Crandi

Unit Roots And Cointegration Modelling Through A Family Of Flexible

Leo Migdal
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unit roots and cointegration modelling through a family of flexible

Academia.edu no longer supports Internet Explorer. To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser. 2010, Journal of Statistical Computation and Simulation We propose a fast and consistent procedure to detect unit roots based on subspace methods. It has three distinctive features. First, the same method can be applied to single or multiple time series.

Second, it employs a flexible family of information criteria, which loss functions can be adapted to the statistical properties of the data. Last, it does not require specifying a model for the analyzed series. Besides, we provide a subspace-based consistent estimator for the cointegrating rank and the cointegrating matrix. Simulation exercises show that these procedures have good finite sample properties. In cointegration analysis, it is customary to test the hypothesis of unit roots separately for each single time series. In this note, we point out that this procedure may imply large size distortion of the unit root tests if the DGP is a VAR.

It is well-known that univariate models implied by a VAR data generating process necessarily have a finite order MA component. This feature may explain why an MA component has often been found in univariate ARIMA models for economic time series. Thereby, it has important implications for unit root tests in univariate settings given the well-known size distortion of popular unit root test in the presence of a large negative coefficient in the MA component. In a small simulation experiment, considering several popular unit root tests and the ADF sieve bootstrap unit tests, we find that, besides the well known size distortion effect, there can be substantial differences in... Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic Research output: Working paper / Preprint › Preprint

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic N1 - Data source: FRED-MD Database (https://research.stlouisfed.org/econ/mccracken/fred-databases/), CBS StatLine (https://opendata.cbs.nl/statline/#/CBS/en/) & Google Trends (https://trends.google.com/trends) N2 - In this chapter we investigate how the possible presence of unit roots and cointegration affects forecasting with big data. As most macroeoconomic time series are very persistent and may contain unit roots, a proper handling of unit roots and cointegration is of paramount importance for macroeconomic forecasting. The high-dimensional nature of big data complicates the analysis of unit roots and cointegration in two ways. First, transformations to stationarity require performing many unit root tests, increasing room for errors in the classification.

Second, modelling unit roots and cointegration directly is more difficult, as standard high-dimensional techniques such as factor models and penalized regression are not directly applicable to (co)integrated data and need to be adapted. In this chapter we provide an overview of both issues and review methods proposed to address these issues. These methods are also illustrated with two empirical applications. Part of the book series: Advanced Studies in Theoretical and Applied Econometrics ((ASTA,volume 46)) This is a preview of subscription content, log in via an institution to check access. Tax calculation will be finalised at checkout

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Academia.edu No Longer Supports Internet Explorer. To Browse Academia.edu And

Academia.edu no longer supports Internet Explorer. To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser. 2010, Journal of Statistical Computation and Simulation We propose a fast and consistent procedure to detect unit roots based on subspace methods. It has three distinctive features. First, the same method can be applied to sin...

Second, It Employs A Flexible Family Of Information Criteria, Which

Second, it employs a flexible family of information criteria, which loss functions can be adapted to the statistical properties of the data. Last, it does not require specifying a model for the analyzed series. Besides, we provide a subspace-based consistent estimator for the cointegrating rank and the cointegrating matrix. Simulation exercises show that these procedures have good finite sample pr...

It Is Well-known That Univariate Models Implied By A VAR

It is well-known that univariate models implied by a VAR data generating process necessarily have a finite order MA component. This feature may explain why an MA component has often been found in univariate ARIMA models for economic time series. Thereby, it has important implications for unit root tests in univariate settings given the well-known size distortion of popular unit root test in the pr...

Research Output: Chapter In Book/Report/Conference Proceeding › Chapter › Academic

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic N1 - Data source: FRED-MD Database (https://research.stlouisfed.org/econ/mccracken/fred-databases/), CBS StatLine (https://opendata.cbs.nl/statline/#/CBS/en/) & Google Trends (https://trends.google.com/trends) N2 - In this chapter we investigate how the possible presence of unit roots and cointegration affects forec...

Second, Modelling Unit Roots And Cointegration Directly Is More Difficult,

Second, modelling unit roots and cointegration directly is more difficult, as standard high-dimensional techniques such as factor models and penalized regression are not directly applicable to (co)integrated data and need to be adapted. In this chapter we provide an overview of both issues and review methods proposed to address these issues. These methods are also illustrated with two empirical ap...