Pierre Perron

Lag Length Selection and the Construction of Unit Root Tests with Good Size and Power

It is widely known that when there are negative moving average errors, a high order augmented autoregression is necessary for unit root tests to have good size, but that information criteria such as the AIC and BIC tend to select a truncation lag that is very small. Furthermore, size distortions increase with the number of deterministic terms in the regression. We trace these problems to the fact that information criteria omit important biases induced by a low order augmented autoregression. We consider a class of Modified Information Criteria (MIC) which account for the fact that the bias in the sum of the autoregressive coefficients is highly dependent on the lag order k. Using a local asymptotic framework in which the root of an MA(1) process is local to -1, we show that the MIC allows for added dependence between k and the number of deterministic terms in the regression. Most importantly, the k selected by the recommended MAIC is such that both its level and rate of increase with the sample size are desirable for unit root tests in the local asymptotic framework, whereas the AIC, MBIC and especially the BIC are less attractive in at least one dimension. In monte-carlo experiments, the MAIC is found to yield huge size improvements to the DF(GLS) and the feasible point optimal P(t) test developed in Elliot, Rothenberg and Stock (1996). We also extend the M tests developed in Perron and Ng (1996) to allow for GLS detrending of the data. The M(GLS) tests are shown to have power functions that lie very close to the power envelope. In addition, we recommend using GLS detrended data to estimate the required autoregressive spectral density at frequency zero. This provides more efficient estimates on the one hand, and ensures that the estimate of the spectral density is invariant to the parameters of the deterministic trend function, a property not respected by the estimation procedure currently employed by several studies. The MAIC along with GLS detrended data yield a set of Mbar(GLS) tests with desirable size and power properties.

Further Evidence on Breaking Trend Functions in Macroeconomic Variables

Abstract This study first reexamines the findings of Perron (1989) regarding the claim that most macroeconomic time series are best construed as stationary fluctuations around a deterministic trend function if allowance is made for the possibility of a shift in the intercept of the trend function in 1929 (a crash) and a shift in slope in 1973 (a slowdown in growth). Unlike that previous study, the date of possible change is not fixed a priori but is considered as unknown. We consider various methods to select the break points and the asymptotic and finite sample distributions of the corresponding statistics. A detailed discussion about the choice of the truncation lag parameter in the autoregression and of its effect on the critical values is also included. Most of the rejections reported in Perron (1989) are confirmed using this approach. Secondly, this paper investigates an international data set of post-war quarterly real GNP (or GDP) series for the G-7 countries. Our results are compared and contrasted to those of Banerjee et al. (1992) and Zivot and Andrews (1992). In contrast to the theoretical results contained in these papers, we derive the limiting distribution of the sequential test without trimming.

Unit Root Tests in ARMA Models with Data-Dependent Methods for the Selection of the Truncation Lag

Abstract We analyze the choice of the truncation lag in the context of the Said-Dickey test for the presence of a unit root in a general autoregressive moving average model. It is shown that a deterministic relationship between the truncation lag and the sample size is dominated by data-dependent rules that take sample information into account. In particular, we study data-dependent rules that are not constrained to satisfy the lower bound condition imposed by Said-Dickey. Akaikes information criterion falls into this category. The analytical properties of the truncation lag selected according to a class of information criteria are compared to those based on sequential testing for the significance of coefficients on additional lags. The asymptotic properties of the unit root test under various methods for selecting the truncation lag are analyzed, and simulations are used to show their distinctive behavior in finite samples. Our results favor methods based on sequential tests over those based on informat...

Nonstationarity and Level Shifts With an Application to Purchasing Power Parity

This study considers testing for a unit root in a time series characterized by a structural change in its mean. The analysis is in the spirit of Perron (1990a), who showed that the existence of such a shift in a stationary time series biases the usual tests for a unit root toward nonrejection. The approach is, however, different given that we suppose the date of the change to be unknown. The statistic of interest is then the minimal t statistic over all possible breakpoints in regressions similar to those proposed by Perron (1990a). Other related statistics are also discussed. We derive and tabulate the asymptotic distributions of interest. Most of the emphasis, however, is given to the tabulation of finite-sample critical values using simulation experiments. Particular attention is given to the effect, on the finite-sample critical values, of various procedures to select the appropriate order of the estimated autoregressions. We apply the tests to analyze the issue of purchasing power parity between the ...

Testing For A Unit Root In A Time Series With A Changing Mean

This study considers testing for a unit root in a time series characterized by a structural change in its mean level. My approach follows the “intervention analysis” of Box and Tiao (1975) in the sense that I consider the change as being exogenous and as occurring at a known date. Standard unit-root tests are shown to be biased toward nonrejection of the hypothesis of a unit root when the full sample is used. Since tests using split sample regressions usually have low power, I design test statistics that allow the presence of a change in the mean of the series under both the null and alternative hypotheses. The limiting distribution of the statistics is derived and tabulated under the null hypothesis of a unit root. My analysis is illustrated by considering the behavior of various univariate time series for which the unit-root hypothesis has been advanced in the literature. This study complements that of Perron (1989), which considered time series with trends.

Trends and random walks in macroeconomic time series: Further evidence from a new approach

This Paper Presents a Summary of Recent Work on a New Methodology to Test for the Presence of a Unit Root in Univariate Time Series Models. the Stochastic Framework Is Quite General. While the Dickey-Fuller Approach Accounts for the Autocorrelation of the First-Differences of a Serie in a Paremetric Fashion by Estimating Additional Nuisance Parameters, This New Approach Deals with This Phenomenon in a Nonparametric Way. We Apply These New Tests to Reassess Recent Findings on the Behavior of Common Macroeconomic Time Series, Including the Various Series Studies by Nelson and Plosser (1982).

Testing the Random Walk Hypothesis: Power Versus Frequency of Observation

Power functions of tests of the random walk hypothesis versus stationary first order autoregressive alternatives are tabulated for samples of fixed span but various frequencies of observation.

Useful Modifications to some Unit Root Tests with Dependent Errors and their Local Asymptotic Properties

Many unit root tests have distorted sizes when the root of the error process is close to the unit circle. This paper analyses the properties of the Phillips-Perron tests and some of their variants in the problematic parameter space. We use local asymptotic analyses to explain why the Phillips-Perron tests suffer from severe size distortions regardless of the choice of the spectral density estimator but that the modified statistics show dramatic improvements in size when used in conjunction with a particular formulation an autoregressive spectral density estimator. We explain why kernel based spectral density estimators aggravate the size problem in the Phillips-Perron tests and yield no size improvement to the modified statistics. The local asymptotic power of the modified statistics are also evaluated. These modified statistics are recommended as being useful in empirical work since they are free of the size problems which have plagued many unit root tests, and they retain respectable power.

Critical values for multiple structural change tests

to enable proper empirical applications. We provide response surface regressions valid for a wide range of parameters. Copyright Royal Economic Society, 2003

Trend, Unit Root and Structural Change in Macroeconomic Time Series

The unit root hypothesis has attracted a considerable amount of work in both the economics and statistics literature. Indeed, the view that most economic time series are characterized by a stochastic rather than deterministic nonstationarity has become prevalent. The seminal study of Nelson and Plosser (1982) which found that most macroeconomic variables have a univariate time series structure with a unit root has catalysed a burgeoning research program with both empirical and theoretical dimensions.