Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Estimation and Inference in Econometrics

Using a Bayesian likelihood approach, we estimate a dynamic stochastic general equilibrium model for the US economy using seven macro-economic time series. The model incorporates many types of real and nominal frictions and seven types of structural shocks. We show that this model is able to compete with Bayesian Vector Autoregression models in out-of-sample prediction. We investigate the relative empirical importance of the various frictions. Finally, using the estimated model we address a number of key issues in business cycle analysis: What are the sources of business cycle fluctuations? Can the model explain the cross-correlation between output and inflation? What are the effects of productivity on hours worked? What are the sources of the "Great Moderation"?

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Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach

We estimate a dynamic stochastic general equilibrium (DSGE) model for the US economy. The model incorporates many types of real and nominal frictions: sticky nominal price and wage setting, habit formation in consumption, investment adjustment costs, variable capital utilisation and fixed costs in production. It also contains many types of shocks including productivity, labour supply, investment, preference, cost-push and monetary policy shocks. Using Bayesian estimation techniques, the relative importance of the various frictions and shocks in explaining the US business cycle are empirically investigated. We also show that this model is able to outperform standard VAR and BVAR models in out-of-sample prediction.

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Shocks and frictions in US business cycles: A Bayesian DSGE approach

This bestselling and thoroughly classroom-tested textbook is a complete resource for finance students. A comprehensive and illustrated discussion of the most common empirical approaches in finance prepares students for using econometrics in practice, while detailed case studies help them understand how the techniques are used in relevant financial contexts. Worked examples from the latest version of the popular statistical software EViews guide students to implement their own models and interpret results. 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. Building on the successful data- and problem-driven approach of previous editions, this third edition has been updated with new data, extensive examples and additional introductory material on mathematics, making the book more accessible to students encountering econometrics for the first time. A companion website, with numerous student and instructor resources, completes the learning package.

Introductory Econometrics for Finance

This book provides the most comprehensive treatment of the theoretical concepts and modelling techniques of quantitative risk management. Whether you are a financial risk analyst, actuary, regulator or student of quantitative finance, Quantitative Risk Management gives you the practical tools you need to solve real-world problems. Describing the latest advances in the field, Quantitative Risk Management covers the methods for market, credit and operational risk modelling. It places standard industry approaches on a more formal footing and explores key concepts such as loss distributions, risk measures and risk aggregation and allocation principles. The book's methodology draws on diverse quantitative disciplines, from mathematical finance and statistics to econometrics and actuarial mathematics. A primary theme throughout is the need to satisfactorily address extreme outcomes and the dependence of key risk drivers. Proven in the classroom, the book also covers advanced topics like credit derivatives. Fully revised and expanded to reflect developments in the field since the financial crisis Features shorter chapters to facilitate teaching and learning Provides enhanced coverage of Solvency II and insurance risk management and extended treatment of credit risk, including counterparty credit risk and CDO pricing Includes a new chapter on market risk and new material on risk measures and risk aggregation

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Quantitative Risk Management: Concepts, Techniques, and Tools

This paper surveys the most important developments in multivariate ARCH-type modelling. It reviews the model specifications, the inference methods, and the main areas of application of these models in financial econometrics.

Multivariate GARCH Models: A Survey

This paper surveys the most important developments in multivariate ARCH-type modelling. It reviews the model specifications and inference methods, and identifies likely directions of future research.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/jae.842

Multivariate GARCH models: a survey

Chapter 1: Decision Theory and Bayesian Inference Chapter 2: Bayesian Statistics and Linear Regression Chapter 3: Methods of Numerical Integration Chapter 4: Prior Densities for the Regression Model Chapter 5: Dynamic Regression Models Chapter 6: Bayesian Unit Roots Chapter 7: Heteroskedasticity and ARCH Chapter 8: Nonlinear Tome Series Models Chapter 9: Systems of Equations Appendix A: Probability Distributions Appendix B: Generating Random Numbers

Bayesian Inference in Dynamic Econometric Models

This paper introduces the logarithmic autoregressive conditional duration (Log-ACD) model and compares it with the ACD model of ENGLE and RUSSELL [1998]. The logarithmic version allows to introduce in the model additional variables without sign restrictions on their coefficients. We apply the Log-ACD model to price durations relative to the bid-ask quote process of three securities listed on the New York Stock Exchange, and we investigate the influence of some characteristics of the trade process (trading intensity, average volume per trade and average spread) on the bid-ask quote process.

https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=884252

The Logarithmic ACD Model: An Application to the Bid-Ask Quote Process of Three NYSE Stocks

We propose a practical and flexible method to introduce skewness in multivariate symmetric distributions. Applying this procedure to the multivariate Student density leads to a “multivariate skew-Student” density in which each marginal has a specific asymmetry coefficient. Combined with a multivariate generalized autoregressive conditional heteroscedasticity model, this new family of distributions is found to be more useful than its symmetric counterpart for modeling stock returns and especially for forecasting the value-at-risk of portfolios.

Luc Bauwens

Papers

Multivariate GARCH Models: A Survey

Multivariate GARCH models: a survey

Bayesian Inference in Dynamic Econometric Models

The Logarithmic ACD Model: An Application to the Bid-Ask Quote Process of Three NYSE Stocks

A New Class of Multivariate Skew Densities, With Application to Generalized Autoregressive Conditional Heteroscedasticity Models