...Basic Econometrics Tools Correlation and Regression Analysis Christopher Grigoriou Executive MBA – HEC Lausanne 2007/2008 1 A collector of antique grandfather clocks wants to know if the price received for the clocks increases linearly with the age of the clocks. The following model: yi=a0 + a1*x1i + εi , where yi=Auction price of the clock i, x1i=Age of clock (years), A sample of 32 auction prices of grandfather clocks, along with their age, is given in the next table. Table 1- Auction price and Age i 1 2 3 4 5 6 7 8 9 10 11 12 Auction Price, y 1235 1080 845 1522 1047 1979 1822 1253 1297 946 1713 1024 Age, x1 127 115 127 150 156 182 156 132 137 113 137 117 i 13 14 15 16 17 18 19 20 21 22 23 24 Auction Price, y 1147 1092 1152 1336 2131 1550 1884 2041 845 1483 1055 1545 Age, x1 137 153 117 126 170 182 162 184 143 159 108 175 i 25 26 27 28 29 30 31 32 Auction Price, y 729 1792 1175 1593 785 744 1356 1262 Age, x1 108 179 111 187 111 115 194 168 2 Correlation analysis (see Appendix 1) Figure 1- Scatter Plot- Auction Price and Age 2590 Auction Price, y 2 09 0 1 590 1 09 0 590 90 1 00 125 150 175 2 00 Age, x1 From this figure => positive correlation between Auction price and Age? 3 => Correlation coefficient Outil/Utilitaire d'Analyse/ Analyse de correlation/plages d’entrée/de sortie/ok ...
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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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...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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...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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...Financial Econometrics With Eviews Roman Kozhan Download free books at Roman Kozhan Financial Econometrics Download free eBooks at bookboon.com 2 Financial Econometrics – with EViews © 2010 Roman Kozhan & Ventus Publishing ApS ISBN 978-87-7681-427-4 To my wife Nataly Download free eBooks at bookboon.com 3 Contents Financial Econometrics Contents Preface 6 1 1.1 1.2 1.3 1.4 Introduction to EViews 6.0 Workfiles in EViews Objects Eviews Functions Programming in Eviews 7 8 10 18 22 2 2.1 2.2 2.3 Regression Model Introduction Linear Regression Model Nonlinear Regression 34 34 34 52 3 3.1 3.2 3.3 Univariate Time Series: Linear Models Introduction Stationarity and Autocorrelations ARMA processes 54 54 54 59 www.sylvania.com We do not reinvent the wheel we reinvent light. Fascinating lighting offers an infinite spectrum of possibilities: Innovative technologies and new markets provide both opportunities and challenges. An environment in which your expertise is in high demand. Enjoy the supportive working atmosphere within our global group and benefit from international career paths. Implement sustainable ideas in close cooperation with other specialists and contribute to influencing our future. Come and join us in reinventing light every day. Light is OSRAM Download free eBooks at bookboon.com 4 Click on the ad to read more Contents ...
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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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...CHAPTER ONE 1.1 INTRODUCTION A casual look at the published empirical work in business and econometric will reveal that many economic relationships are of single –equation type. In such models, one variable (the dependent variable Y) is expressed as a linear function of one or more other variables (the explanatory variables, the X’s). An implicit assumption is that the cause and effect relationship, if any , between Y and X’s is unidirectional. The explanatory variables are the cause and the dependent variable is the effect . However, there are situations where there is a two- way or simultaneous relationships between Y and some of the X’s which makes the distinction between the dependent and the explanatory variables of dubious value. It is better to lump together a set of variables that can be determined simultaneously by the remaining set of variables- precisely what is done in simultaneous equation models. In such models, there is more than one equation - one for each of the mutually or jointly dependent or endogenous variables. And unlike the single equation models, in the simultaneous equation models, one may estimate the parameters of a single equation without taking into account information provided by the other equation in the system. In a simultaneous equation system, variables that appear only on the right – hand side of the equation are called exogenous or predetermined variables. They are truly independent or non-stochastic because they remain fixed. Variables...
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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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...Donau-City-Strasse 1, A-1220 Vienna, Austria. Author for correspondence. E-mail: thomas.roediger@arcs.ac.at a Abstract. During the past 20 years, the world pharmaceutical industry has experienced a dramatic increase in R&D intensity. We apply and extend a model developed by Grabowski and Vernon (2000, Journal of Evolutionary Economics, 10, 201–215) with a pooled data sample of the 15 publicly listed Japanese drug firms for the period 1987– 1998. As in the original study, we find expected returns to be an important determinant of R&D spending in the Japanese drug industry, albeit considerably smaller than in the U.S., which is particularly obvious in the case of returns from newly introduced drugs. However, our results are sensitive to econometric model specification, in particular to controlling for serial correlation and to a dynamic specification of the baseline model. Likewise, estimates on financial constraints are sensitive to model specification, indicating that Japanese drug firms face small or no financial constraints. Our results are consistent with the general literature on R&D investment behaviour, yet raise some methodological questions with regard to the original study. Key words: investment, Japan, panel data estimation, pharmaceuticals, R&D. JEL Classifications: L65, O31, O33. I. Introduction During the past 20 years, the world pharmaceutical industry has experienced a dramatic increase in R&D intensity. In a recent paper, Grabowski and Vernon (2000) explore the...
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...ECO 6191A Labor Economics Human Capital and Education -----------The use of IV based on AK-91’s paper Content 1. Overview of the Human Capital……………………………………………3 (1) Origin of Human Capital…………………………………………………..3 (2) Theory of Human Capital………………………………………………….4 2. The methodology used in the paper………………………………………...5 (1) Ability Bias/Selection Bias………………………………………………….5 (2) Instrument Variables……………………………………………………….6 3. AK-91’s Paper………………………………………………………………...9 (1) Seansonal Pattern………………………………………….…………………….9 (2) Estimating the Return to Education………………..…………………….……….12 4. Conclusion……………………………………………………..……………...15 5. Reference……………………………………………………………………...16 1. Overview of the Human Capital It is obvious that human capital becomes a vital part in our real life. In order to improve the productivity, people want to invest in the human capital more than before. From the Wikipedia, it defines human capital as the stock of competencies, knowledge, social and personality attributes, including creativity, embodied in the ability to perform labor so as to produce economic value. In a simply word, I think human capital is the set of skills which an employee acquires on the job, through training and experience, and which increase that employee's value in the market place. (1) Origin of Human Capital In fact, no one realized the human capital before A.W.Lewis wrote his paper "Economic Development with...
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...ELECTRICITY CONSUMPTION AND ECONOMIC GROWTH IN NIGERIA: NEW INSIGHTS INTO THE CAUSALITY RELATIONSHIP AN ECONOMETRICS ASSIGNMENT SUBMITTED BY ------------------------------------------------- OLUWAFEMI JOSHUA IBRAHIM MATRIC NUMBER: 121937 LEVEL: 700 LECTURER IN-CHARGE: PROFESSOR E.O. OGUNKOLA November, 2010 1) STATEMENT OF THE PROBLEM Electricity plays a very important role in the socio-economic and technological development of every nation. It is widely accepted that there is a strong correlation between socio-economic development and the availability of electricity (Sambo, 2008). It is generally recognized that energy, including electricity, plays a significant role in economic development, not only because it enhances the productivity of capital , labour and other factors of production, but also that increased consumption, particularly commercial energy like electricity, signifies high economic status of a country(Aklas & Yilmaz, 2008). The relationship that exists between electricity consumption and economic growth has been of great interest to many researchers. The study of this relationship arises from the need to understand the complex links between these variables. Such understanding is basic to regulators and investors in deregulated electricity markets, in order to design a system that is reliable, efficient and growth-efficient. The empirical argument has been centered on whether economic growth responds to increase in electricity consumption, or whether...
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...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 that is robust to, and efficient in the presence of, arbitrary serial correlation. • A range of test statistics that allow the user to address the problems of underidentification or weak identification, including statistics that are robust in the presence of heteroskedasticity, autocorrelation or clustering. • Three additional IV/GMM estimators: the GMM continuously updated estimator (CUE) of...
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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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