The book, Sustainability and Resources: Theoretical Issues in Dynamic Economics, presents a collection of mathematical models dealing with sustainability and resource management.The focus in Part A is on harvesting renewable resources, while Part B explores the optimal extraction of exhaustible resources. Part C introduces models dealing with uncertainty. Some are descriptive models; others have deep roots in intertemporal welfare economics. The tools of dynamic optimization developed in the 1960s are used in a formal, rigorous presentation to address wide-ranging issues that have appeared in academic research as well as policy debates on the world stage.The book also provides a self-contained treatment that is accessible to advanced undergraduate and graduate students, who are interested in dynamic models of resource allocation and social welfare, resource management, and applications of optimization theory and methods of probability theory to economics. For researchers in dynamic economics, it will be an invaluable source for formal treatment of substantive macroeconomic issues raised by policymakers. The part dealing with uncertainty and random dynamical systems (largely developed by the author and his collaborators) exposes the reader to contemporary frontiers of research on stochastic processes with novel applications to economic problems.

This Open Access Brief presents the KAPSARC Global Energy Macroeconometric Model (KGEMM). KGEMM is a policy analysis tool for examining the impacts of domestic policy measures and global economic and energy shocks on the Kingdom of Saudi Arabia. The model has eight blocks (real sector, fiscal, monetary, external sector, price, labor and wages, energy, population, and age cohorts) that interact with each other to represent the Kingdom's macroeconomy and energy linkages. It captures New Keynesian demand-side features anchored to medium-run equilibrium and long-run aggregate supply. It applies a cointegration and equilibrium correction modeling (ECM) methodology to time series data to estimate the model's behavioral equations in the framework of Autometrics, a general-to-specific econometric modeling strategy. Hence, the model combines 'theory-driven' approach with 'data-driven' approach. The Brief begins with an introduction to the theoretical framework of the model and the KGEMM methodology and then walks the reader through the structure of the model and its behavioral equations. The book closes with simulations showing the application of the model. Providing a detailed introduction to a cutting-edge, robust predictive model, this Brief will be of great use to researchers and policymakers interested in macroeconomics, energy economics, econometrics, and more specifically, the economy of Saudi Arabia.

This book is the first of its kind to systematically analyze and apply Lim Chong Yah's S-Curve Hypothesis to the various facets of economic growth and economic transition. By augmenting the mathematical and economical sophistication of the hypothesis, this book extends the S-Curve hypothesis to provide further insight into economic growth and transition.

It also utilizes a construction of a stochastic growth model to provide the microeconomic foundation for the S-Curve hypothesis. This model resolves the puzzle of why some developing countries experience economic take-off, while others do not. The book analyzes and extends discussion on the S-Curve, and also applies the S-Curve hypothesis to predict long-term growth in Japan and Singapore. It serves as an excellent resource for people interested in Lim's growth theory.

In this Element and its accompanying second Element, A Practical Introduction to Regression Discontinuity Designs: Extensions, Matias Cattaneo, Nicolas Idrobo, and Rociio Titiunik provide an accessible and practical guide for the analysis and interpretation of regression discontinuity (RD) designs that encourages the use of a common set of practices and facilitates the accumulation of RD-based empirical evidence. In this Element, the authors discuss the foundations of the canonical Sharp RD design, which has the following features: (i) the score is continuously distributed and has only one dimension, (ii) there is only one cutoff, and (iii) compliance with the treatment assignment is perfect. In the second Element, the authors discuss practical and conceptual extensions to this basic RD setup.

Market Analysis for Real Estate is a comprehensive introduction to how real estate markets work and the analytical tools and techniques that can be used to identify and interpret market signals. The markets for space and varied property assets, including residential, office, retail, and industrial, are presented, analyzed, and integrated into a complete understanding of the role of real estate markets within the workings of contemporary urban economies. Unlike other books on market analysis, the economic and financial theory in this book is rigorous and well integrated with the specifics of the real estate market. Furthermore, it is thoroughly explained as it assumes no previous coursework in economics or finance on the part of the reader. The theoretical discussion is backed up with numerous real estate case study examples and problems, which are presented throughout the text to assist both student and teacher.

Including discussion questions, exercises, several web links, and online slides, this textbook is suitable for use on a variety of degree programs in real estate, finance, business, planning, and economics at undergraduate and MSc/MBA level. It is also a useful primer for professionals in these disciplines.

Table of Contents

PART A: INTRODUCTION 1. Market Analysis In Perspective 2. Real Estate Economics PART B: METROPOLITAN GROWTH ANALYSIS 3. Metropolitan Growth Patterns 4. Analyzing Metropolitan Economies PART C: ANALYZING RESIDENTIAL REAL ESTATE MARKETS 5. Residential Real Estate Markets 6. Macroeconomic Analysis of Residential Real Estate Markets: Accounting Techniques 7. Macroeconomic Analysis of Residential Real Estate Markets: The Basics of the Econometric Approach 8. Macroeconomic Analysis of Residential Real Estate Markets: Applying the Econometric Approach 9. Analyzing Residential Projects: A Micro Perspective 10. Analysis of Residential Real Estate Markets: An Example PART D: ANALYZING THE MARKET FOR RETAIL SPACE 11. Retail Markets and Retail Market Studies 12. Analyzing the Market for Retail Space 13. Analyzing the Market for Retail Space: Synthesis and Market Studies PART E: OFFICE MARKET ANALYSIS 14. The Market for Office Space 15. Office Market Analysis: A Macro Perspective 16. Micro Analysis of Office Markets 17. Office Market Analysis: Synthesis and Market Studies PART F: INDUSTRIAL MARKET ANALYSIS 18. Industrial Space Market PART G: DATA SOURCES 19. Data Sources for Real Estate Market Analysis

This book unifies and extends the definition and measurement of economic efficiency and its use as a real-life benchmarking technique for actual organizations. Analytically, the book relies on the economic theory of duality as guiding framework. Empirically, it shows how the alternative models can be implemented by way of Data Envelopment Analysis. An accompanying software programmed in the open-source Julia language is used to solve the models. The package is a self-contained set of functions that can be used for individual learning and instruction. The source code, associated documentation, and replication notebooks are available online. The book discusses the concept of economic efficiency at the firm level, comparing observed to optimal economic performance, and its decomposition according to technical and allocative criteria. Depending on the underlying technical efficiency measure, economic efficiency can be decomposed multiplicatively or additively. Part I of the book deals with the classic multiplicative approach that decomposes cost and revenue efficiency based on radial distance functions. Subsequently, the book examines how these partial approaches can be expanded to the notion of profitability efficiency, considering both the input and output dimensions of the firm, and relying on the generalized distance function for the measurement of technical efficiency. Part II is devoted to the recent additive framework related to the decomposition of economic inefficiency defined in terms of cost, revenue, and profit. The book presents economic models for the Russell and enhanced graph Russell measures, the weighted additive distance function, the directional distance function, the modified directional distance function, and the Hoelder distance function. Each model is presented in a separate chapter. New approaches that qualify and generalize previous results are also introduced in the last chapters, including the reverse directional distance function and the general direct approach. The book concludes by highlighting the importance of benchmarking economic efficiency for all business stakeholders and recalling the main conclusions obtained from many years of research on this topic. The book offers different alternatives to measure economic efficiency based on a set of desirable properties and advises on the choice of specific economic efficiency models.

A complete resource for finance students, this textbook presents the most common empirical approaches in finance in a comprehensive and well-illustrated manner that shows how econometrics is used in practice, and includes detailed case studies to explain how the techniques are used in relevant financial contexts. Maintaining the accessible prose and clear examples of previous editions, the new edition of this best-selling textbook provides support for the main industry-standard software packages, expands the coverage of introductory mathematical and statistical techniques into two chapters for students without prior econometrics knowledge, and includes a new chapter on advanced methods. Learning outcomes, key concepts and end-of-chapter review questions (with full solutions online) highlight the main chapter takeaways and allow students to self-assess their understanding. Online resources include extensive teacher and student support materials, including EViews, Stata, R, and Python software guides.

Reflecting the fast pace and ever-evolving nature of the financial industry, the Handbook of High-Frequency Trading and Modeling in Finance details how high-frequency analysis presents new systematic approaches to implementing quantitative activities with high-frequency financial data. Introducing new and established mathematical foundations necessary to analyze realistic market models and scenarios, the handbook begins with a presentation of the dynamics and complexity of futures and derivatives markets as well as a portfolio optimization problem using quantum computers. Subsequently, the handbook addresses estimating complex model parameters using high-frequency data. Finally, the handbook focuses on the links between models used in financial markets and models used in other research areas such as geophysics, fossil records, and earthquake studies. The Handbook of High-Frequency Trading and Modeling in Finance also features: Contributions by well-known experts within the academic, industrial, and regulatory fields A well-structured outline on the various data analysis methodologies used to identify new trading opportunities Newly emerging quantitative tools that address growing concerns relating to high-frequency data such as stochastic volatility and volatility tracking; stochastic jump processes for limit-order books and broader market indicators; and options markets Practical applications using real-world data to help readers better understand the presented material The Handbook of High-Frequency Trading and Modeling in Finance is an excellent reference for professionals in the fields of business, applied statistics, econometrics, and financial engineering. The handbook is also a good supplement for graduate and MBA-level courses on quantitative finance, volatility, and financial econometrics. Ionut Florescu, PhD, is Research Associate Professor in Financial Engineering and Director of the Hanlon Financial Systems Laboratory at Stevens Institute of Technology. His research interests include stochastic volatility, stochastic partial differential equations, Monte Carlo Methods, and numerical methods for stochastic processes. Dr. Florescu is the author of Probability and Stochastic Processes, the coauthor of Handbook of Probability, and the coeditor of Handbook of Modeling High-Frequency Data in Finance, all published by Wiley. Maria C. Mariani, PhD, is Shigeko K. Chan Distinguished Professor in Mathematical Sciences and Chair of the Department of Mathematical Sciences at The University of Texas at El Paso. Her research interests include mathematical finance, applied mathematics, geophysics, nonlinear and stochastic partial differential equations and numerical methods. Dr. Mariani is the coeditor of Handbook of Modeling High-Frequency Data in Finance, also published by Wiley. H. Eugene Stanley, PhD, is William Fairfield Warren Distinguished Professor at Boston University. Stanley is one of the key founders of the new interdisciplinary field of econophysics, and has an ISI Hirsch index H=128 based on more than 1200 papers. In 2004 he was elected to the National Academy of Sciences. Frederi G. Viens, PhD, is Professor of Statistics and Mathematics and Director of the Computational Finance Program at Purdue University. He holds more than two dozen local, regional, and national awards and he travels extensively on a world-wide basis to deliver lectures on his research interests, which range from quantitative finance to climate science and agricultural economics. A Fellow of the Institute of Mathematics Statistics, Dr. Viens is the coeditor of Handbook of Modeling High-Frequency Data in Finance, also published by Wiley.

The past twenty years have seen an extraordinary growth in the use of quantitative methods in financial markets. Finance professionals now routinely use sophisticated statistical techniques in portfolio management, proprietary trading, risk management, financial consulting, and securities regulation. This graduate-level textbook is intended for PhD students, advanced MBA students, and industry professionals interested in the econometrics of financial modeling. The book covers the entire spectrum of empirical finance, including: the predictability of asset returns, tests of the Random Walk Hypothesis, the microstructure of securities markets, event analysis, the Capital Asset Pricing Model and the Arbitrage Pricing Theory, the term structure of interest rates, dynamic models of economic equilibrium, and nonlinear financial models such as ARCH, neural networks, statistical fractals, and chaos theory.

Each chapter develops statistical techniques within the context of a particular financial application. This exciting new text contains a unique and accessible combination of theory and practice, bringing state-of-the-art statistical techniques to the forefront of financial applications. Each chapter also includes a discussion of recent empirical evidence, for example, the rejection of the Random Walk Hypothesis, as well as problems designed to help readers incorporate what they have read into their own applications

Continuous-Time Models in Corporate Finance synthesizes four decades of research to show how stochastic calculus can be used in corporate finance. Combining mathematical rigor with economic intuition, Santiago Moreno-Bromberg and Jean-Charles Rochet analyze corporate decisions such as dividend distribution, the issuance of securities, and capital structure and default. They pay particular attention to financial intermediaries, including banks and insurance companies. The authors begin by recalling the ways that option-pricing techniques can be employed for the pricing of corporate debt and equity. They then present the dynamic model of the trade-off between taxes and bankruptcy costs and derive implications for optimal capital structure. The core chapter introduces the workhorse liquidity-management model--where liquidity and risk management decisions are made in order to minimize the costs of external finance. This model is used to study corporate finance decisions and specific features of banks and insurance companies. The book concludes by presenting the dynamic agency model, where financial frictions stem from the lack of interest alignment between a firm's manager and its financiers. The appendix contains an overview of the main mathematical tools used throughout the book. Requiring some familiarity with stochastic calculus methods, Continuous-Time Models in Corporate Finance will be useful for students, researchers, and professionals who want to develop dynamic models of firms' financial decisions.

It is impossible to understand modern economics without knowledge of the basic tools of gametheory and mechanism design. This book provides a graduate-level introduction to the economic modeling of strategic behavior. The goal is to teach Economics doctoral students the tools of game theory and mechanism design that all economists should know.

This second edition retains the positive features of being clearly written, well organized, and incorporating calculus in the text, while adding expanded coverage on game theory, experimental economics, and behavioural economics. It remains more focused and manageable than similar textbooks, and provides a concise yet comprehensive treatment of the core topics of microeconomics, including theories of the consumer and of the firm, market structure, partial and general equilibrium, and market failures caused by public goods, externalities and asymmetric information. The book includes helpful solved problems in all the substantive chapters, as well as over seventy new mathematical exercises and enhanced versions of the ones in the first edition. The authors make use of the book's full color with sharp and helpful graphs and illustrations. This mathematically rigorous textbook is meant for students at the intermediate level who have already had an introductory course in microeconomics, and a calculus course.

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.

Leonid Hurwicz (1917-2008) was a major figure in modern theoretical economics whose contributions over sixty-five years spanned at least five areas: econometrics, nonlinear programming, decision theory, microeconomic theory, and mechanism design. In 2007, at age ninety, he received the Nobel Memorial Prize in Economics (shared with Eric Maskin and Roger Myerson) for pioneering the field of mechanism design and incentive compatibility. Hurwicz made seminal contributions in the other areas as well. In non-linear programming, he contributed to the understanding of Lagrange-Kuhn-Tucker problems (along with co-authors Kenneth Arrowand Hirofumi Uzawa). In econometrics, the Hurwicz bias in the least-squares analysis of time series is a fundamental and commonly cited benchmark. In decision theory, the Hurwicz criterion for decision-making under ambiguity is routinely invoked, sometimes without a citation since his original paper was never published. In microeconomic theory, Hurwicz (along with Arrow and H.D. Block) initiated the study of stability of the market mechanism, and (with Uzawa) solved the classic integrability of demand problem, a core result in neoclassical consumer theory. While some of Hurwicz's work were published in journals, many remain scattered as chapters in books which are difficult to access; yet others were never published at all. The Collected Papers of Leonid Hurwicz is the first volume in a series of four that will bring his oeuvre in one place, to bring to light the totality of his intellectual output, to document his contribution to economics and the extent of his legacy, with the express purpose to make it easily available for future generations of researchers to build upon.

Today, most money is credit money, created by commercial banks. While credit can finance innovation, excessive credit can lead to boom/bust cycles, such as the recent financial crisis. This highlights how the organization of our monetary system is crucial to stability. One way to achieve this is by separating the unit of account from the medium of exchange and in pre-modern Europe, such a separation existed. This new volume examines this idea of monetary separation and this history of monetary arrangements in the North and Baltic Seas region, from the Hanseatic League onwards. This book provides a theoretical analysis of four historical cases in the Baltic and North Seas region, with a view to examining evolution of monetary arrangements from a new monetary economics perspective. Since the objective exhange value of money (its purchasing power), reflects subjective individual valuations of commodities, the author assesses these historical cases by means of exchange rates. Using theories from new monetary economics , the book explores how the units of account and their media of exchange evolved as social conventions, and offers new insight into the separation between the two. Through this exploration, it puts forward that money is a social institution, a clearing device for the settlement of accounts, and so the value of money, or a separate unit of account, ultimately results from the size of its network of users. The History of Money and Monetary Arrangements offers a highly original new insight into monetary arrangments as an evolutionary process. It will be of great interest to an international audience of scholars and students, including those with an interest in economic history, evolutionary economics and new monetary economics.

This text disputes the laissez-faire direction of both economic theory and practice that has gained prominence since the mid-1970s. Dissenting voices, the author argues, have been drowned out by a sea of circular arguments and complex mathematical models that ignore real-world conditions and disregard values that can't easily be turned into commodities. Included is an explanation of how some sectors of the economyrequire a blend of market, regulation and social outlay.

Microeconometrics Using Stata, Second Edition is an invaluable reference for researchers and students interested in applied microeconometric methods. Like previous editions, this text covers all the classic microeconometric techniques ranging from linear models to instrumental-variables regression to panel-data estimation to nonlinear models such as probit, tobit, Poisson, and choice models. Each of these discussions has been updated to show the most modern implementation in Stata, and many include additional explanation of the underlying methods. In addition, the authors introduce readers to performing simulations in Stata and then use simulations to illustrate methods in other parts of the book. They even teach you how to code your own estimators in Stata. The second edition is greatly expanded—the new material is so extensive that the text now comprises two volumes. In addition to the classics, the book now teaches recently developed econometric methods and the methods newly added to Stata. Specifically, the book includes entirely new chapters on duration models randomized control trials and exogenous treatment effects endogenous treatment effects models for endogeneity and heterogeneity, including finite mixture models, structural equation models, and nonlinear mixed-effects models spatial autoregressive models semiparametric regression lasso for prediction and inference Bayesian analysis Anyone interested in learning classic and modern econometric methods will find this the perfect companion. And those who apply these methods to their own data will return to this reference over and over as they need to implement the various techniques described in this book.

This text presents modern developments in time series analysis and focuses on their application to economic problems. The book first introduces the fundamental concept of a stationary time series and the basic properties of covariance, investigating the structure and estimation of autoregressive-moving average (ARMA) models and their relations to the covariance structure. The book then moves on to non-stationary time series, highlighting its consequences for modeling and forecasting and presenting standard statistical tests and regressions. Next, the text discusses volatility models and their applications in the analysis of financial market data, focusing on generalized autoregressive conditional heteroskedastic (GARCH) models. The second part of the text devoted to multivariate processes, such as vector autoregressive (VAR) models and structural vector autoregressive (SVAR) models, which have become the main tools in empirical macroeconomics. The text concludes with a discussion of co-integrated models and the Kalman Filter, which is being used with increasing frequency. Mathematically rigorous, yet application-oriented, this self-contained text will help students develop a deeper understanding of theory and better command of the models that are vital to the field. Assuming a basic knowledge of statistics and/or econometrics, this text is best suited for advanced undergraduate and beginning graduate students.

Panel data is a data type increasingly used in research in economics, social sciences, and medicine. Its primary characteristic is that the data variation goes jointly over space (across individuals, firms, countries, etc.) and time (over years, months, etc.). Panel data allow examination of problems that cannot be handled by cross-section data or time-series data. Panel data analysis is a core field in modern econometrics and multivariate statistics, and studies based on such data occupy a growing part of the field in many other disciplines. The book is intended as a text for master and advanced undergraduate courses. It may also be useful for PhD-students writing theses in empirical and applied economics and readers conducting empirical work on their own. The book attempts to take the reader gradually from simple models and methods in scalar (simple vector) notation to more complex models in matrix notation. A distinctive feature is that more attention is given to unbalanced panel data, the measurement error problem, random coefficient approaches, the interface between panel data and aggregation, and the interface between unbalanced panels and truncated and censored data sets. The 12 chapters are intended to be largely self-contained, although there is also natural progression. Most of the chapters contain commented examples based on genuine data, mainly taken from panel data applications to economics. Although the book, inter alia, through its use of examples, is aimed primarily at students of economics and econometrics, it may also be useful for readers in social sciences, psychology, and medicine, provided they have a sufficient background in statistics, notably basic regression analysis and elementary linear algebra.

For all its elaborate theories and models, economics always reduces to comparisons. Should we build A rather than B? Will I be better off if I eat D rather than C? How much will it cost me to produce F instead of E? At root, the ultimate goal of economics is simple: assessing the alternatives and finding the best possible outcome. This basic mathematical concept underlies all introductions to the field of economics, yet as advanced students progress through the discipline, they often lose track of this foundational idea when presented with real-world complications and uncertainty. In Competitive Agents in Certain and Uncertain Markets, Robert G. Chambers develops an integrated analytic framework for treating consumer, producer, and market equilibrium analyses as special cases of a generic optimization problem. He builds on lessons learned by all beginning students of economics to show how basic concepts can still be applied even in complex and highly uncertain conditions. Drawing from optimization theory, Chambers demonstrates how the same unified mathematical framework applies to both stochastic and non-stochastic decision settings. The book borrows from both convex and variational analysis and gives special emphasis to differentiability, conjugacy theory, and Fenchel's Duality Theorem. Throughout, Chambers includes practical examples, problems, and exercises to make abstract material accessible. Bringing together essential theoretical tools for understanding decision-making under uncertainty, Competitive Agents in Certain and Uncertain Markets provides a unified framework for analyzing a broad range of microeconomic decisions. This book will be an invaluable resource for advanced graduate students and scholars of microeconomic theory.

The Phillips Curve is world famous amongst economists. The man who invented it was an inventor, an engineer, a genius, who led an exciting life and contributed to economics in many different ways. Born and brought up on a remote farm in rural New Zealand, his early life was a search for adventure. He invented toys and rebuilt machinery as a child. He experienced the rigours of the Great Depression on construction sites, and while still a young man he roamed the outback of Australia picking up casual work, sometimes working in gold mines, sometimes crocodile hunting. In 1937 he set off to discover militarising Japan, a guerrilla war in Manchuria, Stalin's Soviet Union, and the tensions in Europe. On the outbreak of war, he joined the RAF and was sent to Singapore where he rearmed planes but was eventually incarcerated in a POW camp by the Japanese. In camp he learned languages, invented gadgets for the troops and built a clandestine radio. If his first 30 years had been a search for adventure, his later life was a search for economic stability. Back in Britain after the war, he scraped through a sociology degree at the LSE, before convincing a sceptical faculty to let him build a hydraulic model of the economy. This beautiful complex machine was a great success and put Bill Phillips on the track of serious economics. In the next few decades he developed new ideas for stabilising economies, was one of the first to use electronic computers, developed the Phillips Curve, showed ways to help an economy to grow, and developed new techniques to model economies. Always innovative, he took another heading in his later years, working out how to stabilise the Chinese economy which was being wracked by the Cultural Revolution. Bill Phillips pioneered a dozen new directions in economics, making him one of the most innovative and influential of our economic pioneers.

An excellent starting point for graduate-level econometrics, this comprehensive, well-organized and well-written introductory text includes all of the major topic areas of the subject, clearly explained through concepts rather than relying on complex algebra, and carefully pitched at the right level for students who may not already have a strong background in the subject. The text also includes discussion of bootstrap inference in order to aid students in understanding inference based on exact and asymptotic distributions.

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.

David F. Hendry is a seminal figure in modern econometrics. He has pioneered the LSE approach to econometrics, and his influence is wide ranging. This book is a collection of papers dedicated to him and his work. Many internationally renowned econometricians who have collaborated with Hendry or have been influenced by his research have contributed to this volume, which provides a reflection on the recent advances in econometrics and considers the future progress for the methodology of econometrics. Central themes of the book include dynamic modelling and the properties of time series data, model selection and model evaluation, forecasting, policy analysis, exogeneity and causality, and encompassing. The book strikes a balance between econometric theory and empirical work, and demonstrates the influence that Hendry's research has had on the direction of modern econometrics. Contributors include: Karim Abadir, Anindya Banerjee, Gunnar Bardsen, Andreas Beyer, Mike Clements, James Davidson, Juan Dolado, Jurgen Doornik, Robert Engle, Neil Ericsson, Jesus Gonzalo, Clive Granger, David Hendry, Kevin Hoover, Soren Johansen, Katarina Juselius, Steven Kamin, Pauline Kennedy, Maozu Lu, Massimiliano Marcellino, Laura Mayoral, Grayham Mizon, Bent Nielsen, Ragnor Nymoen, Jim Stock, Pravin Trivedi, Paolo Paruolo, Mark Watson, Hal White, and David Zimmer.