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Introductory Econometrics for Finance

TLDR
The 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 as discussed by the authors.
Abstract
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.

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Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks

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References
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Journal ArticleDOI

Econometric tests of rationality and market efficiency

TL;DR: In this article, the authors describe how economic theories can be tested from vector time series models using cointegration and the concept of co-integration in the modeling and testing procedure.
Journal ArticleDOI

Full Bayesian Inference for GARCH and EGARCH Models

TL;DR: A full Bayesian analysis of GARCH and EGARCH models is proposed consisting of parameter estimation, model selection, and volatility prediction and implemented via Markov-chain Monte Carlo methodologies.
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A New Class of Multivariate Skew Densities, with Application to GARCH Models

TL;DR: In this article, the authors proposed a method to introduce skewness in multivariate symmetric distributions, which leads to a multivariate skew-student density, in which each marginal has a specific asymmetry coefficient.
Journal ArticleDOI

Multivariate stochastic volatility models: Estimation and a comparison with VGARCH models

TL;DR: In this article, a multivariate stochastic volatility (MSV) model is presented together with an estimation method, which requires specialized estimation techniques since the volatility is a dynamic latent variable.
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