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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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Citations
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Oil prices and the stock prices of alternative energy companies

TL;DR: In this article, a four variable vector autoregression model is developed and estimated in order to investigate the empirical relationship between alternative energy stock prices, technology stock prices and oil prices, and interest rates.
Journal ArticleDOI

Impact of Reward and Recognition on Job Satisfaction and Motivation: An Empirical Study from Pakistan

TL;DR: In this article, the authors present an attempt to find out the major factors that motivate employees and it tells what is the relationship among reward, recognition and motivation while working within an organization.
Posted Content

Volatility and correlation forecasting

TL;DR: A recent survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications is provided in this paper, where a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management.
Posted Content

Bank Specific and Macroeconomic Determinants of Commercial Bank Profitability Empirical Evidence from Turkey

TL;DR: In this paper, the authors examined the bank-specific and macroeconomic determinants of the banks profitability in Turkey over the time period from 2002 to 2010 and found that asset size and non-interest income have a positive and significant effect on bank profitability.
Journal ArticleDOI

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

TL;DR: In this article, a hybrid EMD-BPN forecasting approach which combines empirical mode decomposition (EMD) and back-propagation neural networks (BPN) is developed to predict the short-term passenger flow in metro systems.
References
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Journal ArticleDOI

Inference when a nuisance parameter is not identified under the null hypothesis

Bruce E. Hansen
- 01 Mar 1996 - 
TL;DR: In this paper, the asymptotic distribution of standard test statistics is described as functionals of chi-square processes, and a transformation based upon a conditional probability measure yields an asymptic distribution free of nuisance parameters, which can be easily approximated via simulation.
Journal ArticleDOI

Testing for Common Trends

TL;DR: In this article, two tests for the number of common stochastic trends (i.e., for the order of cointegration) in a multiple time series with and without drift are developed.
Book

Introduction to multiple time series analysis

TL;DR: The choice of point and interval forecasts as well as innovation accounting are presented as tools for structural analysis within the multiple time series context.
Journal ArticleDOI

Improved Methods for Tests of Long-Run Abnormal Stock Returns

TL;DR: Barber and Lyon as mentioned in this paper analyzed tests for long-run abnormal returns and document that two approaches yield well-specified test statistics in random samples, but misspecification in non-random samples is pervasive.
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