS 1991 subject classification. Primary 62M10; secondary 62E20.     

Time‐Transformed Unit Root Tests for Models with Non‐Stationary Volatility

Abstract. Conventional unit root tests are known to be unreliable in the presence of permanent volatility shifts. In this paper, we propose a new approach to unit root testing which is valid in the presence of a quite general class of permanent variance changes which includes single and multiple (abrupt and smooth transition) volatility change processes as special cases. The new tests are based on a time transformation of the series of interest which automatically corrects their form for the presence of non‐stationary volatility without the need to specify any parametric model for the volatility process. Despite their generality, the new tests perform well even in small samples. We also propose a class of tests for the null hypothesis of stationary volatility in (near‐) integrated time‐series processes.     

Bootstrap Unit‐Root Tests: Comparison and Extensions

Abstract. In this article, we study and compare the properties of several bootstrap unit‐root tests recently proposed in the literature. The tests are Dickey–Fuller (DF) or Augmented DF, based either on residuals from an autoregression and the use of the block bootstrap or on first‐differenced data and the use of the stationary bootstrap or sieve bootstrap. We extend the analysis by interchanging the data transformations (differences vs. residuals), the types of bootstrap and the presence or absence of a correction for autocorrelation in the tests. We show that two sieve bootstrap tests based on residuals remain asymptotically valid. In contrast to the literature which focuses on a comparison of the bootstrap tests with an asymptotic test, we compare the bootstrap tests among themselves using response surfaces for their size and power in a simulation study. This study leads to the following conclusions: (i) augmented DF tests are always preferred to standard DF tests; (ii) the sieve bootstrap performs better than the block bootstrap; (iii) difference‐based tests appear to have slightly better size properties, but residual‐based tests appear more powerful.     

On the Robustness of Unit Root Tests in the Presence of Double Unit Roots

We examine some of the consequences on commonly used unit root tests when the underlying series is integrated of order two rather than of order one. It turns out that standard augmented Dickey–Fuller type of tests for a single unit root have excessive density in the explosive region of the distribution. The lower (stationary) tail, however, will be virtually unaffected in the presence of double unit roots. On the other hand, the Phillips–Perron class of semi-parametric tests is shown to diverge to plus infinity asymptotically and thus favouring the explosive alternative. Numerical simulations are used to demonstrate the analytical results and some of the implications in finite samples.     

Bootstrap Tests for an Autoregressive Unit Root in the Presence of Weakly Dependent Errors

This paper examines bootstrap tests of the null hypothesis of an autoregressive unit root in models that may include a linear rend and/or an intercept and which are driven by innovations that belong to the class of stationary and invertible linear processes. Our approach makes use of a sieve bootstrap procedure based on residual resampling from autoregressive approximations, the order of which increases with the sample size at a suitable rate. We show that the sieve bootstrap provides asymptotically valid tests of the unit-root hypothesis and demonstrate the small-sample effectiveness of the method by means of simulation.     

Unit Root Tests Based on Unconditional Maximum Likelihood Estimation for the Autoregressive Moving Average

Unconditional maximum likelihood estimation is considered for an autoregressive moving average that may contain an autoregressive unit root. The limiting distribution of the normalized maximum likelihood estimator of the unit root is shown to be the same as that of the estimator for the first-order autoregressive process. A likelihood ratio test based on unconditional maximum likelihood estimation is proposed. In a Monte Carlo study for the autoregressive moving-average model of order (1, 1), the new test is shown to have better size and power than those of several other tests.     

Testing for a Unit Root in Noncausal Autoregressive Models

This work develops maximum likelihood‐based unit root tests in the noncausal autoregressive (NCAR) model with a non‐Gaussian error term formulated by Lanne and Saikkonen (2011, Journal of Time Series Econometrics 3, Issue 3, Article 2). Finite‐sample properties of the tests are examined via Monte Carlo simulations. The results show that the size properties of the tests are satisfactory and that clear power gains against stationary NCAR alternatives can be achieved in comparison with available alternative tests. In an empirical application to a Finnish interest rate series, evidence in favour of an NCAR model with leptokurtic errors is found.     

Testing for a Unit Root in Autoregressive Moving-average Models with Missing Data

Testing for a single autoregressive unit root in an autoregressive moving-average (ARMA) model is considered in the case when data contain missing values. The proposed test statistics are based on an ordinary least squares type estimator of the unit root parameter which is a simple approximation of the one-step Newton–Raphson estimator. The limiting distributions of the test statistics are the same as those of the regression statistics in AR(1) models tabulated by Dickey and Fuller (Distribution of the estimators for autoregressive time series with a unit root. J. Am. Stat. Assoc . 74 (1979), 427–31) for the complete data situation. The tests accommodate models with a fitted intercept and a fitted time trend.     

Heteroskedasticity‐Robust Unit Root Testing for Trending Panels

Time‐varying volatility and linear trends are common features of several macroeconomic time series. Recent articles have proposed panel unit root tests (PURTs) that are pivotal in the presence of volatility shifts, excluding linear trends, however. This article proposes a new PURT that works well for data that is both heteroskedastic and trending. Under the null hypothesis, the test statistic has a limiting Gaussian distribution. We derive the local asymptotic power to underpin the consistency of the test statistic. Simulation results reveal that the test performs well in small samples. As an empirical illustration, we examine the stationarity of energy use per capita in OECD economies. While the series are in general difference stationary, they could also be considered as trend stationary for specific time spans.