Bootstraping cointegration tests under structural co-breaks: a robust extended ECM test

The aim of the paper is the analysis of ECM bootstrap cointegration tests under structural breaks. Classical ECM tests depend on sorne nuisance parameters, which is an undesirable feature for empirical applications. This problem is overcome by using the bootstrap ECM test, which shows good size and power properties when there are no breaks. In this paper we study the small sample properties of alternative bootstrap ECM tests under different cobreaking situations. ECM test statistics are made robust to partial cobreaking by using extended error correction models or by imposing a common factor restriction

Cointegration Testing Under Structural Breaks: A Robust Extended Error Correction Model

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Detrending procedures and cointegration testing: ECM tests under structural breaks

It is well known that all the test for unit roots and cointegration depend on the deterministic elements that are in mean of the variables; constant, trend, breaks, outliers, segmented trends, etc. This is a serious inconvenient for empirical work. In this paper we analyze if those problems could be solved by forming the cointegration tests on extended models, on the components of the series obtained from trend cycle decompositions. We do that by Monte Carlo Simulations allowing for several structural breaks in the data generating process

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

Outliers robust ECM cointegration test based on the trend components

The main goal of this paper is to analyze the behaviour of the ECM non-co integration test when there are additive outliers in the time series under different co-breaking situations. We show that the critical values of the usual ECM test are not robust to the presence of transitory shocks and we suggest a procedure based on signal extraction to bypass this problem. These procedure renders ECM tests with a left tail of distribution under the null that is robust to the presence of additive outliers in the series. The small sample critical values and the empirical power of the test are analyzed by Monte Carlo simulations for several low frequency filters

Out-of-sample forecast errors in misspecified perturbed long memory processes

The correlogram is not a useful diagnosis tool in the presence of long-memory or long range depedent time series. The aim of this paper is to illustrate this claim by examining the relative increase in mean square forecast error from fitting a weakly stationary process to the series of interest hen in fact the true model is a so-called perturbed long-memory process recently introduced by Granger and Marmol (1997). This model has the property of being unidentifiable from a white noise process on the basis of the correlogram and the usual rule-of thumbs in the Box-Jenkins methodology. We prove that this kind of misspecification can lead to serious errors in terms of forecasting

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

A model free cointegration approach for pairs of I(d) variables

In this paper we propose several model free (non parametric) statistics to measure serial dependence that are useful to characterize the short and the long memory properties of series in the time and the frequency domain. Conditions on the joint memory properties of the series such as cointegration are introduced by means of these statistics. We show that the relationship between the non parametric concept of cointegration and the cross-covariance functions of the series, has a natural interpretation as an instrumental variable estimator. We show that its small sample behavior is better than the usual least squares estimator. Finally, from our characterization it is posibble to discriminate between fractional and integer cointegratio

Bootstrapping Cointegration Tests Under Structural Co-Breaks: A Robust Extended ECM test.

The aim of the paper is the analysis of ECM (Error Correction Model) bootstrap cointegration tests under structural breaks. Classical ECM tests depend on some nuisance parameters, which is an undesirable feature for empirical applications. This problem is overcome by using the bootstrap ECM test, which shows good size and power properties when there are no breaks. In this paper we study the small sample properties of alternative bootstrap ECM tests under different co-breaking situations. ECM test statistics are made robust to partial co-breaking by using extended error correction models or by imposing a common factor restriction.Bootstrap; structural breaks; cointegration testing; extended error correction model - co-breaks; 62M10; 91B84; 62F40; 82C80; 62F03; 62P20;