Effects of Applying Linear and Nonlinear Filters on Tests for Unit Roots with Additive Outliers

Conventional univariate Dickey-Fuller tests tend to produce spurious stationarity when there exist additive outlying observations in the time series. Correct critical values are usually obtained by adding dummy variables to the Dickey-Fuller regression. This is a nice theoretical result but not attractive from the empirical point of view since almost any result can be obtained just by a convenient selection of dummy variables. In this paper we suggest a robust procedure based on running Dickey-Fuller tests on the trend component instead of the original series. We provide both finite-sample and large-sample justifications. Practical implementation is illustrated through an empirical example based on the US/Finland real exchange rate series.Additive outliers, Dickey-Fuller test, Linear and nonlinear filtering, Bootstrap

On the finite-sample power of modified Dickey-Fuller tests: The role of the initial condition

The relationship between the initial condition of time series data and the power of the Dickey-Fuller (1979) test and a number of modified Dickey-Fuller tests is examined. The results obtained extend the asymptotic analysis of Muller and Elliott (2003) by both focussing upon finite-sample power and examining previously unconsidered modified tests. It is shown that deviation of the initial condition from the underlying deterministic component of a time series increases the finite-sample power of the original Dickey-Fuller test, but removes the potential gains in power resulting from the use of modified tests. Interestingly, some variation in the properties of modified tests is noted. In addition to allowing evaluation of previous Monte Carlo studies of the finite-sample power of unit root tests, the results presented allow practitioners to select, and interpret the results of, alternative unit root tests in light of the initial condition of the data examined.Forward and reverse regressions

Tests For Unit Roots: A Monte Carlo Investigation

Recent work by Said and Dickey (1984 ,1985) , Phillips (1987), and Phillips and Perron(1988) examines tests for unit roots in the autoregressive part of mixed autoregressive-integrated-moving average (ARIHA) models (tests for stationarity). Monte Carlo experiments show that these unit root tests have different finite sample distributions than the unit root tests developed by Fuller(1976) and Dickey and Fuller (1979, l981) for autoregressive processes. In particular, the tests developed by Philllps (1987) and Phillips and Perron (1988) seem more sensitive to model misspeciflcation than the high order autoregressive approximation suggested by Said and Diekey(1984).

Bootstrap Unit Root Tests

We consider the bootstrap unit root tests based on autoregressive integrated models, with or without deterministic time trends. A general methodology is developed to approximate asymptotic distributions for the models driven by integrated time series, and used to obtain asymptotic expansions for the Dickey-Fuller unit root tests. The second-order terms in their expansions are of stochastic orders Op($n^{-1/4}$) and Op($n^{-1/2}$), and involve functionals of Brownian motions and normal random variates. The asymptotic expansions for the bootstrap tests are also derived and compared with those of the Dickey-Fuller tests. We show in particular that the usual nonparametric bootstrap offers asymptotic refinements for the Dickey-Fuller tests, i.e., it corrects their second-order errors. More precisely, it is shown that the critical values obtained by the bootstrap resampling are correct up to the second-order terms, and the errors in rejection probabilities are of order o($n^{-1/2}$) if the tests are based upon the bootstrap critical values. Through simulation, we investigate how effective is the bootstrap correction in small samples.

Bootstrap Unit Root Tests

We consider the bootstrap unit root tests based on finite order autoregressive integrated models driven by iid innovations, with or without deterministic time trends. A general methodology is developed to approximate asymptotic distributions for the models driven by integrated time series, and used to obtain asymptotic expansions for the Dickey-Fuller unit root tests. The second-order terms in their expansions are of stochastic orders Op(n-1/4) and Op(n-1/2), and involve functionals of Brownian motions and normal random variates. The asymptotic expansions for the bootstrap tests are also derived and compared with those of the Dickey-Fuller tests. We show in particular that the bootstrap offers asymptotic refinements for the Dickey-Fuller tests, i.e., it corrects their second-order errors. More precisely, it is shown that the critical values obtained by the bootstrap resampling are correct up to the second-order terms, and the errors in rejection probabilities are of order o(n-1/2) if the tests are based upon the bootstrap critical values. Through simulations, we investigate how effective is the bootstrap correction in small samples.

Covariate Augmented Dickey-Fuller Tests with R

This paper describes CADFtest, a R (R Development Core Team 2008) package for testing for the presence of a unit root in a time series using the Covariate Augmented Dickey-Fuller (CADF) test proposed in Hansen (1995). The procedures presented here are user friendly, allow fully automatic model specification, and allow computation of the asymptotic p-values of the test.unit root, stationary covariates, asymptotic p-values, R.

Detection of Functional Form Misspecification in Cointegrating Relations

A simple specification test based on fully modified residuals and the CUSUM test for cointegration of Xiao and Phillips (Journal of Econometrics, 2002) are considered as means of testing for functional form in long-run cointegrating relations. It is shown that both tests are consistent under functional form misspecification and lack of cointegration. A simulation experiment is carried out to assess the properties of the tests in infinite samples. The Dickey-Fuller test is also considered. The simulation results reveal that the first two tests per- form reasonably well. However, the Dickey-Fuller test performs poorly under functional form misspecification.

Deterministic Seasonality in Dickey-Fuller Tests: Should We Care?

This paper investigates the properties of Dickey-Fuller tests for seasonally unadjusted quarterly data when deterministic seasonality is present but it is neglected in the test regression. While for the random walk case the answer is straightforward, an extensive Monte Carlo study has to be performed for more realistic processes and testing strategies. The most important conclusion is that the common perception that deterministic seasonality has nothing to do with the long-run properties of the data is incorrect. Further numerical evidence on the shortcomings of the general-to-specific t-sig lag selection method is also presented.unit root; Dickey-Fuller tests; similar tests; seasonality; Monte Carlo

Unit root testing

The occurrence of unit roots in economic time series has far reaching consequences for univariate as well as multivariate econometric modelling. Therefore, unit root tests are nowadays the starting point of most empirical time series studies. The oldest and most widely used test is due to Dickey and Fuller (1979). Reviewing this test and variants thereof we focus on the importance of modelling the deterministic component. In particular, we survey the growing literature on tests accounting for structural shifts. Finally, further applied aspects are addressed how to get the size correct and obtain good power at the same time. --Dickey-Fuller,size and power,deterministic components,structural breaks