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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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...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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...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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...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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...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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