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On a Likelihood Ratio Test for Idiosyncratic Unit Roots in the Exact Factor Model with Nonstationary Common Shocks
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

We consider an exact factor model with unobservable common stochastic trends imposed by nonstationary factors, and study, by simulation, the power of the likelihood ratio test for unit roots in the idiosyncratic components. The power of the test is compared with the analogous Lagrange multiplier test and the Fisher-type test proposed by Bai and Ng (A PANIC attack on unit roots and cointegration, Econometrica,72, 1127–1177, 2004). The results suggest that the benefit of the likelihood ratio test is in panels with a small cross-section.

Keyword [en]
Panel unit root, Dynamic factors, Maximum likelihood, Likelihood ratio
National Category
Social Sciences
Research subject
Statistics
Identifiers
URN: urn:nbn:se:uu:diva-207293OAI: oai:DiVA.org:uu-207293DiVA: diva2:647580
Projects
Solving Macroeconomic Problems Using Non-Stationary Panel Data
Available from: 2013-09-11 Created: 2013-09-11 Last updated: 2014-01-23
In thesis
1. Likelihood-Based Tests for Common and Idiosyncratic Unit Roots in the Exact Factor Model
Open this publication in new window or tab >>Likelihood-Based Tests for Common and Idiosyncratic Unit Roots in the Exact Factor Model
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Dynamic panel data models are widely used by econometricians to study over time the economics of, for example, people, firms, regions, or countries, by pooling information over the cross-section. Though much of the panel research concerns inference in stationary models, macroeconomic data such as GDP, prices, and interest rates are typically trending over time and require in one way or another a nonstationary analysis. In time series analysis it is well-established how autoregressive unit roots give rise to stochastic trends, implying that random shocks to a dynamic process are persistent rather than transitory. Because the implications of, say, government policy actions are fundamentally different if shocks to the economy are lasting than if they are temporary, there are now a vast number of univariate time series unit root tests available. Similarly, panel unit root tests have been designed to test for the presence of stochastic trends within a panel data set and to what degree they are shared by the panel individuals. Today, growing data certainly offer new possibilities for panel data analysis, but also pose new problems concerning double-indexed limit theory, unobserved heterogeneity, and cross-sectional dependencies. For example, economic shocks, such as technological innovations, are many times global and make national aggregates cross-country dependent and related in international business cycles.

Imposing a strong cross-sectional dependence, panel unit root tests often assume that the unobserved panel errors follow a dynamic factor model. The errors will then contain one part which is shared by the panel individuals, a common component, and one part which is individual-specific, an idiosyncratic component. This is appealing from the perspective of economic theory, because unobserved heterogeneity may be driven by global common shocks, which are well captured by dynamic factor models. Yet, only a handful of tests have been derived to test for unit roots in the common and in the idiosyncratic components separately. More importantly, likelihood-based methods, which are commonly used in classical factor analysis, have been ruled out for large dynamic factor models due to the considerable number of parameters.

This thesis consists of four papers where we consider the exact factor model, in which the idiosyncratic components are mutually independent, and so any cross-sectional dependence is through the common factors only. Within this framework we derive some likelihood-based tests for common and idiosyncratic unit roots. In doing so we address an important issue for dynamic factor models, because likelihood-based tests, such as the Wald test, the likelihood ratio test, and the Lagrange multiplier test, are well-known to be asymptotically most powerful against local alternatives.

Our approach is specific-to-general, meaning that we start with restrictions on the parameter space that allow us to use explicit maximum likelihood estimators. We then proceed with relaxing some of the assumptions, and consider a more general framework requiring numerical maximum likelihood estimation. By simulation we compare size and power of our tests with some established panel unit root tests. The simulations suggest that the likelihood-based tests are locally powerful and in some cases more robust in terms of size.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2013. 51 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Social Sciences, ISSN 1652-9030 ; 90
Keyword
panel unit root, dynamic factors, maximum likelihood, Lagrange multiplier, likelihood ratio, factor analysis
National Category
Social Sciences
Research subject
Statistics
Identifiers
urn:nbn:se:uu:diva-207296 (URN)978-91-554-8754-6 (ISBN)
Public defence
2013-11-08, Hörsal 2, Ekonomikum, Kyrkogårdsgatan 10, Uppsala, 10:00 (English)
Opponent
Supervisors
Projects
Solving Macroeconomic Problems Using Non-Stationary Panel Data
Available from: 2013-10-17 Created: 2013-09-11 Last updated: 2014-01-23

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