...Independent Variables 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 Omitted Variables Irrelevant Variables An Illustration of the Misuse of Specification Criteria Specification Searches Lagged Independent Variables An Example of Choosing Independent Variables Summary and Exercises Appendix: Additional Specification Criteria Before any equation can be estimated, it must be completely specified. Specifying an econometric equation consists of three parts: choosing the correct independent variables, the correct functional form, and the correct form of the stochastic error term. A specification error results when any one of these choices is made incorrectly. This chapter is concerned with only the first of these, choosing the variables; the second and third will be taken up in later chapters. That researchers can decide which independent variables to include in regression equations is a source of both strength and weakness in econometrics. The strength is that the equations can be formulated to fit individual needs, but the weakness is that researchers can estimate many different specifications until they find the one that “proves” their point, even if many other results disprove it. A major goal of this chapter is to help you understand how to choose variables for your regressions without falling prey to the various errors that result from misusing the ability to choose. The primary consideration in deciding if an independent variable belongs in an equation is whether the variable is essential...
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...Variables in our modern world effecting University Students’ academic success Abstract This research investigates the factors and variables that affect students’ academic results. This research paper focuses on variables which include hours of work, gender, high School academic results, hours of study and what relationship it has with our dependent variable which is the GPA. We aim to discover what relationship our variables hold with the dependent variable; if it is significant or in significant to be able to determine if it is valid to be rejected or accepted in our model. This study will draw upon the raw data that has been collected by surveying 50 university students from the University of Western Sydney. We have provided students with a questionnaire for numerical and measurable data related to the variables. Our data is validated and reiterated by previous literature, and we have created our own questionnaire which collects academic data where GPA is influenced highly by the selected variables. Our research is presented using a cross section data with many subjects without regard to difference in times. The data is uploaded on an excel spread sheet and ran through a STATA program to allow us to see the significance and correlating levels. The findings of this research is deemed to be a great source of knowledge for education institutions, students and the government to be able to further understand what major factors affect a student’s results. These results could...
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...The Inaugural Coase Lecture An Introduction to Regression Analysis Alan O. Sykes* Regression analysis is a statistical tool for the investigation of relationships between variables. Usually, the investigator seeks to ascertain the causal effect of one variable upon another—the effect of a price increase upon demand, for example, or the effect of changes in the money supply upon the inflation rate. To explore such issues, the investigator assembles data on the underlying variables of interest and employs regression to estimate the quantitative effect of the causal variables upon the variable that they influence. The investigator also typically assesses the “statistical significance” of the estimated relationships, that is, the degree of confidence that the true relationship is close to the estimated relationship. Regression techniques have long been central to the field of economic statistics (“econometrics”). Increasingly, they have become important to lawyers and legal policy makers as well. Regression has been offered as evidence of liability under Title VII of the Civil Rights Act of , as evidence of racial bias in death penalty litigation, as evidence of damages in contract actions, as evidence of violations under the Voting Rights Act, and as evidence of damages in antitrust litigation, among other things. In this lecture, I will provide an overview of the most basic techniques of regression analysis—how they work, what they assume, Professor of Law, University of Chicago...
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...Econometrics = Science & art of using economic theory & statistical techniques to analyze economic data 1. Causal Effect & the Logic of Randomized Experiments Causal Relationships Decision depends on understanding relationships among variables Empirical research seek to reveal causal relationships: cause/treatment effect Treatment or costs = variables which are subject to intervention (change) Direction & magnitude of effects? Causal Question 1. Hormone Therapy does HRT risk of coronary events? Causal Question 2.Class size does redcing class size improce outcomes of elementary school? A. pupils get more attention, less class disruptions = better grades Smaller classes = expensive, only possible if they produce better outcomes Potential outcomes & treatment effects of binary treatments Outcome (yi) without treatment: Di = 0 Outcome (yi) with treatment: Di = 1 TE (treatment effect)= difference between potential outcomes: Counterfactuals: Fundamental problem of casual inference (Holland) Not able to observe both potential outcomes (y1i & y0i) (would need parallel world) Outcome that is not observable = counterfactual outcome Average treatment effects (ATE) Estimate average effect in target population (probability that y occurs when D has already happened) straightforward: simple comparison of means estimation of ATE 1. collect data from target population 2. identify individuals with/without treatment 3....
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...enrolments is not influenced significantly by the price or living conditions of the campus. When reading some articles I was personally very surprised about this since normally the living conditions should be influent more than the other factors in the enrolments. Based on this, I was very interested which other factors influence to students’ decisions. That is why I chose this dataset to analyze for the final term paper in the course of econometrics. 2. Multiple regressions 2.1 Data source The numerous data used in this paper (reference see below), consists of observations on six variables. The variables are: • enroll • priv = private collage • police • crime 2.2 Model specification At the first place it is necessary to run a regression with all variables: [pic] In the first step the variable priv is removed, because according to the t-test it has a low significance. In that case, this means that priv is not significant at the 10% level. [pic] In this decision...
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...For Students Solutions to Odd-Numbered End-of-Chapter Exercises * Chapter 2 Review of Probability 2.1. (a) Probability distribution function for Y Outcome (number of heads) | Y 0 | Y 1 | Y 2 | Probability | 0.25 | 0.50 | 0.25 | (b) Cumulative probability distribution function for Y Outcome (number of heads) | Y 0 | 0 Y 1 | 1 Y 2 | Y 2 | Probability | 0 | 0.25 | 0.75 | 1.0 | (c) . Using Key Concept 2.3: and so that 2.3. For the two new random variables and we have: (a) (b) (c) 2.5. Let X denote temperature in F and Y denote temperature in C. Recall that Y 0 when X 32 and Y 100 when X 212; this implies Using Key Concept 2.3, X 70oF implies that and X 7oF implies 2.7. Using obvious notation, thus and This implies (a) per year. (b) , so that Thus where the units are squared thousands of dollars per year. (c) so that and thousand dollars per year. (d) First you need to look up the current Euro/dollar exchange rate in the Wall Street Journal, the Federal Reserve web page, or other financial data outlet. Suppose that this exchange rate is e (say e 0.80 Euros per dollar); each 1 dollar is therefore with e Euros. The mean is therefore e C (in units of thousands of Euros per year), and the standard deviation is e C (in units of thousands of Euros per year). The correlation is unit-free, and is unchanged. 2.9. | | Value of Y | Probability Distribution of X | | | 14 | 22 | 30 |...
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...Is it true that Good Institutions Alleviate the Resource Curse? A Reassessment of Existing OLS Cross Country Evidence Malebogo Bakwena* Abstract The paper tests the hypothesis that the effect of resources on growth is conditional on the quality of institutions, by further building on Mehlum, Moene, and Torvik’s (2006b) influential work. Advances are made by re-testing the hypothesis, using: (a) a dataset of up to 53 countries over the period 1984-2003; and (b) a resource abundance indicator that focuses on non-renewable resources alone rather than the ones commonly used in the literature that include renewable resources, which are inappropriate. The empirical results of the paper confirm the hypothesis that resource rich economies are not destined to be cursed if they have good institutions. Keywords: World economic growth, resource curse, institutions * Senior Lecturer at the University of Botswana, Department of Economics, Private Bag 0022 Gaborone, Botswana. E-mail: thokweng@mopipi.ub.bw, Telephone: (+267) 3552151, Fax: (+267)3972936. 1 1. Introduction Contrary to conventional theory, a growing body of evidence suggests that economies with abundant natural resources perform badly in terms of economic growth relative to their resource poor counterparts—the so-called resource curse hypothesis. However, this general hypothesis is not robust. It clearly fails to account for the differing experiences of resource abundant economies. For instance, the theory, applied...
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...For Students Solutions to Odd-Numbered End-of-Chapter Exercises * Chapter 2 Review of Probability 2.1. (a) Probability distribution function for Y Outcome (number of heads) | Y 0 | Y 1 | Y 2 | Probability | 0.25 | 0.50 | 0.25 | (b) Cumulative probability distribution function for Y Outcome (number of heads) | Y 0 | 0 Y 1 | 1 Y 2 | Y 2 | Probability | 0 | 0.25 | 0.75 | 1.0 | (c) . Using Key Concept 2.3: and so that 2.3. For the two new random variables and we have: (a) (b) (c) 2.5. Let X denote temperature in F and Y denote temperature in C. Recall that Y 0 when X 32 and Y 100 when X 212; this implies Using Key Concept 2.3, X 70oF implies that and X 7oF implies 2.7. Using obvious notation, thus and This implies (a) per year. (b) , so that Thus where the units are squared thousands of dollars per year. (c) so that and thousand dollars per year. (d) First you need to look up the current Euro/dollar exchange rate in the Wall Street Journal, the Federal Reserve web page, or other financial data outlet. Suppose that this exchange rate is e (say e 0.80 Euros per dollar); each 1 dollar is therefore with e Euros. The mean is therefore e C (in units of thousands of Euros per year), and the standard deviation is e C (in units of thousands of Euros per year). The correlation is unit-free, and is unchanged. 2.9. | | Value of Y | Probability Distribution of X | | | 14 | 22 | 30 | 40 | 65 | | | Value of X | 1 |...
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...for AIDS Research (#07-1191, Brown PI). The authors also wish to thank James R. Bettman, Jane D. Brown, Gavan Fitzsimons, Rhonda Gibson, Joe Bob Hester, Joel Huber, Laurence W. Jacobs, Chuanshu Ji, Wagner Kamakura, Gary McClelland, Carl Mela, Andres Musalem, Jonathan Levav, Jason Roos, Woochoel Shin, Stephen Spiller, Rick Staelin, Ning Mena Wang, William D. Wells, Stacy Wood, and seminar participants at the Duke University for their assistance and comments. We thank Jon James for assistance with the Monte Carlo simulations. Any errors or omissions are the fault of the authors. 2 ABSTRACT Baron and Kenny’s (1986) framework for mediation analysis has become a standard part of the consumer researcher’s toolkit: an independent variable X affects some mediator M that in turn affects some...
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...Econometric Methods FIN5EME Semester 1, 2013 Assignment 2 * Cobb-Douglas cost function: TCi = µQiβ2 pi1β3 pi2β4 pi3β5 (1) Where, TCi= Total Cost for firm i Q= Output of firm i pi1= Wage Rate pi2= Rental Price of Capital pi3= Fuel Price * Taking the natural log of equation (1) log(TCi)= β1 + β2 log(Qi) + β3 log(pi1) + β4 log(pi2) + β5 log(pi3) + ei (2) where β1= (logµ) and ei= error term. * Eviews Output of the log-log model is as follows: Dependent Variable: LOG(TC) | | | Method: Least Squares | | | Date: 06/12/13 Time: 12:47 | | | Sample: 1 145 | | | | Included observations: 145 | | | | | | | | | | | | | Variable | Coefficient | Std. Error | t-Statistic | Prob. | | | | | | | | | | | C | -3.526503 | 1.774367 | -1.987471 | 0.0488 | LOG(Q) | 0.720394 | 0.017466 | 41.24448 | 0.0000 | LOG(WAGE) | 0.436341 | 0.291048 | 1.499209 | 0.1361 | LOG(CAPITAL) | -0.219888 | 0.339429 | -0.647819 | 0.5182 | LOG(FUEL) | 0.426517 | 0.100369 | 4.249483 | 0.0000 | | | | | | | | | | | R-squared | 0.925955 | Mean dependent var | 1.724663 | Adjusted R-squared | 0.923840 | S.D. dependent var | 1.421723 | S.E. of regression | 0.392356 | Akaike info criterion | 1.000578 | Sum squared resid | 21.55201 | Schwarz criterion | 1.103224 | Log likelihood | -67.54189 | Hannan-Quinn criter. | 1.042286 | F-statistic | 437.6863 | Durbin-Watson stat...
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...QMM 241 – HW#5 – Multiple Regression 13.21 – Data It is cross-sectional and the unit of observation is a single Noodles restaurant. 13.22 – Data conditioning Yes the X and Y data is well-conditioned. The Y variable will be the Sales/SqFt which we will be able to figure out with a formula containing all the X variables and the variable magnitudes are all similar in all categories. 13.23 – Predicted signs Variable | Sign | Reason | Seats-Inside | Positive | The larger the size of the restaurant, the higher the sales will be. | Seats-Patio | Positive | The higher number of seats, the higher the revenue will be. | MedIncome | Positive | The higher the income of customers, the higher the sales will be. | MedAge | Positive | The older the potential customers, the higher the revenue will be. | BachDeg% | Positive | More education would be positively correlated with higher income so there will be higher revenue like Income. | 13.24 – Sample Size 75/5=14.8. The data set meets both Evan’s and Donae’s Rules. 13.25 – Regression The estimated regression equation is = 429.5114 − 1.8149Seats-Inside + 1.2719Seats-Patio − 2.1021MedIncome − 0.0158MedAge + 8.6604BachDeg%. These signs do not match our a priori reasoning for Seats-Inside, MedIncome, and MedAge. Regression Analysis | | | | | | | | | | | | | | R² | 0.233 | | | | | | Adjusted R² | 0.177 | n | 74 | | | | R | 0.483 | k | 5 | | | | Std. Error | 124.529 | Dep. Var...
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...Quality and Firm Value in Brazil Abstract This paper investigates the influence of corporate governance quality on market value of 154 Brazilian listed companies in 2002. In order to obtain a proxy for corporate governance quality, a broad governance index was built. The investigation was carried out through different econometric approaches in increasing order of complexity, including multiple regressions by Ordinary Least Squares (OLS), instrumental variables approach and systems of simultaneous equations. The results obtained in all econometric approaches show a positive and significant influence of corporate governance quality on firm’s market value. OLS results with market value variables Tobin’s Q and PBV multiple suggest that, ceteris paribus, a worst-to-best change in governance quality would result in a market capitalization increase of around 85% and 100%, respectively. The paper also finds evidence of corporate governance variable endogeneity and proposes different instruments for estimation by instrumental variables approach. Moreover, results by simultaneous equation approach indicate a relation of...
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...imposed taxes or labour conditions. Measuring these effects is not as straight forward as it might seem and various studies 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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...Unemployment and crime: New evidence for an old question Kerry L. Papps Victoria University of Wellington Rainer Winkelmann* IZA and Centre for Economic Policy Research, London December 1999 This paper uses panel data techniques to examine the relationship between unemployment and a range of categories of crime in New Zealand. The data cover sixteen regions over the period 1984 to 1996. Random and fixed effects models are estimated to investigate the possibility of a causal relationship between unemployment and crime. Hypothesis tests show that two-way fixed effects models should be used. The main result of the paper is that there is some evidence of significant effects of unemployment on crime, both for total crime and for some subcategories of crime. We are grateful to Rachel Bambery, New Zealand Police National Headquarters, for her assistance in obtaining crime and population statistics. The staff of the University of Canterbury Library also gave invaluable help in unraveling the complexities of New Zealand unemployment and income data. The paper has benefited from useful comments by two anonymous referees, Simon Kemp, Jacques Poot and participants of the CEPR conference on “Metropolitan Economic Performance”, Lisbon, October 1998. *Corresponding author: IZA, P.O. Box 7240, 53072 Bonn, Germany; winkelmann@iza.org. “I know only of three ways of living in society: one must be a beggar, a thief, or a wage earner.” HONORÉ de MIRABEAU (1749-1791) 1. Introduction ...
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...article demonstrates historically and statistically that conversionary Protestants (CPs) heavily influenced the rise and spread of stable democracy around the world. It argues that CPs were a crucial catalyst initiating the development and spread of religious liberty, mass education, mass printing, newspapers, voluntary organizations, and colonial reforms, thereby creating the conditions that made stable democracy more likely. Statistically, the historic prevalence of Protestant missionaries explains about half the variation in democracy in Africa, Asia, Latin America and Oceania and removes the impact of most variables that dominate current statistical research about democracy. The association between Protestant missions and democracy is consistent in different continents and subsamples, and it is robust to more than 50 controls and to instrumental variable analyses. ocial scientists tend to ignore religion in the processes of post-Enlightenment modernization. In individual cases and events, the role of religious actors is clear—especially in the primary documents. Yet in broad histories and comparative analyses, religious groups are pushed to the periphery, only to pop out like a jack-in-the-box from time to time to surprise and scare people and then shrink back into their box to let the important historical changes be directed by “secular” actors and forces (Butler 2004). Yet integrating religious actors and motivations into narratives about the rise and spread...
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