This authoritative collection of papers covers a broad spectrum of topics in theoretical and applied economics and econometrics. The tone of the book is set by Paul Klemperer's contribution on using and abusing economic theory, in which academics are encouraged to widen the scope of their analyses beyond the confines of elegant models which sometimes lack 'real-world' detail. As a result, many of the chapters in this volume share a high degree of practical relevance. Extensive discussion of a variety of contemporary issues in economics and econometrics follows, including: * theoretical contributions in economics: the economics of auctions; industry sunk costs and entry dynamics * econometric theory: automated-model selection; conditions for weak-exogeneity in vector correction models; Bayesian inference for trended economic time series; Gibbs sampling for truncated multivariate normal distributions * methodology and applications: lag-length selection in non-linear dynamic models; the relationship between intercepts, threshold and autoregressive coefficients in the two-regime self-exciting autoregressive model; the problems caused by incomplete data for econometric modelling of the term structure of interest rates and also in models using unbalanced panel data; the informational content of the term structure of interest rates with respect to future inflation. The wide variety of topics explored, along with the focus on practical application, will make this book particularly valuable reading for students and applied researchers as well as appealing to a wider academic audience.

Financial econometrics brings financial theory and econometric methods together with the power of data to advance understanding of the global financial universe upon which all modern economies depend. Financial Econometric Modeling is an introductory text that meets the learning challenge of integrating theory, measurement, data, and software to understand the modern world of finance. Empirical applications with financial data play a central position in this book's exposition. Each chapter is a how-to guide that takes readers from ideas and theories through to the practical realities of modeling, interpreting, and forecasting financial data. The book reaches out to a wide audience of students, applied researchers, and industry practitioners, guiding readers of diverse backgrounds on the models, methods, and empirical practice of modern financial econometrics. Financial Econometric Modeling delivers a self-contained first course in financial econometrics, providing foundational ideas from financial theory and relevant econometric technique. From this foundation, the book covers a vast arena of modern financial econometrics that opens up empirical applications with data of the many different types that are now generated in financial markets. Every chapter follows the same principle ensuring that all results reported in the book may be reproduced using standard econometric software packages such as Stata or EViews, with a full set of data and programs provided to ensure easy implementation.

This book provides a general framework for specifying, estimating, and testing time series econometric models. Special emphasis is given to estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalized method of moments estimation, nonparametric estimation, and estimation by simulation. An important advantage of adopting the principle of maximum likelihood as the unifying framework for the book is that many of the estimators and test statistics proposed in econometrics can be derived within a likelihood framework, thereby providing a coherent vehicle for understanding their properties and interrelationships. In contrast to many existing econometric textbooks, which deal mainly with the theoretical properties of estimators and test statistics through a theorem-proof presentation, this book squarely addresses implementation to provide direct conduits between the theory and applied work.

This book provides a general framework for specifying, estimating, and testing time series econometric models. Special emphasis is given to estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalized method of moments estimation, nonparametric estimation, and estimation by simulation. An important advantage of adopting the principle of maximum likelihood as the unifying framework for the book is that many of the estimators and test statistics proposed in econometrics can be derived within a likelihood framework, thereby providing a coherent vehicle for understanding their properties and interrelationships. In contrast to many existing econometric textbooks, which deal mainly with the theoretical properties of estimators and test statistics through a theorem-proof presentation, this book squarely addresses implementation to provide direct conduits between the theory and applied work.

Aspects of environmental change are some of the greatest challenges faced by policymakers today. The key issues addressed by environmental science are often empirical, and in many instances very detailed, sizable datasets are available. Researchers in this field should have a solid understanding of the econometric tools best suited for analysis of these data. While complex and expensive physical models of the environment exist, it is becoming increasingly clear that reduced-form econometric models have an important role to play in modeling environmental phenomena. In short, successful environmental modeling does not necessarily require a structural model, but the econometric methods underlying a reduced-form approach must be competently executed. Environmental Econometrics Using Stata provides an important starting point for this journey by presenting a broad range of applied econometric techniques for environmental econometrics and illustrating how they can be applied in Stata. The emphasis is not only on how to formulate and fit models in Stata but also on the need to use a wide range of diagnostic tests in order to validate the results of estimation and subsequent policy conclusions. This focus on careful, reproducible research should be appreciated by academic and non-academic researchers who are seeking to produce credible, defensible conclusions about key issues in environmental science.