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Introductory Econometrics for Finance

SECOND EDITION

This best-selling textbook addresses the need for an introduction to econometrics speciﬁcally written for ﬁnance students. It includes examples and case studies which ﬁnance students will recognise and relate to. This new edition builds on the successful data- and problem-driven approach of the ﬁrst edition, giving students the skills to estimate and interpret models while developing an intuitive grasp of underlying theoretical concepts.

Key features:

● Thoroughly revised and updated, including two new chapters on

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panel data and limited dependent variable models

Problem-solving approach assumes no prior knowledge of econometrics emphasising intuition rather than formulae, giving students the skills and conﬁdence to estimate and interpret models

Detailed examples and case studies from ﬁnance show students how techniques are applied in real research

Sample instructions and output from the popular computer package

EViews enable students to implement models themselves and understand how to interpret results

Gives advice on planning and executing a project in empirical ﬁnance, preparing students for using econometrics in practice

Covers important modern topics such as time-series forecasting, volatility modelling, switching models and simulation methods

Thoroughly class-tested in leading ﬁnance schools

Chris Brooks is Professor of Finance at the ICMA Centre, University of

Reading, UK, where he also obtained his PhD. He has published over sixty articles in leading academic and practitioner journals including the Journal of Business, the Journal of Banking and Finance, the Journal of

Empirical Finance, the Review of Economics and Statistics and the Economic

Journal. He is an associate editor of a number of journals including...

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...e YOUR ECONOMETRICS PAPER BASIC TIPS There are a couple of websites that you can browse to give you some ideas for topics and data. Think about what you want to do with this paper. Econometrics is a great tool to market when looking for jobs. A well-written econometrics paper and your presentation can be a nice addition to your resume. You are not expected to do original research here. REPLICATION of prior results is perfectly acceptable. Read Studenmund's Chapter 11. One of the most frustrating things in doing an econometrics paper is finding the data. Do not spend a lot of time on a topic before determining whether there is data available that will allow you to answer your question. It is a good idea to write down your ideal data set that would allow you to address your topic. If you find that the available data is not even close to what you had originally desired, you might want to change your topic. Also, remember that knowing the location of your data – website, reference book, etc – is not the same as having your data available to use. It may take a LONG time to get the data in a format that EVIEWS can read. Do not leave this till the last minute. For most data, I enter the data into Excel first. I save the Excel sheet in the oldest version, namely MS Excel Worksheet 2.1 . The reason is that format can be read by most programs whereas newer formats may or may not be read. Eviews easily reads an Excel sheet 2.1 version. You should......

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...A Guide to Modern Econometrics 2nd edition Marno Verbeek Erasmus University Rotterdam A Guide to Modern Econometrics A Guide to Modern Econometrics 2nd edition Marno Verbeek Erasmus University Rotterdam Copyright 2004 John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England Telephone (+44) 1243 779777 Email (for orders and customer service enquiries): cs-books@wiley.co.uk Visit our Home Page on www.wileyeurope.com or www.wiley.com All Rights Reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except under the terms of the Copyright, Designs and Patents Act 1988 or under the terms of a licence issued by the Copyright Licensing Agency Ltd, 90 Tottenham Court Road, London W1T 4LP, UK, without the permission in writing of the Publisher. Requests to the Publisher should be addressed to the Permissions Department, John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England, or emailed to permreq@wiley.co.uk, or faxed to (+44) 1243 770620. This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold on the understanding that the Publisher is not engaged in rendering professional services. If professional advice or other expert assistance is required,...

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...Оценивание отдачи от образования. Данные из учебника Manno Verbeek “A guide to Modern Econometrics” http://www.econ.kuleuven.ac.be/GME/ Файл schooling содержит данные Национального панельного опроса 1976 года молодых мужчин (NLSYM, проживающих в США. Переменные в файле и их описание: ￼smsa66 1 if lived in smsa in 1966 1 Семинары по эконометрике, 2013 г. smsa76 1 if lived in smsa in 1976 nearc2 grew up near 2-yr college nearc4 grew up near 4-yr college nearc4a grew up near 4-year public college nearc4b grew up near 4-year private ed76 education in 1976 ed66 education in 1966 age76 age in 1976 college daded dads education (imputed avg nodaded 1 if dads education imputed momed mothers education nomomed 1 if moms education imputed momdad14 1 if lived with mom and dad sinmom14 1 if single mom at age 14 step14 1 if step parent at age 14 south66 1 if lived in south in 1966 south76 1 if lived in south in 1976 lwage76 log wage in 1976 (outliers trimmed) famed mom-dad education class (1-9) black 1 if black wage76 wage in 1976 (raw, cents per hour) enroll76 1 if enrolled in 1976 kww the kww score iqscore a normed IQ score mar76 marital status in libcrd14 1 if library card exp76 exp762 experience in 1976 exp76 squared 1976 (1 if married) in home at age 14 if missing) at age 14 1.1. Оцените простую линейную модель регрессии: reg lwage76 ed76 exp76 exp762 black smsa76 south76 est store ols 1.2. Проверка мультиколлинеарности: vif 1.3. Проверка......

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...Question no. 1 Y1= ∝0+∝1Y2+∝2X1+∈1 Y2= β0+β1Y1+β2X1+β3X3+ϵ2 i. Identification Status: Equation 1: P1=1, P2=1 so that P1=P2 so, equation is Exactly identified. Equation 2: P1=0, P2=1 so that P1<P2 so, equation is Unidentified. ii. Reduced form equations: Putting Y1 in Y2: Y2= β0+β1(∝0+∝1Y2+∝2X1+∈1) +β2X1+β3X3+ϵ2 Y2= β0+β1α0+β1α1Y2+β1α2X1+β1ϵ1+β2X1+β3X3+ϵ2 Y21-β1α1= β0+β1α0+X1β1α2+β2+β3X3+β1ϵ1+ϵ2 Y2= β0+β1α01-β1α1+β1α2+β21-β1α1X1+β31-β1α1X3+β1ϵ1+ϵ21-β1α1 Y1=π20+π21X1+π22X3+ν2 Now putting this reduced form equation of Y2 in Y1 equation: Y1= ∝0+∝1(π20+π21X1+π22X3+V2)∝2X1+∈1 Y1= ∝0+∝1π20+X1α1π21+α2+α1π22X3+α1V2+ϵ1 Y1= π10+π11X1+π12X3+V1 π10= ∝0+∝1π20 α0= π10-π12π22(π20) π11= α1π21+α2 α2= π11- π12π22(π21) π12= α1π22 α1= π12π22 Using STATA the reduced form equation (DATA set 1) Y2= -1.57953 -.37781X1+1.744 X3 Y1= 14.245+ .67809 X1+ .99181 X3 Estimations of structural parameters For equation 1: Run the regression on reduced form equations in STATA and we calculated the following values of structural parameters: 1. α1= π12π22 = .99181 1.744 α1= 0.5688 2. α2= π11- π12π22π21 = .67809 - 0.5688 (-.37781) α2= 0.8930 3. α0= π10-π12π22(π20) = 14.245-0.5688(-1.57953) α0= 15.1437 Question no. 2 Y1=αo+α1Y2+α2X1+α3X2+e1 Y2=βo+β1Y1+β2X1+β3X3+e2 Status Identification of equations: For equation 1: P1=1 P2=1 P1=P2This identifies that the equation 1 is......

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...Mostly Harmless Econometrics: An Empiricist’ Companion s Joshua D. Angrist Massachusetts Institute of Technology Jörn-Ste¤en Pischke The London School of Economics March 2008 ii Contents Preface Acknowledgments Organization of this Book xi xiii xv I Introduction 1 3 9 10 12 16 1 Questions about Questions 2 The Experimental Ideal 2.1 2.2 2.3 The Selection Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Random Assignment Solves the Selection Problem . . . . . . . . . . . . . . . . . . . . . . . . Regression Analysis of Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II The Core 19 21 22 23 26 30 36 38 38 44 47 51 51 3 Making Regression Make Sense 3.1 Regression Fundamentals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1 3.1.2 3.1.3 3.1.4 3.2 Economic Relationships and the Conditional Expectation Function . . . . . . . . . . . Linear Regression and the CEF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Asymptotic OLS Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Saturated Models, Main E¤ects, and Other Regression Talk . . . . . . . . . . . . . . . Regression and Causality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 3.2.2 3.2.3 The Conditional Independence Assumption . . . . . . . . . . . . . . . . . . . . . . . . The Omitted Variables Bias Formula . ....

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...Using gretl for Principles of Econometrics, 4th Edition Version 1.0411 Lee C. Adkins Professor of Economics Oklahoma State University April 7, 2014 1 Visit http://www.LearnEconometrics.com/gretl.html for the latest version of this book. Also, check the errata (page 459) for changes since the last update. License Using gretl for Principles of Econometrics, 4th edition. Copyright c 2011 Lee C. Adkins. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.1 or any later version published by the Free Software Foundation (see Appendix F for details). i Preface The previous edition of this manual was about using the software package called gretl to do various econometric tasks required in a typical two course undergraduate or masters level econometrics sequence. This version tries to do the same, but several enhancements have been made that will interest those teaching more advanced courses. I have come to appreciate the power and usefulness of gretl’s powerful scripting language, now called hansl. Hansl is powerful enough to do some serious computing, but simple enough for novices to learn. In this version of the book, you will ﬁnd more information about writing functions and using loops to obtain basic results. The programs have been generalized in many instances so that they could be adapted for other uses if desired. As I learn more about hansl speciﬁcally...

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...1. In the population model: cigsi = βo + β1educi + ui a) Interpret the coefficient β1. β1 represents the slope of the regression line and is the change in cigs associated with a unit change in educ. So for one unit increase of educ there will be β1 units increase/decrease (depending on the sign of β1) in the cigs. b) Can you predict the sign of β1 (without doing any estimation)? Explain. The sign of β1 would most probably be minus looking at the information from the surveys in the excel spreadsheet. The education value is always a positive number (educi > 0), βo is also a positive number (the intercept with y, as cigsi >=0). In this way in order for the number of cigarettes to be equal to 0, the β1 value should be negative. 2. Use the data in SMOKE.sav (see Blackboard) to estimate the model from question 1. Report the estimated equation in the usual way. Also, plot a handwritten graph of the estimated equation. cigs = 11,412 – 0,219 educ 3. Does educ explain a lot of the variation in the number of cigarettes smoked? Explain. The regression R2 is the fraction of the sample variance of cigsiexplained by (or predicted by) educi. In this example R2 equals to 0,002 and this amount is closer to 0, which means that the regressor educ is not very good at predicting the value of cigs, thus does not explain a lot of the variation in the number of cigarettes smoked. 4. Find the predicted difference in number of smoked cigarettes for two people...

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...empec, Vol. 13, 1988, page 223-249 Nonparametric Estimation and Hypothesis Testing in Econometric Models By A. Ullah ~ Abstract: In this paper we systematically review and develop nonparametric estimation and testing techniques in the context of econometric models. The results are discussed under the settings of regression model and kernel estimation, although as indicated in the paper these results can go through for other econometric models and for the nearest neighbor estimation. A nontechnical survey of the asymptotic properties of kernel regression estimation is also presented. The technique described in the paper are useful for the empirical analysis of the economic relations whose true functional forms are usually unknown. 1 Introduction Consider an economic model y =R(x)+u where y is a dependent variable, x is a vector o f regressors, u is the disturbance and R(x) = E ( y l x ) . Often, in practice, the estimation o f the derivatives o f R(x)are o f interest. For example, the first derivative indicates the response coefficient (regression coefficient) o f y with respect to x, and the second derivauve indicates the curvature o f R(x). In the parametric econometrics the estimation o f these derivatives and testing 1 Aman Ullah, Department of Economics, University of Western Ontario, London, Ontario, N6A 5C2, Canada. I thank L Ahmad, A. Bera, A. Pagan, C. Robinson, A. Zellner, and the participants of the workshops at the Universities of......

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...AND COMMENTS TO Joy de Beyer ( jdebeyer@worldbank.org) and Ayda Yurekli (ayurekli@worldbank.org) World Bank, MSN G7-702 1818 H Street NW Washington DC, 20433 USA Fax : (202) 522-3234 Contents I. Introduction 1 Purpose of this Tool 1 Who Should Use this Tool 2 How to Use this Tool 2 II. Define the Objectives of the Analysis 4 The Reason for Analysis of Demand 4 The Economic Case for Demand Intervention 4 Analysis of Demand for the Policy Maker 5 Design an Analysis of Demand Study 6 Components of a Study 6 The Nature of Econometric Analysis 7 Resources Required 7 Summary 8 References and Additional Information 8 III. Conduct Background Research 9 IV. Build the Data Set 11 Choose the Variables 11 Data Availability 11 Data Types 12 Prepare the Data 13 Data Cleaning and Preliminary Examination 14 Preparing the Data Variables 14 References and Additional Information 19 V. Choose the Demand Model 20 Determine the Identification Problem 20 Test for Price Endogeneity 21 ...

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...What is Econometrics? Econometrics is a rapidly developing branch of economics which, broadly speaking, aims to give empirical content to economic relations. The term ‘econometrics’ appears to have been first used by Pawel Ciompa as early as 1910; although it is Ragnar Frisch, one of the founders of the Econometric Society, who should be given the credit for coining the term, and for establishing it as a subject in the sense in which it is known today (see Frisch, 1936, p. 95). Econometrics can be defined generally as ‘the application of mathematics and statistical methods to the analysis of economic data’, or more precisely in the words of Samuelson, Koopmans and Stone (1954), ... as the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference (p. 142). Other similar descriptions of what econometrics entails can be found in the preface or the introduction to most texts in econometrics. Malinvaud (1966), for example, interprets econometrics broadly to include ‘every application of mathematics or of statistical methods to the study of economic phenomena’. Christ (1966) takes the objective of econometrics to be ‘the production of quantitative economic statements that either explain the behaviour of variables we have already seen, or forecast (i.e. predict) behaviour that we have not yet seen, or both’. Chow (1983) in a more recent textbook succinctly defines econometrics ‘as......

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