Advances in Econometrics – serie

The entropy concept was developed and used by Shannon in 1940 as a measure of uncertainty in the context of information theory. In 1957 Jaynes made use of Shannon's entropy concept as a basis for estimation and inference in problems that are ill-suited for traditional statistical procedures. This volume consists of two sections. The first section contains papers developing econometric methods based on the entropy principle. An interesting array of applications is presented in the second section of the volume.

Often applied econometricians are faced with working with data that is less than ideal. The data may be observed with gaps in it, a model may suggest variables that are observed at different frequencies, and sometimes econometric results are very fragile to the inclusion or omission of just a few observations in the sample. Papers in this volume discuss new econometric techniques for addressing these problems.

This volume is dedicated to two recent intensive areas of research in the econometrics of panel data, namely nonstationary panels and dynamic panels. It includes a comprehensive survey of the nonstationary panel literature including panel unit root tests, spurious panel regressions and panel cointegration tests. In addition, it provides recent developments in the estimation of dynamic panel data models using generalized method of moments. The volume includes eleven chapters written by twenty authors. These chapters: investigate better methods of estimating dynamic panels; develop methods for estimating and testing hypotheses for cointegrating vectors in dynamic panels; extend the concept of serial correlation common features analysis to nonstationary panel data models; study the local power of panel unit root test statistics; derive the asymptotic distributions of various estimators for the panel cointegrated regression model; propose a unit root test in the presence of structural change; develop a new limit theory for panel data that may be cross-sectionally heterogeneous; propose stationarity tests for a heterogeneous panel data model; derive instrumental variable estimators for a semiparametric partially linear dynamic panel data model; and conduct Monte Carlo experiments to study the small sample properties of a growth convergence equation. This collection of papers should prove useful for practitioners and researchers working with panel data.

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. Given the applied nature of marketing research, measurement and quantitative issues arise frequently. Quantitative marketing tends to rely heavily upon statistics and econometrics. However, quantitative marketing can place a different emphasis upon the problem than econometrics, even when using the same techniques. A basic difference between quantitative marketing research and econometrics tends to be the pragmatism that is found in many marketing studies. Another important motivating factor in marketing research is the type of data that is available. Applied econometrics tends to rely heavily on data collected by governmental organizations. In contrast, marketing often uses data collected by private firms or marketing research firms. Observational and survey data are quite similar to those used in econometrics. However, the remaining types of data, panel and transactional, can look quite different from what may be familiar to econometricians. The automation and computerization of much of the sales transaction process leaves an audit trail that results in huge quantities of data. A popular area of study is the use of scanner data collected at the checkout stand using bar code readers. Methods that work for small data sets may not work well in these larger data sets. In addition, new sources of data, such as clickstream data from a web site, will offer new challenges. This volume addresses these and related issues.

This volume is the result of an "Advances in Econometrics" conference held in November of 2002 at Louisiana State University in recognition of Halbert White's pioneering work published in Econometrica in 1980 and 1982 on robust variance-covariance estimation and quasi-maximum likelihood estimation. It contains 11 papers on a range of related topics including the estimation of possibly misspecified error component and fixed effects panel models, estimation and inference in possibly misspecified quantile regression models, quasi-maximum likelihood estimation of linear regression models with bounded and symmetric errors and quasi-maximum likelihood estimation of models with parameter dependencies between the mean vector and error variance-covariance matrix. Other topics include GMM, HAC, Heckit, asymmetric GARCH, Cross-Entropy, and multivariate deterministic trend estimation and testing under various possible misspecifications.

This volume focuses on econometric models that confront estimation and inference issues occurring when sample data exhibit spatial or spatiotemporal dependence. This can arise when decisions or transactions of economic agents are related to the behaviour of nearby agents. Dependence of one observation on neighbouring observations violates the typical assumption of independence made in regression analysis. Contributions to this volume by leading experts in the field of spatial econometrics provide details regarding estimation and inference based on a variety of econometric methods including, maximum likelihood, Bayesian and hierarchical Bayes, instrumental variables, generalized method of moments, maximum entropy, non-parametric and spatiotemporal. An overview of spatial econometric models and methods is provided that places contributions to this volume in the context of existing literature. New methods for estimation and inference are introduced in this volume and Monte Carlo comparisons of existing methods are described. In addition to topics involving estimation and inference, approaches to model comparison and selection are set forth along with new tests for spatial dependence and functional form. These methods are applied to a variety of economic problems including: hedonic real estate pricing, agricultural harvests and disaster payments, voting behaviour, identification of edge cities, and regional labour markets. The volume is supported by a web site containing data sets and software to implement many of the methods described by contributors to this volume.

Artificial intelligence is a consortium of data-driven methodologies which includes artificial neural networks, genetic algorithms, fuzzy logic, probabilistic belief networks and machine learning as its components. We have witnessed a phenomenal impact of this data-driven consortium of methodologies in many areas of studies, the economic and financial fields being of no exception. In particular, this volume of collected works will give examples of its impact on the field of economics and finance. This volume is the result of the selection of high-quality papers presented at a special session entitled 'Applications of Artificial Intelligence in Economics and Finance' at the '2003 International Conference on Artificial Intelligence' (IC-AI '03) held at the Monte Carlo Resort, Las Vegas, Nevada, USA, June 23-26 2003. The special session, organised by Jane Binner, Graham Kendall and Shu-Heng Chen, was presented in order to draw attention to the tremendous diversity and richness of the applications of artificial intelligence to problems in Economics and Finance. This volume should appeal to economists interested in adopting an interdisciplinary approach to the study of economic problems, computer scientists who are looking for potential applications of artificial intelligence and practitioners who are looking for new perspectives on how to build models for everyday operations. There are still many important Artificial Intelligence disciplines yet to be covered. Among them are the methodologies of independent component analysis, reinforcement learning, inductive logical programming, classifier systems and Bayesian networks, not to mention many ongoing and highly fascinating hybrid systems. A way to make up for their omission is to visit this subject again later. We certainly hope that we can do so in the near future with another volume of "Applications of Artificial Intelligence in Economics and Finance".

The editors are pleased to offer the following papers to the reader in recognition and appreciation of the contributions to our literature made by Robert Engle and Sir Clive Granger, winners of the 2003 Nobel Prize in Economics. The basic themes of this part of Volume 20 of "Advances in Econometrics" are time varying betas of the capital asset pricing model, analysis of predictive densities of nonlinear models of stock returns, modelling multivariate dynamic correlations, flexible seasonal time series models, estimation of long-memory time series models, the application of the technique of boosting in volatility forecasting, the use of different time scales in GARCH modelling, out-of-sample evaluation of the Fed Model in stock price valuation, structural change as an alternative to long memory, the use of smooth transition auto-regressions in stochastic volatility modelling, the analysis of the balanced-ness of regressions analyzing Taylor-Type rules of the Fed Funds rate, a mixture-of-experts approach for the estimation of stochastic volatility, a modern assessment of Clives first published paper on Sunspot activity, and a new class of models of tail-dependence in time series subject to jumps. This Series aids in the diffusion of new econometric techniques. Emphasis is placed on expositional clarity and ease of assimilation for readers who are unfamiliar with a given topic of a volume. It illustrates new concepts.

The editors are pleased to offer the following papers to the reader in recognition and appreciation of the contributions to our literature made by Robert Engle and Sir Clive Granger, winners of the 2003 Nobel Prize in Economics. The basic themes of this part of "Volume 20 of Advances in Econometrics" are time varying betas of the capital asset pricing model, analysis of predictive densities of nonlinear models of stock returns, modelling multivariate dynamic correlations, flexible seasonal time series models, estimation of long-memory time series models, the application of the technique of boosting in volatility forecasting, the use of different time scales in GARCH modelling, out-of-sample evaluation of the Fed Model in stock price valuation, structural change as an alternative to long memory, the use of smooth transition auto-regressions in stochastic volatility modelling, the analysis of the balanced-ness of regressions analyzing Taylor-Type rules of the Fed Funds rate, a mixture-of-experts approach for the estimation of stochastic volatility, a modern assessment of Clives first published paper on Sunspot activity, and a new class of models of tail-dependence in time series subject to jumps. This series aids in the diffusion of new econometric techniques. Emphasis is placed on expositional clarity and ease of assimilation for readers who are unfamiliar with a given topic of a volume. It illustrates new concepts.

The estimation of the effects of treatments endogenous variables representing everything from individual participation in a training program to national participation in a World Bank loan program has occupied much of the theoretical and applied econometric research literatures in recent years. This volume brings together a diverse collection of papers on this important topic by leaders in the field from around the world. Some of the papers offer new theoretical contributions on various estimation techniques and others provide timely empirical applications illustrating the benefits of these and other methods. All of the papers share two common themes. First, as different estimators estimate different treatment effect parameters, it is vital to know what you are estimating and to know to whom the estimate applies. Second, as different estimators require different identification assumptions, it is crucial to understand the assumptions underlying each estimator. In empirical applications, the researcher must also make the case that the assumptions hold based on the available data and the institutional context. The theoretical contributions range over a variety of different estimators drawn from both statistics and econometrics, including matching and other non-parametric methods, panel methods, instrumental variables, methods based on hazard rate models and principal stratification, and they draw upon both the Bayesian and classical statistical traditions. The empirical contributions focus mainly on the evaluation of active labor market programs in Europe and the United States, but also examine of the effect of parenthood on wages and of the number of children on child health. It contains both theoretical and empirical contributions. It includes examples from both Europe and the US.

This volume is a collection of methodological developments and applications of simulation-based methods that were presented at a workshop at Louisiana State University in November, 2009. The first two papers are extensions of the GHK simulator: one reconsiders the computation of the probabilities in a discrete choice model while another example uses an adaptive version of sparse-grids integration (SGI) instead of simulation. Two studies are focused specifically on the methodology: the first compares the performance of the maximum-simulated likelihood (MSL) approach with a proposed composite marginal likelihood (CML) approach in multivariate ordered-response situations, while the second examines methods of testing for the presence of heterogeneity in the heterogeneity model. Further topics examined include: education savings accounts, parent contributions and education attainment; estimating the effect of exchange rate flexibility on financial account openness; estimating a fractional response model with a count endogenous regressor; and modelling and forecasting volatility in a bayesian approach.

This Book Set consists of:*9781780525242 - Missing Data Methods: Cross-sectional Methods and Applications (Part A)*9781780525266 - Missing Data Methods: Time-series Methods and Applications (Part B)The papers in this volume cover topics in the econometric approach to missing data problems. Data can be missing because an individual failed to answer a question or because the laws of nature imply that an individual can only follow one of several possible paths. We refer to the first case as one of missing observations and to the second case as one of unobserved outcomes. This volume reflects the fact that econometricians have been very active in the development and use of methods for unobserved outcomes. The huge interest in these methods caused the volume to be split into parts A and B. The 12 chapters in Part A discuss cross-sectional methods. All the papers either derive, survey, or evaluate new methods for handling missing-data problems. Per the current interest in econometrics, 11 of the 12 papers address unobserved-outcome problems. The 4 chapters in Part B discuss time-series methods. Two chapters comprehensively survey the use of Markov switching models in finance. The third chapter surveys discrete-time and continuous-time models for volatility. The fourth chapter derives a new imputation method for nonstationary panel-data models and compares it to existing methods.

Volume 27 of "Advances in Econometrics", entitled "Missing Data Methods", contains 16 chapters authored by specialists in the field, covering topics such as: Missing-Data Imputation in Nonstationary Panel Data Models; Markov Switching Models in Empirical Finance; Bayesian Analysis of Multivariate Sample Selection Models Using Gaussian Copulas; Consistent Estimation and Orthogonality; and Likelihood-Based Estimators for Endogenous or Truncated Samples in Standard Stratified Sampling.

This volume of Advances in Econometrics contains articles that examine key topics in the modeling and estimation of dynamic stochastic general equilibrium (DSGE) models. Because DSGE models combine micro- and macroeconomic theory with formal econometric modeling and inference, over the past decade they have become an established framework for analyzing a variety of issues in empirical macroeconomics. The research articles make contributions in several key areas in DSGE modeling and estimation. In particular, papers cover the modeling and role of expectations, the study of optimal monetary policy in two-country models, and the problem of non-invertibility. Other interesting areas of inquiry include the analysis of parameter identification in new open economy macroeconomic models and the modeling of trend inflation shocks. The second part of the volume is devoted to articles that offer innovations in econometric methodology. These papers advance new techniques for addressing major inferential problems and include discussion and applications of Laplace-type, frequency domain, empirical likelihood and method of moments estimators.

The 'Advances in Econometrics' series aims to publish annual original scholarly econometrics papers on designated topics with the intention of expanding the use of developed and emerging econometric techniques by disseminating ideas on the theory and practice of econometrics throughout the empirical economic, business and social science literature.

The 30th Volume of Advances in Econometrics is in honor of the two individuals whose hard work has helped ensure thirty successful years of the series, Thomas Fomby and R. Carter Hill. This volume began with a history of the Advances series by Asli Ogunc and Randall Campbell summarizing the prior volumes. Tom Fomby and Carter Hill both provide discussions of the role of Advances over the years. The remaining articles include contributions by a number of authors who have played key roles in the series over the years and in the careers of Fomby and Hill. Overall, this leads to a more diverse mix of papers than a typical volume of Advances in Econometrics.

Vector autoregressive (VAR) models are among the most widely used econometric tools in the fields of macroeconomics and financial economics. Much of what we know about the response of the economy to macroeconomic shocks and about how various shocks have contributed to the evolution of macroeconomic and financial aggregates is based on VAR models. VAR models also have been used successfully for economic and business forecasting, for modelling risk and volatility, and for the construction of forecast scenarios. Since the introduction of VAR models by C.A. Sims in 1980, the VAR methodology has continuously evolved. Even today important extensions and reinterpretations of the VAR framework are being developed. Examples include VAR models for mixed-frequency data, VAR models as approximations to DSGE models, factor-augmented VAR models, new tools for the identification of structural shocks in VAR models, panel VAR approaches, and time-varying parameter VAR models. This volume collects contributions from some of the leading VAR experts in the world on VAR methods and applications. Each chapter highlights and synthesizes a new development in this literature in a way that is accessible to practitioners, to graduate students, and to readers in other fields.

This volume of Advances in Econometrics focuses on recent developments in the use of structural econometric models in empirical economics. The papers in this volume are divided in to three broad groups. The first part looks at recent developments in the estimation of dynamic discrete choice models. This includes using new estimation methods for these models based on Euler equations, estimation using sieve approximation of high dimensional state space, the identification of Markov dynamic games with persistent unobserved state variables and developing test of monotone comparative static in models of multiple equilibria. The second part looks at recent advances in the area empirical matching models. The papers in this section look at developing estimators for matching models based on stability conditions, estimating matching surplus functions using generalized entropy functions, solving for the fixed point in the Choo-Siow matching model using a contraction mapping formulation. While the issue of incomplete, or partial identification of model parameters is touched upon in some of the foregoing chapters, two chapters focus on this issue, in the context of testing for monotone comparative statics in models with multiple equilibria, and estimation of supermodular games under the restrictions that players' strategies be rationalizable. The last group of three papers looks at empirical applications using structural econometric models. Two applications applies matching models to solve endogenous matching to the loan spread equation and to endogenize marriage in the collective model of intrahousehold allocation. Another applications looks at market power of condominium developers in the Japanese housing market in the 1990s.

These essays honor Professor Peter C.B. Phillips of Yale University and his many contributions to the field of econometrics. Professor Phillips's research spans many topics in econometrics including: non-stationary time series and panel models partial identification and weak instruments Bayesian model evaluation and prediction financial econometrics and finite-sample statistical methods and results. The papers in this volume reflect additions to and amplifications of many of Professor Phillips' research contributions. Some of the topics discussed in the volume include panel macro-econometric modeling, efficient estimation and inference in difference-in-difference models, limiting and empirical distributions of IV estimates when some of the instruments are endogenous, the use of stochastic dominance techniques to examine conditional wage distributions of incumbents and newly hired employees, long-horizon predictive tests in financial markets, new developments in information matrix testing, testing for co-integration in Markov switching error correction models, and deviation information criteria for comparing vector autoregressive models.

The volume contains articles that should appeal to readers with computational, modeling, theoretical, and applied interests. Methodological issues include parallel computation, Hamiltonian Monte Carlo, dynamic model selection, small sample comparison of structural models, Bayesian thresholding methods in hierarchical graphical models, adaptive reversible jump MCMC, LASSO estimators, parameter expansion algorithms, the implementation of parameter and non-parameter-based approaches to variable selection, a survey of key results in objective Bayesian model selection methodology, and a careful look at the modeling of endogeneity in discrete data settings. Important contemporary questions are examined in applications in macroeconomics, finance, banking, labor economics, industrial organization, and transportation, among others, in which model uncertainty is a central consideration.

Dynamic factor models (DFM) constitute an active and growing area of research, both in econometrics, in macroeconomics, and in finance. Many applications lie at the center of policy questions raised by the recent financial crises, such as the connections between yields on government debt, credit risk, inflation, and economic growth. This volume collects a key selection of up-to-date contributions that cover a wide range of issues in the context of dynamic factor modeling, such as specification, estimation, and application of DFMs. Examples include further developments in DFM for mixed-frequency data settings, extensions to time-varying parameters and structural breaks, for multi-level factors associated with subsets of variables, in factor augmented error correction models, and in many other related aspects. A number of contributions propose new estimation procedures for DFM, such as spectral expectation-maximization algorithms and Bayesian approaches. Numerous applications are discussed, including the dating of business cycles, implied volatility surfaces, professional forecaster survey data, and many more.

Volume 36 of Advances in Econometrics recognizes Aman Ullah's significant contributions in many areas of econometrics and celebrates his long productive career. The volume features original papers on the theory and practice of econometrics that is related to the work of Aman Ullah. Topics include nonparametric/semiparametric econometrics; finite sample econometrics; shrinkage methods; information/entropy econometrics; model specification testing; robust inference; panel/spatial models. Advances in Econometrics is a research annual whose editorial policy is to publish original research articles that contain enough details so that economists and econometricians who are not experts in the topics will find them accessible and useful in their research.

Advances in Econometrics is a research annual whose editorial policy is to publish original research articles that contain enough details so that economists and econometricians who are not experts in the topics will find them accessible and useful in their research. Volume 37 exemplifies this focus by highlighting key research from new developments in econometrics.

The Regression Discontinuity (RD) design is one of the most popular and credible research designs for program evaluation and causal inference. This volume 38 of Advances in Econometrics collects twelve innovative and thought-provoking contributions to the RD literature, covering a wide range of methodological and practical topics. Some chapters touch on foundational methodological issues such as identification, interpretation, implementation, falsification testing, estimation and inference, while others focus on more recent and related topics such as identification and interpretation in a discontinuity-in-density framework, empirical structural estimation, comparative RD methods, and extrapolation. These chapters not only give new insights for current methodological and empirical research, but also provide new bases and frameworks for future work in this area. This volume contributes to the rapidly expanding RD literature by bringing together theoretical and applied econometricians, statisticians, and social, behavioural and biomedical scientists, in the hope that these interactions will further spark innovative practical developments in this important and active research area.

This volume of Advances in Econometrics contains a selection of papers presented at the "Econometrics of Complex Survey Data: Theory and Applications" conference organized by the Bank of Canada, Ottawa, Canada, from October 19-20, 2017. The papers included in this volume span a range of methodological and practical topics including survey collection comparisons, imputation mechanisms, the bootstrap, nonparametric techniques, specification tests, and empirical likelihood estimation using complex survey data. For academics and students with an interest in econometrics and the ways in which complex survey data can be used and evaluated, this volume is essential.

Volume 40 in the Advances in Econometrics series features twenty-three chapters that are split thematically into two parts. Part A presents novel contributions to the analysis of time series and panel data with applications in macroeconomics, finance, cognitive science and psychology, neuroscience, and labor economics. Part B examines innovations in stochastic frontier analysis, nonparametric and semiparametric modeling and estimation, A/B experiments, big-data analysis, and quantile regression.  Individual chapters, written by both distinguished researchers and promising young scholars, cover many important topics in statistical and econometric theory and practice. Papers primarily, though not exclusively, adopt Bayesian methods for estimation and inference, although researchers of all persuasions should find considerable interest in the chapters contained in this work. The volume was prepared to honor the career and research contributions of Professor Dale J. Poirier.  For researchers in econometrics, this volume includes the most up-to-date research across a wide range of topics.

Including contributions spanning a variety of theoretical and applied topics in econometrics, this volume of Advances in Econometrics is published in honour of Cheng Hsiao.In the first few chapters of this book, new theoretical panel and time series results are presented, exploring JIVE estimators, HAC, HAR and various sandwich estimators, as well as asymptotic distributions for using information criteria to distinguish between the unit root model and explosive models. Other chapters address topics such as structural breaks or growth empirics; auction models; and semiparametric methods testing for common vs. individual trends. Three chapters provide novel empirical approaches to applied problems, such as estimating the impact of survey mode on responses, or investigating how cross-sectional and spatial dependence of mortgages varies by default rates and geography. In the final chapters, Cheng Hsiao offers a forward-focused discussion of the role of big data in economics. For any researcher of econometrics, this is an unmissable volume of the most current and engaging research in the field.

The collection in Volume 43 Part A of Advances in Econometrics serves as a tribute to Professor M. Hashem Pesaran. Hashem is one of the most innovative, influential, and productive econometricians of his generation, with over 200 papers published in leading scientific journals to his credit along with highly influential books on both theoretical and applied topics, significantly pushing forward the frontiers of knowledge in econometrics and economics. Thanks to his profound and pioneering work on theoretical and empirical questions, the economics profession has gained a much better understanding of both the power and limitations of econometric analysis.Consistent with Hashem’s contributions, this volume comprises of chapters on a variety of topics covering prediction and macroeconomic modelling. The list of topics includes studies on Bayesian Quantile regression methods, forecasting implications from the economic impact of global warming, assessment of DSGE models, and parameter estimation in the presence of multiple breaks.

The collection of chapters in Volume 43 Part B of Advances in Econometrics serves as a tribute to Professor M. Hashem Pesaran. Hashem is one of the most innovative, influential, and productive econometricians of his generation, with over 200 papers published in leading scientific journals to his credit along with highly influential books on both theoretical and applied topics, significantly pushing forward the frontiers of knowledge in econometrics and economics. Thanks to his profound and pioneering work on theoretical and empirical questions, the economics profession has gained a much better understanding of both the power and limitations of econometric analysis.Reflecting the diversity of Hashem’s many contributions, this volume includes chapters on a wide variety of topics, including panel modelling, micro applications, and econometric methodology. The long list of topics includes studies analysing multiple treatment effects in panels, heterogeneity and aggregation, an exploration of the Orthogonal to Backwards Means (OBM) estimator, and an examination of potential reasons for anaemic productivity growth in Italy using recent dynamic heterogeneous panel data methods developed by Hashem Pesaran and his co-authors.