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Statistics and Econometric Models. Themes in Modern Econometrics. Time Series and Dynamic Models. Themes in Modern Econometrics. Statistique de l'assurance. Collection "Economie et statistiques avancées". ARCH Models and Financial Applications. Springer Series in Statistics. Articles/Essays/Papers. Gourieroux, Christian; Jasiak, Joann (2002).
With A. Szafarz, Broze is the author of The Econometric Analysis of Non-Uniqueness in Rational Expectations Models (Contributions to Economic Analysis, Elsevier, 1991). With Szafarz and C. Gourieroux, she is the author of Reduced Forms of Rational Expectations Models (Fundamentals of Pure and Applied Economics 42, Harwood Academic Publishers ...
Econometrics may use standard statistical models to study economic questions, but most often they are with observational data, rather than in controlled experiments. [10] In this, the design of observational studies in econometrics is similar to the design of studies in other observational disciplines, such as astronomy, epidemiology, sociology and political science.
Econometric models are statistical models used in econometrics. An econometric model specifies the statistical relationship that is believed to hold between the various economic quantities pertaining to a particular economic phenomenon. An econometric model can be derived from a deterministic economic model by allowing for uncertainty, or from ...
A basic tool for econometrics is the multiple linear regression model. [8] Econometric theory uses statistical theory and mathematical statistics to evaluate and develop econometric methods. [9] [10] Econometricians try to find estimators that have desirable statistical properties including unbiasedness, efficiency, and consistency.
Most statistics textbooks will include at least some material on homoscedasticity and heteroscedasticity. Some examples are: Asteriou, Dimitros; Hall, Stephen G. (2011). Applied Econometrics (Second ed.). Palgrave MacMillan. pp. 109– 147. ISBN 978-0-230-27182-1. Davidson, Russell; MacKinnon, James G. (1993). Estimation and Inference in ...
Driven by the rapid growth of computing capacities from the mid-1980s on, the application of Markov chain Monte Carlo simulation to statistical and econometric models, first performed in the early 1990s, enabled Bayesian analysis to drastically increase its influence in economics and econometrics. [6]
If a nonlinear model is fitted to the data one often needs to estimate coefficients through optimization. A number of optimisation algorithms have the following general structure. Suppose that the function to be optimized is Q(β). Then the algorithms are iterative, defining a sequence of approximations, β k given by