Download or read book Testing for Panel Cointegration Using Common Correlated Effects Estimators written by Anindya Banerjee and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Nonstationary Panels Panel Cointegration and Dynamic Panels written by Badi H. Baltagi and published by Elsevier. This book was released on 2000 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Large dimensional Panel Data Econometrics Testing Estimation And Structural Changes written by Feng Qu and published by World Scientific. This book was released on 2020-08-24 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Time Series and Panel Data Econometrics written by M. Hashem Pesaran and published by Oxford University Press. This book was released on 2015 with total page 1095 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book The Econometric Analysis of Non Stationary Spatial Panel Data written by Michael Beenstock and published by Springer. This book was released on 2019-03-27 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Essays on Panel Data with Multidimensional Unobserved Heterogeneity written by and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Econometric Analysis of Cross Section and Panel Data second edition written by Jeffrey M. Wooldridge and published by MIT Press. This book was released on 2010-10-01 with total page 1095 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book This Time They Are Different written by Mr.Markus Eberhardt and published by International Monetary Fund. This book was released on 2013-12-17 with total page 55 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Panel Data Econometrics with R written by Yves Croissant and published by John Wiley & Sons. This book was released on 2018-08-10 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book The Econometrics of Panel Data written by Lászlo Mátyás and published by Advanced Studies in Theoretical and Applied Econometrics. This book was released on 2008-04-25 with total page 992 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume provides a general overview of the econometrics of panel data, both from a theoretical and from an applied viewpoint. This third edition provides a presentation of theoretical developments as well as surveys about how econometric tools are used to study firms and household's behaviors.

Download or read book Sustainable Industrial Engineering along Product Service Life Cycle Supply Chain written by João Carlos de Oliveira Matias and published by MDPI. This book was released on 2021-08-25 with total page 474 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sustainable industrial engineering addresses the sustainability issue from economic, environmental, and social points of view. Its application fields are the whole value chain and lifecycle of products/services, from the development to the end-of-life stages. This book aims to address many of the challenges faced by industrial organizations and supply chains to become more sustainable through reinventing their processes and practices, by continuously incorporating sustainability guidelines and practices in their decisions, such as circular economy, collaboration with suppliers and customers, using information technologies and systems, tracking their products’ life-cycle, using optimization methods to reduce resource use, and to apply new management paradigms to help mitigate many of the wastes that exist across organizations and supply chains. This book will be of interest to the fast-growing body of academics studying and researching sustainability, as well as to industry managers involved in sustainability management.

Download or read book Unit Roots Cointegration and Structural Change written by G. S. Maddala and published by Cambridge University Press. This book was released on 1998 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive review of unit roots, cointegration and structural change from a best-selling author.

Download or read book Econometric Analysis of Panel Data written by Badi Baltagi and published by John Wiley & Sons. This book was released on 2008-06-30 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by one of the world's leading researchers and writers in the field, Econometric Analysis of Panel Data has become established as the leading textbook for postgraduate courses in panel data. This new edition reflects the rapid developments in the field covering the vast research that has been conducted on panel data since its initial publication. Featuring the most recent empirical examples from panel data literature, data sets are also provided as well as the programs to implement the estimation and testing procedures described in the book. These programs will be made available via an accompanying website which will also contain solutions to end of chapter exercises that will appear in the book. The text has been fully updated with new material on dynamic panel data models and recent results on non-linear panel models and in particular work on limited dependent variables panel data models.

Download or read book Handbook Of Energy Finance Theories Practices And Simulations written by Stephane Goutte and published by World Scientific. This book was released on 2020-01-30 with total page 827 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling the dynamics of energy markets has become a challenging task. The intensification of their financialization since 2004 had made them more complex but also more integrated with other tradable asset classes. More importantly, their large and frequent fluctuations in terms of both prices and volatility, particularly in the aftermath of the global financial crisis 2008-2009, posit difficulties for modeling and forecasting energy price behavior and are primary sources of concerns for macroeconomic stability and general economic performance.This handbook aims to advance the debate on the theories and practices of quantitative energy finance while shedding light on innovative results and technical methods applied to energy markets. Its primary focus is on the recent development and applications of mathematical and quantitative approaches for a better understanding of the stochastic processes that drive energy market movements. The handbook is designed for not only graduate students and researchers but also practitioners and policymakers.

Download or read book Testing for Multicointegration in Panel Data with Common Factors written by Vanessa Berenguer-Rico and published by . This book was released on 2006 with total page 29 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Festschrift in Honor of Peter Schmidt written by Robin C. Sickles and published by Springer Science & Business Media. This book was released on 2014-03-15 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: From the Introduction: This volume is dedicated to the remarkable career of Professor Peter Schmidt and the role he has played in mentoring us, his PhD students. Peter’s accomplishments are legendary among his students and the profession. Each of the papers in this Festschrift is a research work executed by a former PhD student of Peter’s, from his days at the University of North Carolina at Chapel Hill to his time at Michigan State University. Most of the papers were presented at The Conference in Honor of Peter Schmidt, June 30 - July 2, 2011. The conference was largely attended by his former students and one current student, who traveled from as far as Europe and Asia to honor Peter. This was a conference to celebrate Peter’s contribution to our contributions. By “our contributions” we mean the research papers that make up this Festschrift and the countless other publications by his students represented and not represented in this volume. Peter’s students may have their families to thank for much that is positive in their lives. However, if we think about it, our professional lives would not be the same without the lessons and the approaches to decision making that we learned from Peter. We spent our days together at Peter’s conference and the months since reminded of these aspects of our personalities and life goals that were enhanced, fostered, and nurtured by the very singular experiences we have had as Peter’s students. We recognized in 2011 that it was unlikely we would all be together again to celebrate such a wonderful moment in ours and Peter’s lives and pledged then to take full advantage of it. We did then, and we are now in the form of this volume.

Download or read book Mostly Panel Econometrics written by Ovidijus Stauskas and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis consists of five chapters which focus on panel data theory. Four of them analyze explicit panel data models and one chapter deals with time series forecasting model, where external panel data help us estimate unobserved explanatory variables. The broad topics discussed in the thesis include i) simplification of distribution of a statistical test under double asymptotics, ii) elimination of fixed effects and bias correction in dynamic panels, iii) accounting for cross-section dependence and estimation of latent factors when they can be non-stationary and iv) usage of latent factors to improve out-of-sample forecasts and testing competing forecast models. In Chapter I, we re-visit a problem posed by Phillips and Lee (2015, Econometric Reviews). They considered a simple bivariate vector autoregression (VAR), where both series exhibited different degrees of non-stationarity: near unit root and mild explosiveness. While one is interested in testing whether both series are in the lower vicinity of unit root and share the same persistence features, unfortunately, Wald test statistic degenerates under the null. We re-consider this setup in the context of panel data, where we use extra observations from the cross-section to simplify asymptotic distributions in order to obtain chi-square-based inference.??Chapter II looks into very popular factor augmented linear forecast models and tests to evaluate out-of-sample forecasting accuracy. In large macroeconomic datasets, various series tend to co-move together and it is modelled by employing a small number of latent factors (see e.g. Stock and Watson, 1999 and 2002). Instead of using a large number of available variables, researchers reduce the dataset dimension by estimating the driving factors and use those estimates directly. We further explore two tests of equal forecasting accuracy for nested models to investigate if factor augmented model outperforms parsimonious model with known set of variables. Unlike Gonçalves el. al (2017, Journal of Econometrics), where the factors are estimated using Principal Components (PC) under presumably known number of factors, we employ Common Correlated Effects (CCE) estimator which is very user friendly and employs a common thematic block structure of large macro datasets. Factors are estimated as block averages to proxy the common underlying information given by factors.??We continue discussing latent factors in Chapter III and Chapter IV. Here we focus on panel data, where unobserved factors model strong cross-section dependence among the panel units and possible endogeneity within the individual time series. Pesaran (2006, Econometrica) suggested solving these issues by augmenting the regression with cross-section averages of the dependent and independent variables. This is CCE estimator. While very simple, pooled version of CCE (CCEP) is asymptotically biased under homogeneous slopes, unless the number of individuals dominates the length of time series in the panel. Moreover, typically the bias is inestimable and analytic correction is not possible. In Chapter III, we analyze the properties of a simple 'pairs' bootstrap algorithm discussed in Kapetanios (2008, Econometrics Journal) in the context of CCE and develop bootstrap-based bias correction procedure. In Chapter IV, we continue the study of Westerlund (2018, Econometrics Journal), where CCE was extended to non-stationary factors of a very general type. In the latter study, however, only CCEP under homogeneous slopes was examined, but we extend the analysis to heterogeneous slopes and explore the properties of the mean group (CCEMG) estimator in order to further model unobserved heterogeneity.??The thesis concludes with Chapter V, where we re-visit at a classical problem in dynamic panels with fixed effects known as Nickel Bias. De-meaning the data to purge individual-specific effects in dynamic panels makes the model errors correlated, and the bias accumulates if the time dimension is large. On the other hand, if we estimate the fixed effects, we run into incidental parameter problem. Bai (2013, Econometrica) considered the so-called Factor Analytical (FA) estimator, which circumvents these issues by estimating the sample variance of individual effects rather than the effects themselves. In the latter study, panel AR(1) model with autoregressive parameter in the stationary region was explored. We extend this to autoregressive coefficient tending to unity and incidental trends, similarly to Moon and Phillips (2004, Econometrica) in order to account for trending and drifting variables.