This book assembles key texts in the theory and applications of the Structural Econometric Time Series Analysis (SEMTSA) approach. The theory and applications of these procedures to a variety of econometric modeling and forecasting problems as well as Bayesian and non-Bayesian testing, shrinkage estimation and forecasting procedures are presented and applied. Finally, attention is focused on the effects of disaggregation on forecasting precision.

This book discusses the developments in trade theories, including new-new trade models that account for firm level trade flows, trade growth accounting using inverse gravity models (including distortions in gravity models), the impact of trade liberalization under the aegis of regional and multilateral liberalization efforts of economies using partial and general equilibrium analysis, methodologies of constructing ad valorem equivalents of non-tariff barriers, volatility spillover effects of financial and exchange rate markets. The main purpose of the book is to guide researchers working in the area of international trade, especially focused on empirical analysis of trade policy issues by updating their knowledge on issues related to trade theory, empirical methods, and their applications. The book would prove useful for policy makers, academicians, and researchers.

Time series econometrics is a rapidly evolving field. Particularly, the cointegration revolution has had a substantial impact on applied analysis. Hence, no textbook has managed to cover the full range of methods in current use and explain how to proceed in applied domains. This gap in the literature motivates the present volume. The methods are sketched out, reminding the reader of the ideas underlying them and giving sufficient background for empirical work. The treatment can also be used as a textbook for a course on applied time series econometrics. Topics include: unit root and cointegration analysis, structural vector autoregressions, conditional heteroskedasticity and nonlinear and nonparametric time series models. Crucial to empirical work is the software that is available for analysis. New methodology is typically only gradually incorporated into existing software packages. Therefore a flexible Java interface has been created, allowing readers to replicate the applications and conduct their own analyses.

This handbook presents emerging research exploring the theoretical and practical aspects of econometric techniques for the financial sector and their applications in economics. By doing so, it offers invaluable tools for predicting and weighing the risks of multiple investments by incorporating data analysis. Throughout the book the authors address a broad range of topics such as predictive analysis, monetary policy, economic growth, systemic risk and investment behavior. This book is a must-read for researchers, scholars and practitioners in the field of economics who are interested in a better understanding of current research on the application of econometric methods to financial sector data.

Applied Econometrics: A Practical Guide is an extremely user-friendly and application-focused book on econometrics. Unlike many econometrics textbooks which are heavily theoretical on abstractions, this book is perfect for beginners and promises simplicity and practicality to the understanding of econometric models. Written in an easy-to-read manner, the book begins with hypothesis testing and moves forth to simple and multiple regression models. It also includes advanced topics:

Endogeneity and Two-stage Least Squares

Simultaneous Equations Models

Panel Data Models

Qualitative and Limited Dependent Variable Models

Vector Autoregressive (VAR) Models

Autocorrelation and ARCH/GARCH Models

Unit Root and Cointegration

The book also illustrates the use of computer software (EViews, SAS and R) for economic estimating and modeling. Its practical applications make the book an instrumental, go-to guide for solid foundation in the fundamentals of econometrics. In addition, this book includes excerpts from relevant articles published in top-tier academic journals. This integration of published articles helps the readers to understand how econometric models are applied to real-world use cases.

Table of Contents

1. Review of Estimation and Hypothesis Tests

2. Simple Linear Regression Models

3. Multiple Linear Regression Models

4. Dummy Explanatory Variables

5. More on Multiple Regression Analysis

6. Endogeneity and Two-Stage Least Squares Estimation

7. Models for Panel Data

8. Simultaneous Equations Models

9. Vector Autoregressive (VAR) Models

10. Autocorrelation and ARCH/GARCH

11. Unit Root, Cointegration and Error Correction Model

12. Qualitative and Limited Dependent Variable Models

In the last 20 years, econometric theory on panel data has developed rapidly, particularly for analyzing common behaviors among individuals over time. Meanwhile, the statistical methods employed by applied researchers have not kept up-to-date. This book attempts to fill in this gap by teaching researchers how to use the latest panel estimation methods correctly. Almost all applied economics articles use panel data or panel regressions. However, many empirical results from typical panel data analyses are not correctly executed. This book aims to help applied researchers to run panel regressions correctly and avoid common mistakes. The book explains how to model cross-sectional dependence, how to estimate a few key common variables, and how to identify them. It also provides guidance on how to separate out the long-run relationship and common dynamic and idiosyncratic dynamic relationships from a set of panel data. Aimed at applied researchers who want to learn about panel data econometrics by running statistical software, this book provides clear guidance and is supported by a full range of online teaching and learning materials. It includes practice sections on MATLAB, STATA, and GAUSS throughout, along with short and simple econometric theories on basic panel regressions for those who are unfamiliar with econometric theory on traditional panel regressions.

Space is a crucial variable in any economic activity. Spatial Economics is the branch of economics that explicitly aims to incorporate the space dimension in the analysis of economic phenomena. From its beginning in the last century, Spatial Economics has contributed to the understanding of the economy by developing plenty of theoretical models as well as econometric techniques having the "space" as a core dimension of the analysis.This edited volume addresses the complex issue of Spatial Economics from an applied point of view. This volume is part of a more complex project including another edited volume (Spatial Economics Volume I: Theory) collecting original papers which address Spatial Economics from a theoretical perspective.

The volume examines the state-of-the-art of productivity and efficiency analysis. It brings together a selection of the best papers from the 10th North American Productivity Workshop. By analyzing world-wide perspectives on challenges that local economies and institutions may face when changes in productivity are observed, readers can quickly assess the impact of productivity measurement, productivity growth, dynamics of productivity change, measures of labor productivity, measures of technical efficiency in different sectors, frontier analysis, measures of performance, industry instability and spillover effects. The contributions in this volume focus on the theory and application of economics, econometrics, statistics, management science and operational research related to problems in the areas of productivity and efficiency measurement. Popular techniques and methodologies including stochastic frontier analysis and data envelopment analysis are represented. Chapters also cover broader issues related to measuring, understanding, incentivizing and improving the productivity and performance of firms, public services, and industries.

High-frequency trading is an algorithm-based computerized trading practice that allows firms to trade stocks in milliseconds. Over the last fifteen years, the use of statistical and econometric methods for analyzing high-frequency financial data has grown exponentially. This growth has been driven by the increasing availability of such data, the technological advancements that make high-frequency trading strategies possible, and the need of practitioners to analyze these data. This comprehensive book introduces readers to these emerging methods and tools of analysis.

Yacine Ait-Sahalia and Jean Jacod cover the mathematical foundations of stochastic processes, describe the primary characteristics of high-frequency financial data, and present the asymptotic concepts that their analysis relies on. Ait-Sahalia and Jacod also deal with estimation of the volatility portion of the model, including methods that are robust to market microstructure noise, and address estimation and testing questions involving the jump part of the model. As they demonstrate, the practical importance and relevance of jumps in financial data are universally recognized, but only recently have econometric methods become available to rigorously analyze jump processes.

Ait-Sahalia and Jacod approach high-frequency econometrics with a distinct focus on the financial side of matters while maintaining technical rigor, which makes this book invaluable to researchers and practitioners alike."

Adonis Yatchew provides simple and flexible (nonparametric) techniques for analyzing regression data. He includes a series of empirical examples with the estimation of Engel curves and equivalence scales, scale economies, household gasoline consumption, housing prices, option prices and state price density estimation. The book is of interest to a broad range of economists including those working in industrial organization, labor, development, and urban, energy and financial economics.

In the light of better and more detailed administrative databases, this open access book provides statistical tools for evaluating the effects of public policies advocated by governments and public institutions. Experts from academia, national statistics offices and various research centers present modern econometric methods for an efficient data-driven policy evaluation and monitoring, assess the causal effects of policy measures and report on best practices of successful data management and usage. Topics include data confidentiality, data linkage, and national practices in policy areas such as public health, education and employment. It offers scholars as well as practitioners from public administrations, consultancy firms and nongovernmental organizations insights into counterfactual impact evaluation methods and the potential of data-based policy and program evaluation.

Presenting an economic perspective of deforestation in the Brazilan Amazon, this study utilizes economic and ecological data from 1970 to 1996. It examines the extent to which land clearing promotes economic activity and growth and analyzes policies such as road building and subsidized credit. It explores whether the economic benefits of land clearing surpass the ecological costs and considers the viability of extractivism as an alternative to deforestation.

This book presents the principles and methods for the practical analysis and prediction of economic and financial time series. It covers decomposition methods, autocorrelation methods for univariate time series, volatility and duration modeling for financial time series, and multivariate time series methods, such as cointegration and recursive state space modeling. It also includes numerous practical examples to demonstrate the theory using real-world data, as well as exercises at the end of each chapter to aid understanding. This book serves as a reference text for researchers, students and practitioners interested in time series, and can also be used for university courses on econometrics or computational finance.

This Handbook takes an econometric approach to the foundations of economic performance analysis. The focus is on the measurement of efficiency, productivity, growth and performance. These concepts are commonly measured residually and difficult to quantify in practice. In real-life applications, efficiency and productivity estimates are often quite sensitive to the models used in the performance assessment and the methodological approaches adopted by the analysis. The Palgrave Handbook of Performance Analysis discusses the two basic techniques of performance measurement - deterministic benchmarking and stochastic benchmarking - in detail, and addresses the statistical techniques that connect them. All chapters include applications and explore topics ranging from the output/input ratio to productivity indexes and national statistics.

This book explores how econometric modelling can be used to provide valuable insight into international housing markets. Initially describing the role of econometrics modelling in real estate market research and how it has developed in recent years, the book goes on to compare and contrast the impact of various macroeconomic factors on developed and developing housing markets. Explaining the similarities and differences in the impact of financial crises on housing markets around the world, the author's econometric analysis of housing markets across the world provides a broad and nuanced perspective on the impact of both international financial markets and local macro economy on housing markets. With discussion of countries such as China, Germany, UK, US and South Africa, the lessons learned will be of interest to scholars of Real Estate economics around the world.

This book systematically provides a prospective integrated approach for complexity social science in its view of statistical physics and mathematics, with an impressive collection of the knowledge and expertise of leading researchers from all over the world. The book mainly covers both finitary methods of statistical equilibrium and data-driven analysis by econophysics. The late Professor Masanao Aoki of UCLA, who passed away at the end of July 2018, in his later years dedicated himself to the reconstruction of macroeconomics mainly in terms of statistical physics. Professor Aoki, who was already an IEEE fellow, was also named an Econometric Society Fellow in 1979. Until the early 1990s, however, his contributions were focused on the new developments of a novel algorithm for the time series model and their applications to economic data. Those contributions were undoubtedly equivalent to the Nobel Prize-winning work of Granger's "co-integration method". After the publications of his New Approaches to Macroeconomic Modeling and Modeling Aggregate Behavior and Fluctuations in Economics, both published by Cambridge University Press, in 1996 and 2002, respectively, his contributions rapidly became known and spread throughout the field. In short, these new works challenged econophysicists to develop evolutionary stochastic dynamics, multiple equilibria, and externalities as field effects and revolutionized the stochastic views of interacting agents. In particular, the publication of Reconstructing Macroeconomics, also by Cambridge University Press (2007), in cooperation with Hiroshi Yoshikawa, further sharpened the process of embodying "a perspective from statistical physics and combinatorial stochastic processes" in economic modeling. Interestingly, almost concurrently with Prof. Aoki's newest development, similar approaches were appearing. Thus, those who were working in the same context around the world at that time came together, exchanging their results during the past decade. In memory of Prof. Aoki, this book has been planned by authors who followed him to present the most advanced outcomes of his heritage.

Space is a crucial variable in any economic activity. Spatial Economics is the branch of economics that explicitly aims to incorporate the space dimension in the analysis of economic phenomena. From its beginning in the last century, Spatial Economics has contributed to the understanding of the economy by developing plenty of theoretical models as well as econometric techniques having the "space" as a core dimension of the analysis. This edited volume addresses the complex issue of Spatial Economics from a theoretical point of view. This volume is part of a more complex project including another edited volume (Spatial Economics Volume II: Applications) collecting original papers which address Spatial Economics from an applied perspective.

The statistical models confronting econometricians are complicated in nature so it is no easy task to apply the procedures recommended by classical statisticians to such models. This book presents the reader with mathematical tools drawn from matrix calculus and zero-one matrices and demonstrates how the use of their tools greatly facilitates such applications in a sequence of linear econometric models of increasing statistical complexity. The book differs from others in that the matrix calculus results are derived from a few basic rules which are generalizations of the rules used in ordinary calculus. Moreover the properties of several new zero-one matrices are investigated.

This book surveys the state-of-the-art in efficiency and productivity analysis, examining advances in the analytical foundations and empirical applications. The analytical techniques developed in this book for efficiency provide alternative ways of defining optimum outcome sets, typically as a (technical) production frontier or as an (economic) cost, revenue or profit frontier, and alternative ways of measuring efficiency relative to an appropriate frontier. Simultaneously, the analytical techniques developed for efficiency analysis extend directly to productivity analysis, thereby providing alternative methods for estimating productivity levels, and productivity change through time or productivity variation across producers. This book includes chapters using data envelopment analysis (DEA) or stochastic frontier analysis (SFA) as quantitative techniques capable of measuring efficiency and productivity. Across the book's 15 chapters, it broadly extends into popular application areas including agriculture, banking and finance, and municipal performance, and relatively new application areas including corporate social responsibility, the value of intangible assets, land consolidation, and the measurement of economic well-being. The chapters also cover topics such as permutation tests for production frontier shifts, new indices of total factor productivity, and also randomized controlled trials and production frontiers.

This textbook for master programs in economics offers a comprehensive overview of microeconomics. It employs a carefully graded approach where basic game theory concepts are already explained within the simpler decision framework. The unavoidable mathematical content is supplied when needed, not in an appendix. The book covers a lot of ground, from decision theory to game theory, from bargaining to auction theory, from household theory to oligopoly theory, and from the theory of general equilibrium to regulation theory. Additionally, cooperative game theory is introduced. This textbook has been recommended and developed for university courses in Germany, Austria and Switzerland.

This book, and its companion volume in the Econometric Society Monographs series (ESM number 33), present a collection of papers by Clive W. J. Granger. His contributions to economics and econometrics, many of them seminal, span more than four decades and touch on all aspects of time series analysis. The papers assembled in this volume explore topics in spectral analysis, seasonality, nonlinearity, methodology, and forecasting. Those in the companion volume investigate themes in causality, integration and cointegration, and long memory. The two volumes contain the original articles as well as an introduction written by the editors.

The book comprises three chapters, with each chapter assigned various type data such as time series data, cross sectional data and panel data. The purpose of this book is to explore the economic and social determinant factors of fertility. Unlike many previous empirical analyses of fertility and the related demographic events, this research has three characteristics. The first is that the relationship between fertility and labor participation by females is thoroughly considered, with much discussion about the structural change between those factors. The second is that time series analysis such as the Bayesian vector autoregressive (BVAR) model or co-integration concepts is applied to explore the determinant factors of fertility. The third is that the effectiveness of public policies related to improve fertility is confirmed. In recent years, micro-econometric analysis has become popular; however, this book takes another approach from the perspective of macro- or semi-macro-econometrics.

Financial data are typically characterised by a time-series and cross-sectional dimension. Accordingly, econometric modelling in finance requires appropriate attention to these two - or occasionally more than two - dimensions of the data. Panel data techniques are developed to do exactly this. This book provides an overview of commonly applied panel methods for financial applications, including popular techniques such as Fama-MacBeth estimation, one-way, two-way and interactive fixed effects, clustered standard errors, instrumental variables, and difference-in-differences. Panel Methods for Finance: A Guide to Panel Data Econometrics for Financial Applications by Marno Verbeek offers the reader: Focus on panel methods where the time dimension is relatively small A clear and intuitive exposition, with a focus on implementation and practical relevance Concise presentation, with many references to financial applications and other sources Focus on techniques that are relevant for and popular in empirical work in finance and accounting Critical discussion of key assumptions, robustness, and other issues related to practical implementation

This collection brings together important contributions by leading econometricians on parametric approaches to qualitative and sample selection models, nonparametric and semi-parametric approaches to qualitative and sample selection models, and nonlinear estimation of cross-sectional and time series models. The advances achieved here can have important bearing on the choice of methods and analytical techniques in applied research. The collection of papers is dedicated to Professor Takeshi Amemiya in view of his path-breaking contributions to econometrics and statistics.

Stochastic differential equations are differential equations whose solutions are stochastic processes. They exhibit appealing mathematical properties that are useful in modeling uncertainties and noisy phenomena in many disciplines. This book is motivated by applications of stochastic differential equations in target tracking and medical technology and, in particular, their use in methodologies such as filtering, smoothing, parameter estimation, and machine learning. It builds an intuitive hands-on understanding of what stochastic differential equations are all about, but also covers the essentials of Ito calculus, the central theorems in the field, and such approximation schemes as stochastic Runge-Kutta. Greater emphasis is given to solution methods than to analysis of theoretical properties of the equations. The book's practical approach assumes only prior understanding of ordinary differential equations. The numerous worked examples and end-of-chapter exercises include application-driven derivations and computational assignments. MATLAB/Octave source code is available for download, promoting hands-on work with the methods.