The basic characteristic of Modern Linear and Nonlinear Econometrics is that it presents a unified approach of modern linear and nonlinear econometrics in a concise and intuitive way. It covers four major parts of modern econometrics: linear and nonlinear estimation and testing, time series analysis, models with categorical and limited dependent variables, and, finally, a thorough analysis of linear and nonlinear panel data modeling. Distinctive features of this handbook are: A unified approach of both linear and nonlinear econometrics, with an integration of the theory and the practice in modern econometrics. Emphasis on sound theoretical and empirical relevance and intuition. Focus on econometric and statistical methods for the analysis of linear and nonlinear processes in economics and finance, including computational methods and numerical tools. Completely worked out empirical illustrations are provided throughout, the macroeconomic and microeconomic (household and firm level) data sets of which are available from the internet; these empirical illustrations are taken from finance (e.g. CAPM and derivatives), international economics (e.g. exchange rates), innovation economics (e.g. patenting), business cycle analysis, monetary economics, housing economics, labor and educational economics (e.g. demand for teachers according to gender) and many others. Exercises are added to the chapters, with a focus on the interpretation of results; several of these exercises involve the use of actual data that are typical for current empirical work and that are made available on the internet. What is also distinguishable in Modern Linear and Nonlinear Econometrics is that every major topic has a number of examples, exercises or case studies. By this `learning by doing' method the intention is to prepare the reader to be able to design, develop and successfully finish his or her own research and/or solve real world problems.

PLASMANS, Joseph, Emanuel, Julien Date of birth: January 12, 1944 Studies 'Licentiaat in de Handels- en Financiële Wetenschappen' (Bachelor Degree in Commercial and Financial Sciences; 1966 - UFSIA, University of Antwerp, "distinction") 'Drs. in de Algemene en de Bedrijfseconometrie' (Drs. in General and Business Econometrics; 1968 - Catholic University of Tilburg, "cum laude") 'Dr. in de Economische Wetenschappen' (Ph.D. in Economics, 1975 - Catholic University of Tilburg, "cum laude") with the Ph.D. Thesis: Production Investment Behaviour, with an Application to six EEC-Countries, Tilburg University Press, 333 pp. Promotors: Prof. Dr. J.J.J. Dalmulder (Catholic University of Tilburg) and Prof. Dr. R.L. Graves (University of Chicago). Academic Functions 19681975: Assistant UFSIA for Statistics (2nd and 3rd university years of the 2nd Cycle in Applied Economics) and for Mathematical Statistics (3rd and 4th university years of the 2nd Cycle in Business Econometrics) 19691975: Assistant Professor in the Economic Faculty of the Catholic University of Tilburg (Department of Econometrics) 19751986: First Assistant Professor at the Catholic University of Tilburg in 'General Econometrics'; from 01/01/1985 ad O,2 f.t.e. 19751984: Associate Professor UFSIA (parttime) 19851986: Associate Professor UFSIA (fulltime) 19861991: Professor UFSIA 1986: Parttime 'universitair hoofddocent' (equivalent to extraordinary professor) at Tilburg University (Katholieke Universiteit Brabant KUB) 1992: Full (ordinary) Professor UFSIA in (Macro) Econometrics (incl. Financial Econometrics

Acknowledgements.- Part I. Linear and Nonlinear Econometric Inference: Estimation and Testing. Estimation in Linear and Nonlinear Models. Generalized Methods of Moments. Testing in Linear and Nonlinear Models.- Part II. Time Series Analysis. A Typology of Dynamic Models. Univariate ARIMA Models. Cointegration and Transfer Functions. Multivariate Time Series. Varying Parameters Models.- Part III. Categorical and Limited Dependent Variables. Discrete Choice Models. Limited responses, duration and count data.- Part IV. Panel Data Analysis. Linear Panel Data Models. Nonlinear Panel Data Models.- A. Nonlinear Optimization and Estimation.- B. Mathematical Formulation of GMM.- C. Stability Criteria for AR(p) Models.- D. MLE of the RSM with Endogenous Prices.- E. Volatility Modeling.