Author: Anindya Banerjee
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Spurious regression analysis in panel data when the time series are cross-section dependent is analyzed in the paper. We show that consistent estimation of the long-run average parameter is possible once we control for cross-section dependence using cross-section averages in the spirit of the common correlated effects approach in Pesaran (2006). This result is used to design a panel cointegration test statistic accounting for cross-section dependence. The performance of the proposal is investigated in comparison with factor-based methods to control for cross-section dependence when strong, semi-weak and weak cross-section dependence may be present.
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: 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: Feng Qu
Publisher: World Scientific
ISBN: 9811220794
Category : Business & Economics
Languages : en
Pages : 167
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Book Description
This book aims to fill the gap between panel data econometrics textbooks, and the latest development on 'big data', especially large-dimensional panel data econometrics. It introduces important research questions in large panels, including testing for cross-sectional dependence, estimation of factor-augmented panel data models, structural breaks in panels and group patterns in panels. To tackle these high dimensional issues, some techniques used in Machine Learning approaches are also illustrated. Moreover, the Monte Carlo experiments, and empirical examples are also utilised to show how to implement these new inference methods. Large-Dimensional Panel Data Econometrics: Testing, Estimation and Structural Changes also introduces new research questions and results in recent literature in this field.
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.
Author: M. Hashem Pesaran
Publisher: Oxford University Press
ISBN: 0198736916
Category : Business & Economics
Languages : en
Pages : 1095
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Book Description
This book is concerned with recent developments in time series and panel data techniques for the analysis of macroeconomic and financial data. It provides a rigorous, nevertheless user-friendly, account of the time series techniques dealing with univariate and multivariate time series models, as well as panel data models. It is distinct from other time series texts in the sense that it also covers panel data models and attempts at a more coherent integration of time series, multivariate analysis, and panel data models. It builds on the author's extensive research in the areas of time series and panel data analysis and covers a wide variety of topics in one volume. Different parts of the book can be used as teaching material for a variety of courses in econometrics. It can also be used as reference manual. It begins with an overview of basic econometric and statistical techniques, and provides an account of stochastic processes, univariate and multivariate time series, tests for unit roots, cointegration, impulse response analysis, autoregressive conditional heteroskedasticity models, simultaneous equation models, vector autoregressions, causality, forecasting, multivariate volatility models, panel data models, aggregation and global vector autoregressive models (GVAR). The techniques are illustrated using Microfit 5 (Pesaran and Pesaran, 2009, OUP) with applications to real output, inflation, interest rates, exchange rates, and stock prices.
Author: Michael Beenstock
Publisher: Springer
ISBN: 3030036146
Category : Business & Economics
Languages : en
Pages : 280
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Book Description
This monograph deals with spatially dependent nonstationary time series in a way accessible to both time series econometricians wanting to understand spatial econometics, and spatial econometricians lacking a grounding in time series analysis. After charting key concepts in both time series and spatial econometrics, the book discusses how the spatial connectivity matrix can be estimated using spatial panel data instead of assuming it to be exogenously fixed. This is followed by a discussion of spatial nonstationarity in spatial cross-section data, and a full exposition of non-stationarity in both single and multi-equation contexts, including the estimation and simulation of spatial vector autoregression (VAR) models and spatial error correction (ECM) models. The book reviews the literature on panel unit root tests and panel cointegration tests for spatially independent data, and for data that are strongly spatially dependent. It provides for the first time critical values for panel unit root tests and panel cointegration tests when the spatial panel data are weakly or spatially dependent. The volume concludes with a discussion of incorporating strong and weak spatial dependence in non-stationary panel data models. All discussions are accompanied by empirical testing based on a spatial panel data of house prices in Israel.
Author:
Publisher:
ISBN: 9789178955145
Category : Econometrics
Languages : en
Pages :
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Book Description
This thesis contributes to econometric methodology in terms of estimation and inference in static panel data models with unobserved multidimensional heterogeneity. When not properly accounted for, unobserved heterogeneity may introduce bias into the parameter estimates associated with covariates of interest, such as treatment indicators or determinants of macroeconomic indicators. A common way of representing such heterogeneity is through an interactive effects structure estimated by factor-augmented regression models. ??One of the workhorse methods in this literature is the common correlated effects (CCE) estimator of Pesaran (2006). A major inconvenience with this method is that its statistical properties are derived under the assumption that both the cross-section dimension, $N$, and the time dimension, $T$, of the panel are large, a condition that is rarely met by datasets used in empirical practice. In the first chapter, we develop a new theory that establishes the asymptotic properties of the CCE estimator in panel datasets with small time dimension $T$. We show that many of the previously derived large-$T$ results continue to hold.??The second chapter investigates the well-known dummy variable trap in the framework of factor-augmented regressions. The problem of multicollinearity among regressors has been extensively discussed in the fixed effects literature but has gone largely unnoticed in the case of interactive effects. We consider the challenging case when some regressors are asymptotically collinear with the interactive effects. In this setting we develop the relevant asymptotic theory.??In the third chapter, we show that fixed effects demeaning in linear panel data regressions is more useful than commonly thought, in that it enables consistent and asymptotically normal estimation of interactive effects models with heterogeneous slope coefficients for panels where $T$ is small and only $N$ is large. As an illustration, we consider the problem of estimating the average treatment effect in the presence of unobserved time-varying heterogeneity. ??The last chapter reviews the use of panel cointegration tests in studies on the existence of a long-run equilibrium relation between insurance market activity and economic output. I point out consequences for the validity of empirical findings when violating theoretically motivated conditions on the relative dimensions of the panel dataset under consideration. The bulk of existing evidence relies on Pedroni's (2004) residual-based panel cointegration test procedure. I demonstrate how this test procedure tends to over-reject the null hypothesis of no cointegration leading to potentially false conclusions if the data set does not meet the theoretical restrictions on the panel size.
Author: Yves Croissant
Publisher: John Wiley & Sons
ISBN: 1118949188
Category : Mathematics
Languages : en
Pages : 435
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Book Description
Panel Data Econometrics with R provides a tutorial for using R in the field of panel data econometrics. Illustrated throughout with examples in econometrics, political science, agriculture and epidemiology, this book presents classic methodology and applications as well as more advanced topics and recent developments in this field including error component models, spatial panels and dynamic models. They have developed the software programming in R and host replicable material on the book’s accompanying website.
Author: Mr.Markus Eberhardt
Publisher: International Monetary Fund
ISBN: 1484309715
Category : Business & Economics
Languages : en
Pages : 55
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Book Description
We study the long-run relationship between public debt and growth in a large panel of countries. Our analysis takes particular note of theoretical arguments and data considerations in modeling the debt-growth relationship as heterogeneous across countries. We investigate the issue of nonlinearities (debt thresholds) in both the cross-country and within-country dimensions, employing novel methods and diagnostics from the time-series literature adapted for use in the panel. We find some support for a nonlinear relationship between debt and long-run growth across countries, but no evidence for common debt thresholds within countries over time.
