...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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...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 use...
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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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...A STUDY: INCOME AND HAPPINESS ACROSS EUROPE: DO REFERENCE VALUES MATTER? Contents Summary 2 The Silver Lining of Materialism: The Impact of Luxury Consumption on Subjective Well-Being 3 Data used for analysis 5 Econometric Model 6 Critical Reflection 8 Reference List 11 A Study: Income and happiness across Europe: Do reference values matter? Summary The authors in this study - Guglielmo Maria Caporale, Yannis Georgellis, Nicholas Tsitsianis and Ya Ping Yin - assess the relationship between income and subjective well-being; by tracing back to works of Adam Smith, Karl Marx, Veblen and Duesenberry, the authors revive significant attention to the neoclassical economic theory that portrays well-being and absolute income to be highly correlated. The data was retrieved from the European Social Survey (ESS) to examine a potential relationship between income and happiness (self-reported satisfaction), the authors execute their study across 19 European Countries. With utilisation of Easterlin (1974) as their seminal paper, the research draws attention to the Easterlin Paradox (Easterlin, 1995) that suggests there is no relationship between economic expansion in industrialised countries and its average level of happiness. The authors look to assess topical contradictory findings that conclude absolute income is correlated with levels of happiness (e.g.: Frijters et al., 2004). The research purpose of this paper was to re-examine this controversial link for...
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...ARTICLE IN PRESS Journal of Econometrics ] (]]]]) ]]]–]]] www.elsevier.com/locate/jeconom Modeling the diffusion of scientific publications Dennis Fok, Philip Hans Fransesà Econometric Institute, Erasmus University Rotterdam, P.O. Box 1738, NL-3000 DR Rotterdam, The Netherlands Abstract This paper illustrates that salient features of a panel of time series of annual citations can be captured by a Bass type diffusion model. We put forward an extended version of this diffusion model, where we consider the relation between key characteristics of the diffusion process and features of the articles. More specifically, parameters measuring citations’ ceiling and the timing of peak citations are correlated with specific features of the articles like the number of pages and the number of authors. Our approach amounts to a multi-level non-linear regression for a panel of time series. We illustrate our model for citations to articles that were published in Econometrica and the Journal of Econometrics. Amongst other things, we find that more references lead to more citations and that for the Journal of Econometrics peak citations of more recent articles tend to occur later. r 2006 Elsevier B.V. All rights reserved. JEL classification: C33; M21 Keywords: Diffusion of innovations; Multi-level regression 1. Introduction Citations to scientific publications like journal articles often show characteristics that bear similarities with the diffusion of a new product. Shortly after publication...
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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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...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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...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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...EXECUTIVE SUMMARY. A case study seeks to associate with reward and employee motivation and identify association between employee motivation and employee motivation variables for instance gender, age, education, and income level and job experience in banking in Pakistan. The study should be based on primary data and sample size, by use of questionnaires. The partners are, therefore, in case of a business opportunity should apply relevant entrepreneurial skills in order to succeed especially by following the following path. Two hypotheses were developed for the present study and were tested by using Chi-square Test and binary Regression Test. The result of Chi-square shows that P- value is 0.048 of Chi-square its mean there is an association between salary and gender. The correlation between rewards and Employee Motivation is 0.546, which shows the positive relationship between compensation and employee motivation Payment structure is a crucial element in a business plan and should be carefully be looked into for instance the partners should make sure that they recruit a reasonable number of workers that they are able to manage. Objectives of this study (i).To determine if there is an association between rewards and employee motivation, biographical factors (Gender, age, education, qualification, and Income level). (ii).To identify the types of reward system in Pakistani Banks. (iii).To determine the impacts of rewards system on Banks employees’ biographical variables. (iv)...
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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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...[pic]University of Illinois at Chicago College of Business Administration Department of Information & Decision Sciences [pic] IDS 578 Research Methodology II Instructor Stanley L. Sclove [pic] The Sears, Roebuck Case These notes Copyright © 2005 Stanley Louis Sclove [pic] HyperTable of Contents 0. Introduction; Key Terms 1. What is Structural Equation Modeling? 2. A Simple Example of SEM 3. The Role of Theory in Structural Equation Modeling 4. Developing A Modeling Strategy 5. Estimating a Path Model with Structural Equation Modeling References [pic] 0. Introduction; Key Terms Preview In this course we consider Structural Equation Models. This note anticipates much material which will be discussed in more detail in the course or in the text (Schumacker and Lomax 2004). The note is based on a Harvard case, together with an example from Hair et al. and some introductory material. Key Terms The diagrams used in SEM are called path diagrams. The response variables are endogenous; the explanatory variables, exogenous. The modeling proceeds in terms of concepts, represented by constructs, measured by variables called indicators of the constructs. The model relating the indicators to the constructs is called the measurement model. The model relating the constructs to one another is called the structural model. [pic] 1. What is Structural Equation Modeling? Structural equation...
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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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...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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...in mechanisms to improve the health of society. This papers tries to find the relation with a minute but important application of this advancement; Prenatal Care for better growth and development of children. It tackles the question of whether the impact of prenatal care is significant on an infant’s health or not. The data used is a cross-sectional data of 1832 observations, with 29 variables. Controlling for different relevant variables like demographics, age, education etc. we try to find a causal relation between the impact of prenatal care and birth weight. And in the end we conclude our results that a positive relationship is indeed present and significant, leaving room for data limitations and possible solutions for future research. IMPACT OF PRENATAL CARE ON BIRTH WEIGHT Shehryar Amin Waqas Sheikh Hafiz Moazam Ali Nasir Javed Econometrics 2 Introduction Birth weight is a key factor for a child’s health and wellness. Not only it is important to prevent infant mortality rate but also a healthier birth weight helps reduce the increased risks of illnesses faced by babies born with low birth weight. Also in a statistical brief for the state center for health statistics of North Carolina1, it was found that compared with children born with normal birth weight, low birth weight children were more at a risk for poorer health and also low educational outcomes through childhood and adolescence. They are more likely to have special health care needs and require additional...
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