Andreu Sansó

Testing the Null of Cointegration With Structural Breaks

In this paper we propose an LM-Type statistic to test the null hypothesis of cointegration allowing for the possibility of a structural break, in both the deterministic and the cointegration vector. Our proposal focuses on the presence of endogenous regressors and analyses which estimation method provides better results. The test has been designed to be used as a complement to the usual non-cointegration tests in order to obtain stronger evidence of cointegration. We consider the cases of known and unknown break date. In the latter case, we show that minimizing the SSR results in a super-consistent estimator of the break fraction. Finally, the behaviour of the tests is studied through Monte Carlo experiments.

Measurement errors and outliers in seasonal unit root testing

Seasonal and non-seasonal data are frequently observed with noise. For instance, the time series can have irregular abrupt changes and interruptions following as a result of additive or temporary change outliers caused by external circumstances. Equally, the time series can have measurement errors. In this paper we analyse the above types of data irregularities on the behavior of seasonal unit root tests. Outliers and measurement errors can seriously affect seasonal unit root inference and it is shown how the distortion of the tests will depend upon the frequency, magnitude, and persistence of the outliers as well as on the signal to noise ratio associated with measurement errors. Some solutions to the implied inference problems are suggested and shown to work in practice.

Joint hypothesis specification for unit root tests with a structural break

Several tests based on a t-ratio have been proposed in the literature to decide the order of integration of a time series allowing for a structural break. However, another approach based on testing a joint hypothesis of unit root and the irrelevance of some nuisance parameters is also feasible. This paper proposes new unit root tests consistent with the presence of a structural break applying this second perspective. Our approach deals both with the case where the break is not allowed under the null hypothesis, and where it is allowed. Simulations investigate the performance of this proposal compared to the existing tests and show important gains in terms of power. Copyright Royal Economic Society 2006

ESTIMATION OF COINTEGRATING VECTORS WITH TIME SERIES MEASURED AT DIFFERENT PERIODICITY

We discuss the effects of temporal aggregation on the estimation of cointegrating vectors and on testing linear restrictions on this vector. We adopt a discrete time approach and demonstrate, in contrast with the findings of Chambers (2003, Econometric Theory 19, 49–77), who adopts a continuous time approach, that in some situations, when the regressand must be aggregated, systematic sampling is preferable to average sampling for estimation purposes. Like Chambers, we show that the best aggregation scheme for regressors, in terms of asymptotic estimation efficiency, is always average sampling. We also show that different types of aggregation have no influence on the relative size of tests of linear restrictions on the cointegration vector.We thank Soren Johansen, Niels Haldrup, Raquel Waters, the associate editor, and two anonymous referees for their helpful comments. Of course, any remaining error is the responsibility of the authors. The first author gratefully acknowledges the financial support of a Marie Curie Fellowship of the European Community Programme “Improving the Human Research Potential and the Socio-Economic Knowledge Base†under contract HPMF-CT-2002-01662 and the Danish Research Council. The second author gratefully acknowledges the financial support of the Spanish Ministry of Science and Technology SEC2002-01512.

Measurement Errors and Outliers in Seasonal Unit Root Testing

Frequently, seasonal and non-seasonal data (especially macro time series) are observed with noise. For instance, the time series can have irregular abrupt changes and interruptions following as a result of additive or temporary change outliers caused by external circumstances which are irrelevant for the series of interest. Equally, the time series can have measurement errors. In this paper we analyse the above types of data irregularities on the behaviour of seasonal unit roots. It occurs that in most cases outliers and measurement errors can seriously affect inference towards the rejection of seasonal unit roots. It is shown how the distortion of the tests will depend upon the frequency, magnitude, and persistence of the outliers as well as on the signal to noise ratio associated with measurement errors. Some solutions to the implied inference problems are suggested.

Detection of Additive Outliers in Seasonal Time Series

The detection and location of additive outliers in integrated variables has attracted much attention recently because such outliers tend to affect unit root inference among other things. Most of these procedures have been developed for non-seasonal processes. However, the presence of seasonality in the form of seasonally varying means and variances affect the properties of outlier detection procedures, and hence appropriate adjustments of existing methods are needed for seasonal data. In this paper we suggest modifications of tests proposed by Shin, Sarkar and Lee (1996) and Perron and Rodriguez (2003) to deal with data sampled at a seasonal frequency and we discuss their size and power properties. We also show that the presence of periodic heteroscedasticity will inflate the size of the tests and hence will tend to identify an excessive number of outliers. A modified Perron-Rodriguez test which allows periodically varying variances is suggested, and it is shown to have excellent properties in terms of both power and size.

Testing for Additive Outliers in Seasonally Integrated Time Series

The detection of additive outliers in integrated variables has attracted some attention recently, see e.g. Shin et al. (1996), Vogelsang (1999) and Perron and Rodriguez (2003). This paper serves several purposes. We prove the inconsistency of the test proposed by Vogelsang, we extend the tests proposed by Shin et al. and Perron and Rodriguez to the seasonal case, and we consider alternative ways of computing their tests. We also study the effects of periodically varying variances on the previous tests and demonstrate that these can be seriously size distorted. Subsequently, some new tests that allow for periodic heteroskedasticity are proposed.

The Dickey-Fuller Test Family and Changes in the Seasonal Pattern

This paper studies the asymptotic behaviour of the Dickey-Fuller family of tests when the variable being considered exhibits a break in the seasonal pattern. We show that the Dickey-Fuller test tends to reject the unit root null hypothesis, except when the break affects all the periods in a similar manner. By contrast, the Dickey-Hasza-Fuller test is biased towards the acceptance of its null hypothesis, the larger the break magnitudes.

Exchange-Rate Movements and the Export of Brazilian Manufactures

This chapter considers the extent to which exchange-rate movements affect the ‘competitiveness’ of Brazilian exports of manufactured goods. Changes in the exchange rate are normally split into changes in the destination currency prices of exported goods, and changes in the profit margins of the exporting firms.