Econometric models

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  • Chapter 1 - Introduction. This chapter sets the scene for the book by discussing in broad terms the questions of what is econometrics, and what are the ‘stylised facts’ describing financial data that researchers in this area typically try to capture in their models. It also collects together a number of preliminary issues relating to the construction of econometric models in finance.

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  • ADVANCED TEXTS IN ECONOMETRICS General Editors Manuel Arellano Guido Imbens Grayham E. Mizon Adrian Pagan Mark Watson Advisory Editor C. W. J. Granger.Other Advanced Texts in conometrics ARCH: Selected Readings Edited by Robert F. Engle Asymptotic Theory for Integrated Processes By H. Peter Boswijk Bayesian Inference in Dynamic Econometric Models By Luc Bauwens, Michel Lubrano, and Jean-Fran¸ois Richard c Co-tegration, Error Correction, and the Econometric Analysis of Non-Stationary Data By Anindya Banerjee, Juan J. ...

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  • Lecture "Advanced Econometrics (Part II) - Chapter 6: Models for count data" presentation of content: Poisson regression model, goodness of fit, overdispersion, negative binomial regression model, too many zeros data.

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  • In this chapter, students will be able to understand: Highlight the problems that may occur if non-stationary data are used in their levels form, test for unit roots, examine whether systems of variables are cointegrated, estimate error correction and vector error correction models, explain the intuition behind Johansen’s test for cointegration,...

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  • Chapter 9 - Modelling volatility and correlation. In this chapter, you will learn how to: Discuss the features of data that motivate the use of GARCH models, explain how conditional volatility models are estimated, test for ‘ARCH-effects’ in time series data, produce forecasts from GARCH models, contrast various models from the GARCH family,...

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  • This Chapter of the Handbook will present a discussion of models, parcicularly models used in econometrics. models play a major role in all econometric studies, whether theoretical or applied. Indeed, defining econometrics as the branch of economics...

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  • This book is intended primarily for use in a second-semester course in graduate econometrics, after a first course at the level of Goldberger (1991) or Greene (1997). Parts of the book can be used for special-topics courses, and it should serve as a general reference. My focus on cross section and panel data methods—in particular, what is often...

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  • (1) Since the simpler model features less regressor than the larger model, it follows that the VIF of the simpler model will be less than that of the larger model. The reason is that the more variables we include in the model, the greater multicollinearity, and, hence, the greater Rj 2 , unless the omitted variables happen to be orthogonal to the regressors included in the simpler model. The simpler model, which omits relevant variables, produces bias estimates but with smaller variances. Consequently, there appears to be a tradeoff between bias and precision. ...

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  • Statistical procedures of estimation and inference are most frequently justified in econometric work on the basis of certain desirable asymptotic properties. One estimation procedure may, for example, be selected over another because it is known to provide consistent and asymptotically efficient parameter estimates under certain stochastic environments.

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  • 3 A continuous dependent variable. In this chapter we review a few principles of econometric modeling, and illustrate these for the case of a continuous dependent variable. We assume basic knowledge of matrix algebra and of basic statistics and mathematics

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  • Regression models form the core of the discipline of econometrics. Although econometricians routinely estimate a wide variety of statistical models, using many different types of data, the vast majority of these are either regression models or close relatives of them. In this chapter, we introduce the concept of a regression model, discuss several varieties of them, and introduce the estimation method that is most commonly used with regression models, namely, least squares.

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  • Non-market valuation has become a broadly accepted and widely practiced means of measuring the economic values of the environment and natural resources. In this book, the authors provide a guide to the statistical and econometric practices that economists employ in estimating non-market values. The authors develop the econometric models that underlie the basic methods: contingent valuation, travel cost models, random utility models and hedonic models.

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  • 8 A duration dependent variable. In the previous chapters we have discussed econometric models for ordered and unordered discrete choice dependent variables and continuous dependent variables, which may be censored or truncated.

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  • Chris Adcock is Professor of Financial Econometrics in the University of Sheffield. His career includes several years working in quantitative investment management in the City and, prior to that, a decade in management science consultancy. His research interests are in the development of robust and non-standard methods for modelling expected returns, portfolio selection methods and the properties of optimized portfolios. He has acted as an advisor to a number of asset management firms. He is the founding editor of the European Journal of Finance. George A.

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  • The aim of this textbook is to provide a step-by-step guide to financial econometrics using EViews 6.0 statistical package. It contains brief overviews of econometric concepts, models and data analysis techniques followed by empirical examples of how they can be implemented in EViews. This book is written as a compendium for undergraduate and graduate students in economics and finance. It also can serve as a guide for researchers and practitioners who desire to use EViews for analysing financial data.

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  • Chapter 31 ECONOMETRIC METHODS PRODUCER BEHAVIOR FOR MODELING The purpose of this chapter is to provide an exposition of econometric methods for modeling producer behavior. The objective of econometric modeling is to determine the nature of substitution among inputs, the character of differences in technology

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  • Chapter 19 INFERENCE MODELS IN ECONOMIC Many econometricians are apt to be uncomfortable when thinking about the concept. Large sample distribution theory is the cornerstone of statistical inference for econometric models. The limiting distribution of a statistic gives approximate distributional results.

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  • Chapter 15 Testing the Specification of Econometric Models 15.1 Introduction As we first saw in Section 3.7, estimating a misspecified regression model generally yields biased and inconsistent parameter estimates.

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  • NONLINEAR MODELS AND RELATED TOPICS We now apply the general methods of Part III to study specific nonlinear models that often arise in applications. Many nonlinear econometric models are intended to explain limited dependent variables. Roughly, a limited dependent variable is a variable whose range is restricted

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  • Chapter 10 CONTINUOUS TIME STOCHASTIC MODELS AND ISSUES OF AGGREGATION OVER TIME Since the publication of the influential articles of Haavelmo (1943) and Mann and Wald (1943) and the subsequent work of the Cowles Commission [see, especially, Koopmans (1950a)], most econometric models of complete economies have been formulated as systems of simultaneous stochastic difference

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