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Econometric Theory (econometric + theory)

Distribution by Scientific Domains
Business, Economics, Finance and Accounting100%

Selected Abstracts

Cointegration Testing in Panels with Common Factors,

OXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 2006
Christian Gengenbach
Abstract Panel unit-root and no-cointegration tests that rely on cross-sectional independence of the panel unit experience severe size distortions when this assumption is violated, as has, for example, been shown by Banerjee, Marcellino and Osbat [Econometrics Journal (2004), Vol. 7, pp. 322,340; Empirical Economics (2005), Vol. 30, pp. 77,91] via Monte Carlo simulations. Several studies have recently addressed this issue for panel unit-root tests using a common factor structure to model the cross-sectional dependence, but not much work has been done yet for panel no-cointegration tests. This paper proposes a model for panel no-cointegration using an unobserved common factor structure, following the study by Bai and Ng [Econometrica (2004), Vol. 72, pp. 1127,1177] for panel unit roots. We distinguish two important cases: (i) the case when the non-stationarity in the data is driven by a reduced number of common stochastic trends, and (ii) the case where we have common and idiosyncratic stochastic trends present in the data. We discuss the homogeneity restrictions on the cointegrating vectors resulting from the presence of common factor cointegration. Furthermore, we study the asymptotic behaviour of some existing residual-based panel no-cointegration tests, as suggested by Kao [Journal of Econometrics (1999), Vol. 90, pp. 1,44] and Pedroni [Econometric Theory (2004a), Vol. 20, pp. 597,625]. Under the data-generating processes (DGP) used, the test statistics are no longer asymptotically normal, and convergence occurs at rate T rather than as for independent panels. We then examine the possibilities of testing for various forms of no-cointegration by extracting the common factors and individual components from the observed data directly and then testing for no-cointegration using residual-based panel tests applied to the defactored data. [source]

Testing for Multicointegration in Panel Data with Common Factors,

OXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 2006
Vanessa Berenguer-Rico
Abstract This paper addresses the concept of multicointegration in a panel data framework and builds upon the panel data cointegration procedures developed in Pedroni [Econometric Theory (2004), Vol. 20, pp. 597,625]. When individuals are either cross-section independent, or cross-section dependence can be removed by cross-section demeaning, our approach can be applied to the wider framework of mixed I(2) and I(1) stochastic processes. The paper also deals with the issue of cross-section dependence using approximate common-factor models. Finite sample performance is investigated through Monte Carlo simulations. Finally, we illustrate the use of the procedure investigating an inventories, sales and production relationship for a panel of US industries. [source]

Sinning in the Basement: What are the Rules?

JOURNAL OF ECONOMIC SURVEYS, Issue 4 2002
The Ten Commandments of Applied Econometrics
Unpleasant realities of real-world data force applied econometricians to violate the prescriptions of econometric theory as taught by our textbooks. Leamer (1978) vividly describes this behavior as wanton sinning in the basement, with sinners' metamorphizing into high priests as they ascend to the third floor to teach econometric theory. But this sinning is not completely wanton , applied econometricians do (or should) follow some unwritten rules of behavior, in effect bounding the sinning and promoting a brand of honor among sinners. This paper exposits these rules, and culls from them an unauthorized list of the Ten Commandments of applied econometrics. [source]

NONPARAMETRIC LIKELIHOOD: EFFICIENCY AND ROBUSTNESS,

THE JAPANESE ECONOMIC REVIEW, Issue 1 2007
YUICHI KITAMURAArticle first published online: 8 FEB 200
Nonparametric likelihood is a natural generalization of parametric likelihood and it offers effective methods for analysing economic models with nonparametric components. This is of great interest, since econometric theory rarely suggests a parametric form of the probability law of data. Being a nonparametric method, nonparametric likelihood is robust to misspecification. At the same time, it often achieves good properties that are analogous to those of parametric likelihood. This paper explores various applications of nonparametric likelihood, with some emphasis on the analysis of biased samples and data combination problems. [source]