The Performance of Panel Unit Root and Stationarity Tests PDF Download
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Author: Jaroslava Hlouskova
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Jaroslava Hlouskova
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Badi H. Baltagi
Publisher: Elsevier
ISBN: 0762306882
Category : Business & Economics
Languages : en
Pages : 351
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Book Description
In the 16th Edition of Advances in Econometrics we present twelve papers discussing the current interface between Marketing and Econometrics. The authors are leading scholars in the fields and introduce the latest models for analysing marketing data. The papers are representative of the types of problems and methods that are used within the field of marketing. Marketing focuses on the interaction between the firm and the consumer. Economics encompasses this interaction as well as many others. Economics, along with psychology and sociology, provides a theoretical foundation for marketing.
Author: Steph De Silva
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 366
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Author: Laszlo Konya
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Using several different unit root/stationarity tests on single time series Konya (2000) found the logarithm of real GDP of most OECD countries behaving as a random walk during the last four decades. This outcome, however, might be due to the generally low power of these tests. The aim of this paper is to reconsider this issue by exploiting the extra information provided by the combination of the time-series and cross-sectional data and the subsequent power advantages of panel data unit root tests. We apply the tests advocated by Levin and Lin (1993), Im, Pesaran and Shin (1997) and Maddala and Wu (1999). The joint unit root null hypothesis cannot be rejected for the whole panel, however, after having dropped the least likely stationary series from the panel, the Im, Pesaran and Shin (1997) and Maddala and Wu (1999) tests can reject the null for the remaining sub-panels. Since these tests are not valid under cross-correlated error terms, we repeat them with data specific bootstrap critical values taking contemporaneous cross-correlation into account. This way, both tests can reject the unit root null hypothesis even for the whole panel. Yet, they cannot suggest how many and which particular panel members are stationary, so these results are not really informative. For this reason finally we apply the unit root test of Breuer, McNown and Wallace (1999) with bootstrap critical values. This procedure makes use of the panel data setting and seemingly unrelated regressions, but performs separate unit-root tests on each panel member. The results are markedly different from the conventional univariate Dickey-Fuller test results, and they support trend stationarity in the case of four countries: Australia, Japan, the Netherlands and Switzerland.
Author: G. S. Maddala
Publisher: Cambridge University Press
ISBN: 9780521587822
Category : Business & Economics
Languages : en
Pages : 528
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Book Description
A comprehensive review of unit roots, cointegration and structural change from a best-selling author.
Author: Lászlo Mátyás
Publisher: Springer Science & Business Media
ISBN: 3540758925
Category : Business & Economics
Languages : en
Pages : 966
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Book Description
This restructured, updated Third Edition provides a general overview of the econometrics of panel data, from both theoretical and applied viewpoints. Readers discover how econometric tools are used to study organizational and household behaviors as well as other macroeconomic phenomena such as economic growth. The book contains sixteen entirely new chapters; all other chapters have been revised to account for recent developments. With contributions from well known specialists in the field, this handbook is a standard reference for all those involved in the use of panel data in econometrics.
Author: Kristian Jönsson
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Abstract: In this paper, we investigate the performance of a panel data stationarity test when cross-sectional correlation is modelled by a time-specific factor. Size distortions, that occurs especially when the number of cross sections is small, are documented. To eliminate these distortions, a new set of critical values is supplied. When investigating the rejection frequency under the alternative hypothesis, it is found that the panel data stationarity test that uses the supplied critical values maintain good power characteristics even when only a subset of the cross-sectional units have a unit root
Author: Hyungsik Roger Moon
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
This paper studies testing for a unit root for large n and T panels in which the cross-sectional units are correlated. To model this cross-sectional correlation, we assume that the data is generated by an unknown number of unobservable common factors. We propose unit root tests in this environment and derive their (Gaussian) asymptotic distribution under the null hypothesis of a unit root and local alternatives. We also show that these tests have no power against the same local alternatives when it is necessary to remove deterministic components. Through Monte Carlo simulations, we provide evidence on the finite sample properties of these new tests.
Author: Chris Brooks
Publisher: Cambridge University Press
ISBN: 1139472305
Category : Business & Economics
Languages : en
Pages : 752
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Book Description
This best-selling textbook addresses the need for an introduction to econometrics specifically written for finance students. Key features: • Thoroughly revised and updated, including two new chapters on panel data and limited dependent variable models • Problem-solving approach assumes no prior knowledge of econometrics emphasising intuition rather than formulae, giving students the skills and confidence to estimate and interpret models • Detailed examples and case studies from finance show students how techniques are applied in real research • Sample instructions and output from the popular computer package EViews enable students to implement models themselves and understand how to interpret results • Gives advice on planning and executing a project in empirical finance, preparing students for using econometrics in practice • Covers important modern topics such as time-series forecasting, volatility modelling, switching models and simulation methods • Thoroughly class-tested in leading finance schools. Bundle with EViews student version 6 available. Please contact us for more details.
Author: Jeffrey M. Wooldridge
Publisher: MIT Press
ISBN: 0262232588
Category : Business & Economics
Languages : en
Pages : 1095
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Book Description
The second edition of a comprehensive state-of-the-art graduate level text on microeconometric methods, substantially revised and updated. The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis. Econometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the "generalized estimating equation" literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain "obvious" procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights.
