...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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...h a p t e r One The Nature of Econometrics and Economic Data C hapter 1 discusses the scope of econometrics and raises general issues that result from the application of econometric methods. Section 1.3 examines the kinds of data sets that are used in business, economics, and other social sciences. Section 1.4 provides an intuitive discussion of the difficulties associated with the inference of causality in the social sciences. 1.1 WHAT IS ECONOMETRICS? Imagine that you are hired by your state government to evaluate the effectiveness of a publicly funded job training program. Suppose this program teaches workers various ways to use computers in the manufacturing process. The twenty-week program offers courses during nonworking hours. Any hourly manufacturing worker may participate, and enrollment in all or part of the program is voluntary. You are to determine what, if any, effect the training program has on each worker’s subsequent hourly wage. Now suppose you work for an investment bank. You are to study the returns on different investment strategies involving short-term U.S. treasury bills to decide whether they comply with implied economic theories. The task of answering such questions may seem daunting at first. At this point, you may only have a vague idea of the kind of data you would need to collect. By the end of this introductory econometrics course, you should know how to use econometric methods to formally evaluate a job training program...
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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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...This page intentionally left blank 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 ● ● ● ● ● ● 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...
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...This page intentionally left blank 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 ● ● ● ● ● ● 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...
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...Boston College Economics The Stata Journal (yyyy) Working Paper Number ii, pp. 1–38 vv, No. 667 Enhanced routines for instrumental variables/GMM estimation and testing Christopher F. Baum Mark E. Schaffer Boston College Heriot–Watt University Steven Stillman Motu Economic and Public Policy Research Abstract. We extend our 2003 paper on instrumental variables (IV) and GMM estimation and testing and describe enhanced routines that address HAC standard errors, weak instruments, LIML and k-class estimation, tests for endogeneity and RESET and autocorrelation tests for IV estimates. Keywords: st0001, instrumental variables, weak instruments, generalized method of moments, endogeneity, heteroskedasticity, serial correlation, HAC standard errors, LIML, CUE, overidentifying restrictions, Frisch–Waugh–Lovell theorem, RESET, Cumby-Huizinga test 1 Introduction In an earlier paper, Baum et al. (2003), we discussed instrumental variables (IV) estimators in the context of Generalized Method of Moments (GMM) estimation and presented Stata routines for estimation and testing comprising the ivreg2 suite. Since that time, those routines have been considerably enhanced and additional routines have been added to the suite. This paper presents the analytical underpinnings of both basic IV/GMM estimation and these enhancements and describes the enhanced routines. Some of these features are now also available in Stata 10’s ivregress, while others are not. The additions include: • Estimation and testing...
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...SOME PRACTICAL GUIDANCE FOR THE IMPLEMENTATION OF PROPENSITY SCORE MATCHING Marco Caliendo IZA, Bonn Sabine Kopeinig University of Cologne Abstract. Propensity score matching (PSM) has become a popular approach to estimate causal treatment effects. It is widely applied when evaluating labour market policies, but empirical examples can be found in very diverse fields of study. Once the researcher has decided to use PSM, he is confronted with a lot of questions regarding its implementation. To begin with, a first decision has to be made concerning the estimation of the propensity score. Following that one has to decide which matching algorithm to choose and determine the region of common support. Subsequently, the matching quality has to be assessed and treatment effects and their standard errors have to be estimated. Furthermore, questions like ‘what to do if there is choice-based sampling?’ or ‘when to measure effects?’ can be important in empirical studies. Finally, one might also want to test the sensitivity of estimated treatment effects with respect to unobserved heterogeneity or failure of the common support condition. Each implementation step involves a lot of decisions and different approaches can be thought of. The aim of this paper is to discuss these implementation issues and give some guidance to researchers who want to use PSM for evaluation purposes. Keywords. Propensity score matching; Treatment effects; Evaluation; Sensitivity analysis; Implementation 1. Introduction ...
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...Seminar Financial Risk Management 20011-2012 Erasmus University Testing the validity of Purchasing Power Parity: In time series and panel analysis using CPI, TPI and PPI. Supervisor: Everaert Gerdie Leonora Jean-Timothy Marsoufandis-Balomenos Nikolaos Venieris Michael February 10th, 2012 319006 356941 354890 Abstract The examination of the Purchasing power parity theory with reference to 22 (invluding the U.S) countries is the prime objective of the paper. Consumer price index (CPI), whole sale price/producer price index (PPI), traded price index (TPI) and nominal exchange rate are the variables which were investigated in this study for the period 1957Q1-1998Q4, on the basis of the mean reversion hypothesis. Engle-Granger co-integration and Unit root tests have been employed to both of variables and estimated residuals of the sample in order to test the purchasing power parity. Keywords: Purchasing Power Parity, unit-root and co-integration. Content 1. Introduction……………………………………………………………….….3 2. Literature review………………………………………………………….….4 3. Data……………………………………………………………………………7 4. Methodology………………………………………………………………….8 5. Empirical results………………………………………………………..…....11 5.1. 5.2. 5.3. 5.4. 5.5. Unit-root and cointegration results(CPI)……………………………....11 Unit-root and co-integration results(TPI)……………………………..15 Unit-root and co-integration results(PPI)……………………………..16 Half-life deviations from PPP…………………………………………..17 Forecast performance…………………………………………………...20 6....
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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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...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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...Case of Salary discrimination based on Gender In this case the assertion has been made that the males of the population receive a higher salary than do females as a direct result of their gender. In order to test this assertion, we must first set up a hypothesis, and then test that hypothesis by analyzing the data provided. The first step in hypothesis testing is formalizing the hypothesis into a null hypothesis and an alternative hypothesis. The null hypothesis is a restatement of the assertion being made, while the alternative hypothesis is the negation of the null hypothesis. The null and alternative hypotheses can be stated respectively as follows: H0 : The mean salary of females < the mean salary of males H1 : The mean salary of females > or = the mean salary of males Before proceeding with determining a difference, and cause and effect linear relationship between these two variables, it is important to note that any individual outliers must be eliminated from the data set before a multiple linear regression is performed. The existence of such outliers in a model could skew the resulting linear relationship. The performance of successive stem and leaf box plots revealed the existence of 6 outliers. These outliers were then removed from the sample Splitting the data set according to gender and comparing the means of the two groupings reveals a difference in the average salary of the sampled males and sampled females. This does not, however, tell...
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...have been conducted in this field. The following paper focuses on the pollution haven hypotheses stating that lax environmental regulations increase Foreign Direct Investment inflow since investing firms experience significant cost efficiencies and comparative advantages. The data set is mainly chosen from the World Data Bank and five explanatory variables are used to investigate their influence on FDI inflow (as percentage of GDP). During the empirical analysis a pivotal factor will be the OECD membership even if several environmental standards are controlled. We expect to see some significant determinants of FDI inflow in order to either agree or reject the pollution haven hypotheses. Contents 1 Introduction 2 The Two Hypotheses 3 Data Set 4 Econometric Model and Results 4.1 Linear Regression Model (OLS) . . . . . . . . . . . . . . . . . 4.2 Assumptions of Gauss-Markov-Theorem . . . . . . . . . . . . 4.3 Chow Test for Structural Break . . . . . . . . . . . . . . . . . 5 Conclusion A Appendix A.1 Program Code EViews . . . . . . . . . . . . . . . . . . . . . . 1 1 1 2 3 4 6 7 9 9 1 Introduction International trade theory is based on the concept of comparative advantages which is consistent with what we could observe in the booming globalization process during the last decades. A multinational firm...
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...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 use the...
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...CASE: 32 - Overdue Bills CONTENTS 1) The Executive Summary a) Describe the most important Facts and Conclusions. 2) Introduction a) Purpose and Scope of Paper b) Questions of Interest, and/or hypotheses c) Describe the nature of the data set 3) Analysis and methods section a) Interpret the statistical summaries i) Tell the reader what you have found in the data (results, facts only). ii) Explain what those findings mean with regard to the problem (interpret results). b) Design – describe the most important aspects of how the data was collected. 4) Conclusions and summary section a) What has the analysis revealed? How have your questions been answered? (Refers back to the questions of interest, problem statement, and/or hypothesis b) Why was the analysis done (Refer back to your background) c) What of value was discovered? (Any unexpected results) 5) References 6) Appendix Executive Summary Based on my analysis I was able to determine that the size of the bill has an effect on the number of days the bill is late. I was also able to determine that commercial and residential bills get paid at different times based on the amount of them. We can also say that with the slope of .0166 (residential) means for each increase of one unit in X, the Y is estimated to increase .0166 units. On the other side the commercial business the slope of -0.191 for each increase of one unit in x, the Y is estimated to increase -0.191. The regression analysis for commercial...
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...A Statistical Analysis of Case 32 Michael A. Wilson GM533 02/12/2012 INTRODUCTION The purpose of this analysis is to analyze the QSCA in determining if the amount or size of a bill is directly correlated to the number of days the bill is past due. In order to support the validity of this relationship, a statistical analysis of the data provided will support the relationship within 95% confidence levels. These findings should give a better understanding of the QSCA’s business and provide vital insight on the relationship between the data being evaluated. SUMMARY The focal point of this analysis is to determine whether or not the amount of the bill has an effect on the number of days the bill is late. This information will be extremely valuable for the business to develop higher efficiency and profitability within the account services team. In addition, the final output of the analysis can be applied to several situations, such as insights into customer trends like bill payments, financing, and the current economic impact on the bill collection business. This analysis will help confirm the importance of paying a bill on time and should be supported by the client services team in the management of bill collection. We are currently face with challenging economic times and the support of motivating clients to expedite their bill payments will help businesses and customer’s personal and internal finances. In order to validate the relationship between the amount of a bill...
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