Chapter 9 Assessing Studies Based on Multiple Regression

Save this PDF as:

Size: px
Start display at page:

Transcription

1 Chapter 9 Assessing Studies Based on Multiple Regression Solutions to Empirical Exercises 1. Age 0.439** (0.030) Age 2 Data from 2004 (1) (2) (3) (4) (5) (6) (7) (8) Dependent Variable AHE ln(ahe) ln(ahe) ln(ahe) ln(ahe) ln(ahe) ln(ahe) ln(ahe) 0.024** (0.002) 0.147** (0.042) ** (0.0007) 0.146** (0.042) ** (0.0007) 0.190** (0.056) ** (0.0009) 0.117* (0.056) (0.0009) ln(age) 0.725** (0.052) Female Age (0.084) Female Age Bachelor Age (0.083) Bachelor Age Female 3.158** (0.176) Bachelor 6.865** (0.185) 0.180** 0.405** 0.180** 0.405** 0.180** 0.405** 0.210** 0.378** Female Bachelor 0.064** (0.021) Intercept (0.897) 1.856** (0.053) F-statistic and p-values on joint hypotheses (a) F-statistic on terms involving Age (0.177) (0.613) (0.612) * (1.230) 0.378** 0.063** (0.021) (0.819) (b) Interaction terms 4.12 with Age and Age 2 (0.02) 0.210** (1.228) 0.066** (0.021) (0.819) (0.064) (0.0011) (0.084) (0.084) (1.239) (1.239) 0.066** (0.021) (0.945) SER R Significant at the *5% and **1% significance level.

2 Solutions to Empirical Exercises in Chapter Age 0.461** (0.028) Age 2 Data from 1992 (1) (2) (3) (4) (5) (6) (7) (8) Dependent Variable AHE ln(ahe) ln(ahe) ln(ahe) ln(ahe) ln(ahe) ln(ahe) ln(ahe) 0.027** (0.002) 0.157** (0.041) ** (0.0006) 0.156** (0.041) ** (0.0007) 0.120* (0.057) (0.0010) 0.138* (0.054) * (0.0009) ln(age) 0.786** (0.052) Female Age (0.083) Female Age (0.0013) Bachelor Age (0.084) Bachelor Age Female 2.698** (0.152) Bachelor 5.903** (0.169) 0.167** 0.377** 0.167** 0.377** 0.167** 0.377** 0.200** (0.013) 0.340** Female Bachelor 0.085** (0.020) Intercept (0.815) 1.776** (0.054) F-statistic and p-values on joint hypotheses (a) F-statistic on terms involving Age (0.178) (0.608) (0.608) ** (1.212) 0.340** 0.079** (0.020) (0.828) (b) Interaction terms 9.04 with Age and Age ** (0.013) 0.365** (1.227) 0.086** (0.020) (0.780) (0.01) (0.065) * (0.0011) (0.083) (0.083) (1.213) (1.226) 0.080** (0.02) (0.959) SER R Significant at the *5% and **1% significance level. (a) (1) Omitted variables: There is the potential for omitted variable bias when a variable is excluded from the regression that (i) has an effect on ln(ahe) and (ii) is correlated with a variable that is included in the regression. There are several candidates. The most important is a worker s Ability. Higher ability workers will, on average, have higher earnings and are more likely to go to college. Leaving Ability out of the regression may lead to omitted variable bias, particularly for the estimated effect of education on earnings. Also omitted from the regression is Occupation. Two workers with the same education (a BA for example) may have different occupations (accountant versus 3 rd grade teacher) and have different earnings. To the extent that occupation choice is correlated with gender, this will lead to omitted variable bias. Occupation choice could also be correlated with Age. Because the data are a cross section, older workers entered the labor force before younger workers (35 yearolds in the sample were born in 1969, while 25 year-olds were born in 1979), and their occupation reflects, in part, the state of the labor market when they entered the labor force.

3 134 Stock/Watson - Introduction to Econometrics - Second Edition (2) Misspecification of the functional form: This was investigated carefully in exercise 8.1. There does appear to be a nonlinear effect of Age on earnings, which is adequately captured by the polynomial regression with interaction terms. (3) Errors-in-variables: Age is included in the regression as a proxy for experience. Workers with more experience are expected to earn more because their productivity increases with experience. But Age is an imperfect measure of experience. (One worker might start his career at age 22, while another might start at age 25. Or, one worker might take a year off to start a family, while another might not). There is also potential measurement error in AHE as these data are collected by retrospective survey in which workers in March 2005 are asked about their average earnings in (4) Sample selection: The data are full-time workers only, so there is potential for sampleselection bias. (5) Simultaneous causality: This is unlikely to be a problem. It is unlikely that AHE affects Age or gender. (6) Inconsistency of OLS standard errors: Heteroskedastic robust standard errors were used in the analysis, so that heteroskedasticity is not a concern. The data are collected, at least approximately, using i.i.d. sampling, so that correlation across the errors is unlikely to be a problem. (b) Results for 1988 are shown in the table above. Using results from (8), several conclusions were reached in E8.1(l) using the data from These are summarized in the table below, and are followed by a similar table for the 1998 data. Results using (8) from the 2004 Data Predicted Value of ln(ahe) at Age Predicted Increase in ln(ahe) (Percent per year) Gender, Education to to 35 Females, High School % 0.8% Males, High School % 0.5% Females, BA % 1.3% Males, BA % 1.0%

4 Solutions to Empirical Exercises in Chapter Results using (8) from the 1998 Data Predicted Value of ln(ahe) at Age Predicted Increase in ln(ahe) (Percent per year) Gender, Education to to 35 Females, High School Males, High School Females, BA Males, BA Based on the 2004 data E81.1(l) concluded: Earnings for those with a college education are higher than those with a high school degree, and earnings of the college educated increase more rapidly early in their careers (age 25 32). Earnings for men are higher than those of women, and earnings of men increase more rapidly early in their careers (age 25 32). For all categories of workers (men/women, high school/college) earnings increase more rapidly from age than from All of these conclusions continue to hold for the 1998 data (although the precise values for the differences change somewhat.) 2. We begin by discussing the internal and external validity of the results summarized in E8.2. Internal Validity 1. Omitted variable bias. It is always possible to think of omitted variables, but the relevant question is whether they are likely to lead to substantial omitted variable bias. Standard examples like instructor diligence, are likely to be major sources of bias, although this is speculation and the next study on this topic should address these issues (both can be measured). One possible source of OV bias is the omission of the department. French instructors could well be more attractive than chemists, and if French is more fun (or better taught) than chemistry then the department would belong in the regression, and its omission could bias the coefficient on Beauty. It is difficult to say whether this is a major problem or not, one approach would be to put in a full set of binary indicators for the department and see if this changed the results. We suspect this is not an important effect, however this must be raised as a caveat. 2. Wrong functional form. Interactions with Female showed some evidence of nonlinearity. It would be useful to see if Beauty 2 enters the regression. (We have run the regression, and the t-statistic on Beauty 2 is 1.15.) 3. Measurement error in the regressors. The Beauty variable is subjectively measured so that it will have measurement error. This is plausibly a case in which the measurement error is more or less random, reflecting the tastes of the six panelists. If so, then the classical measurement error model, in which the measured variable is the true value plus random noise, would apply. But this model implies that the coefficient is biased down so the actual effect of Beauty would be greater than is implied by the OLS coefficient. This suggests that the regressions understate the effect of Beauty.

5 136 Stock/Watson - Introduction to Econometrics - Second Edition 4. Sample selection bias. The only information given in this exam about the sample selection method is that the instructors have their photos on their Web site. Suppose instructors who get evaluations below 3.5 are so embarrassed that they don t put up their photos, and suppose there is a large effect of Beauty. Then, of the least attractive instructors, the only ones that will put up their photos are those with particular teaching talent and commitment, sufficient to overcome their physical appearance. Thus the effect of physical appearance will be attenuated because the error term will be correlated with Beauty (low values of Beauty means there must be a large value of u, else the photo wouldn t be posted.) This story, while logically possible, seems a bit far-fetched, and whether an instructor puts up his or her photo is more likely to be a matter of departmental policy, whether the department has a helpful webmaster and someone to take their photo, etc. So sample selection bias does not seem (in my judgment) to be a potentially major threat. 5. Simultaneous causality bias. There is an interesting possible channel of simultaneous causality, in which good course evaluations improve an instructor s self-image which in turn means they have a more resonant, open, and appealing appearance and thus get a higher grade on Beauty. Against this, the panelists were looking at the Web photos, not their conduct in class, and were instructed to focus on physical features. So for the Beauty variable as measured, this effect is plausibly large. External Validity The question of external validity is whether the results for UT-Austin in can be generalized to, say, Harvard or Cal-State University Northridge (CSUN) in The years are close, so the question must focus on differences between students and the instructional setting. 1. Are UT-Austin students like Harvard (or CSUN) students? Perhaps Beauty matters more or less to Harvard (CSUN) students? 2. Do the methods of instruction differ? For example, if beauty matters more in small classes (where you can see the instructor better) and if the distribution of class size at UT-Austin and Harvard (CSUN) were substantially different, then this would be a threat to external validity. Policy Advice As an econometric consultant, the question is whether this represents an internally and externally valid estimate of the causal effect of Beauty, or whether the threats to internal and/or external validity are sufficiently severe that the results should be dismissed as unreliable for the purposes of the Dean. A correct conclusion is one that follows logically from the systematic discussion of internal and external validity. We would be surprised if the threats to internal and external validity above are sufficiently important, in a quantitative sense, to change the main finding from E8.2 that the effect of Beauty is positive and quantitatively large. So our advice, solely econometric consultants, would be that implementing a policy of affirmative action for attractive people (all else equal, higher the better-looking) would, in expectation, improve course evaluations. This said, a good econometric policy advisor always has some advice about the next study. One thing that next study could do is focus on institutions like your s. (UT-Austin students and professors might be different from students at Harvard or CSUN), and collect data on some potential omitted variables (department offering the course, etc.). A very different study would be to do a randomized controlled experiment that would get directly at the policy question. Some department heads would be instructed to assign their most attractive teachers to the largest introductory courses (treatment group), others would be instructed to maintain the status quo (control group). The study would assess whether there is an improvement in evaluation scores (weighted

6 Solutions to Empirical Exercises in Chapter by class size) in the treatment group. A positive result would indicate that this treatment results in an increase in customer satisfaction. Finally, some thoughts that were out of bounds for this question, but would be relevant and important to raise in the report of an econometric consultant to the Dean. First, the Course Evaluation score is just a student evaluation, not a measure of what students actually learned or how valuable the course was; perhaps an assessment of the value of the course, five years hence, would produce a very different effect of Beauty, and that is arguably a more important outcome than the end-of-semester evaluation (this could be thought of as a threat to external validity, depending on how one defines the Dean s goal). Second, academic output is not solely teaching, and there is no reason at all that the results here would carry over to an analysis of research output, or even graduate student advising and teaching (the data are only for undergrad courses); indeed, the sign might be the opposite for research. Third, the econometric consultant could raise the question of whether Beauty has the same moral status as gender or race, even if it does not have the same legal status as a legally protected class; answering this question is outside the econometric consultant s area of expertise, but it is a legitimate question to raise and to frame so that others can address it. 3. (a) (1) Omitted variables: This is potentially important. For example, students from wealthier families might live closer to colleges and have higher average years of completed education. The estimated regression attempts to control for family wealth using the variables Incomehi and Ownhome, but these are imperfect measures of wealth. (2) Misspecification of the function form: This was investigated in the Chapter 8 empirical exercise. (3) Errors-in-variables: It has already been noted that Incomehi and Ownhome are imperfect measures of family wealth. Years of completed education may also be imprecisely measured as the data description makes clear. (4) Sample Selection: This is a random sample of high school seniors, so sample selection within this population is unlikely to be a problem. However, when considering external validity, these results are not likely to hold for the general population of high school students, some of which may drop out before their senior year. (5) Simultaneous causality: The argument here would be that parents who want to send their children to college may locate closer to a college. This is possible, but the effect is likely to be small. (6) Inconsistency of standard errors: Heteroskedasticity-robust standard errors were used. The data represent a random sample so that correlation across the error terms is not a problem. Thus, the standard errors should be consistent. (b) The table below shows the results for the non-west regions analyzed earlier along with the results for the West. The sample from the West contains 943 observations, compared to 3796 in the non-west sample. This means that the standard errors for the estimated coefficients in the West will be roughly twice as large as the standard errors in the non-west sample. (The ratio of the standard errors will nnon West be roughly.) n West Because the samples are independent, the standard errors for the estimated difference in the coefficients can be calculated as SE ˆ ˆ SE ˆ SE ˆ 2 2 ( βnon West βwest ) = ( βnon West ) + ( βwest ). For example, the standard error for the difference between the non-west and West coefficients on 2 2 Dist is (0.028) + (0.045) = The coefficients on Dist and Dist 2 in the West are very similar to the values for the non-west. This means that the estimated regression coefficients are similar. The interaction terms Incomehi Dist

7 138 Stock/Watson - Introduction to Econometrics - Second Edition and Incomehi Dist 2 look different. In the non-west, the estimated regression function for high income students was essentially flat (E8.3(h)), while the estimated regression coefficient in the West for students with Incomehi = 1 is very similar to the regression function for students with Incomehi = 0. However, the coefficients on the interaction terms for the West sample are imprecisely estimated and are not statistically different from the non-west sample. Indeed, the only statistically significant coefficient across the two samples is the coefficient on Bytest. The 2 2 difference is = 0.20, which has a standard error of =

8 Solutions to Empirical Exercises in Chapter Regession Results for Non-Western and Western States Non-West West Dist 0.110** (0.028) 0.092* (0.045) Dist * (0.0022) (0.0031) Tuition 0.210* (0.099) 0.523* (0.242) Female 0.141** (0.050) 0.051** (0.100) Black 0.333** (0.068) 0.067** (0.182) Hispanic 0.323** (0.078) 0.196** (0.115) Bytest 0.093** (0.003) 0.073** (0.006) Incomehi 0.217* (0.090) 0.407* (0.169) Ownhome 0.144* (0.065) 0.199* (0.127) DadColl 0.663** (0.087) 0.441** (0.144) MomColl 0.567** (0.122) 0.283** (0.262) DadColl MomColl (0.164) (0.330) Cue ** 0.045** (0.023) Stwmfg 0.042* (0.020) 0.031* (0.044) Incomehi Dist 0.124* (0.062) 0.005* (0.090) Incomehi Dist (0.0062) (0.0057) Intercept 9.042** (0.251) 9.227** (0.524) F-statistics (p-values) and measures of fit (a) Dist and Dist (0.000) 2.66 (0.070) (b) Interaction terms Incomehi Dist and 2.34 (0.096) 0.01 (0.993) Incomehi Dist 2 SER R n Significant at the *5% and **1% significance level.

Discussion Section 4 ECON 139/239 2010 Summer Term II

Discussion Section 4 ECON 139/239 2010 Summer Term II 1. Let s use the CollegeDistance.csv data again. (a) An education advocacy group argues that, on average, a person s educational attainment would increase

Instrumental Variables Regression. Instrumental Variables (IV) estimation is used when the model has endogenous s.

Instrumental Variables Regression Instrumental Variables (IV) estimation is used when the model has endogenous s. IV can thus be used to address the following important threats to internal validity: Omitted

ECON 523 Applied Econometrics I /Masters Level American University, Spring 2008. Description of the course

ECON 523 Applied Econometrics I /Masters Level American University, Spring 2008 Instructor: Maria Heracleous Lectures: M 8:10-10:40 p.m. WARD 202 Office: 221 Roper Phone: 202-885-3758 Office Hours: M W

Econ 371 Problem Set #3 Answer Sheet

Econ 371 Problem Set #3 Answer Sheet 4.3 In this question, you are told that a OLS regression analysis of average weekly earnings yields the following estimated model. AW E = 696.7 + 9.6 Age, R 2 = 0.023,

Research Design. Relationships in Nonexperimental Research. Nonexperimental Research Designs and Survey Research. Katie Rommel-Esham Education 504

Nonexperimental Research Designs and Survey Research Katie Rommel-Esham Education 504 Research Design Research design deals with the ways in which data are gathered from subjects Relationships in Nonexperimental

Nonexperimental Research Designs and Survey Research. Katie Rommel-Esham Education 604

Nonexperimental Research Designs and Survey Research Katie Rommel-Esham Education 604 Research Design Research design deals with the ways in which data are gathered from subjects Relationships in Nonexperimental

Validity of Selection Criteria in Predicting MBA Success

Validity of Selection Criteria in Predicting MBA Success Terry C. Truitt Anderson University Abstract GMAT scores are found to be a robust predictor of MBA academic performance. Very little support is

2. Linear regression with multiple regressors

2. Linear regression with multiple regressors Aim of this section: Introduction of the multiple regression model OLS estimation in multiple regression Measures-of-fit in multiple regression Assumptions

Education and Wage Differential by Race: Convergence or Divergence? *

Education and Wage Differential by Race: Convergence or Divergence? * Tian Luo Thesis Advisor: Professor Andrea Weber University of California, Berkeley Department of Economics April 2009 Abstract This

Empirical Methods in Applied Economics

Empirical Methods in Applied Economics Jörn-Ste en Pischke LSE October 2005 1 Observational Studies and Regression 1.1 Conditional Randomization Again When we discussed experiments, we discussed already

Solución del Examen Tipo: 1

Solución del Examen Tipo: 1 Universidad Carlos III de Madrid ECONOMETRICS Academic year 2009/10 FINAL EXAM May 17, 2010 DURATION: 2 HOURS 1. Assume that model (III) verifies the assumptions of the classical

ECON Introductory Econometrics. Lecture 15: Binary dependent variables

ECON4150 - Introductory Econometrics Lecture 15: Binary dependent variables Monique de Haan (moniqued@econ.uio.no) Stock and Watson Chapter 11 Lecture Outline 2 The linear probability model Nonlinear probability

ECON Introductory Econometrics. Lecture 17: Experiments

ECON4150 - Introductory Econometrics Lecture 17: Experiments Monique de Haan (moniqued@econ.uio.no) Stock and Watson Chapter 13 Lecture outline 2 Why study experiments? The potential outcome framework.

Econ 371 Exam #4 - Practice

Econ 37 Exam #4 - Practice Multiple Choice (5 points each): For each of the following, select the single most appropriate option to complete the statement. ) The following will not cause correlation between

Determining Future Success of College Students

Determining Future Success of College Students PAUL OEHRLEIN I. Introduction The years that students spend in college are perhaps the most influential years on the rest of their lives. College students

Conducting an empirical analysis of economic data can be rewarding and

CHAPTER 10 Conducting a Regression Study Using Economic Data Conducting an empirical analysis of economic data can be rewarding and informative. If you follow some basic guidelines, it is possible to use

Career Outcomes of STEM and Non- STEM College Graduates: Persistence in Majored-Field and Influential Factors in Career Choices

Career Outcomes of STEM and Non- STEM College Graduates: Persistence in Majored-Field and Influential Factors in Career Choices Yonghong Jade Xu, Ph.D. University of Memphis AIR 2012, New Orleans Background

Name (Please Print): Red ID: EC 351 : Econometrics I TEST 3 April 15, 011 100 points Instructions: Provide all answers on this exam paper. You must show all work to receive credit. You are allowed to use

MULTIPLE REGRESSION AND ISSUES IN REGRESSION ANALYSIS

MULTIPLE REGRESSION AND ISSUES IN REGRESSION ANALYSIS MSR = Mean Regression Sum of Squares MSE = Mean Squared Error RSS = Regression Sum of Squares SSE = Sum of Squared Errors/Residuals α = Level of Significance

Is the person a permanent immigrant. A non permanent resident. Does the person identify as male. Person appearing Chinese

Cole Sprague Kai Addae Economics 312 Canadian Census Project Introduction This project is based off of the 2001 Canadian Census data, and examines the relationship between wages and education, while controlling

Wooldridge, Introductory Econometrics, 4th ed. Chapter 15: Instrumental variables and two stage least squares

Wooldridge, Introductory Econometrics, 4th ed. Chapter 15: Instrumental variables and two stage least squares Many economic models involve endogeneity: that is, a theoretical relationship does not fit

The Benefits of Community Service Employment (PY2006)

The Benefits of Community Service Employment (PY2006) Prepared for Senior Service America, Inc. By The Charter Oak Group, LLC April 2008 The Benefits of Community Service The customer satisfaction data

1. Suppose that a score on a final exam depends upon attendance and unobserved factors that affect exam performance (such as student ability).

Examples of Questions on Regression Analysis: 1. Suppose that a score on a final exam depends upon attendance and unobserved factors that affect exam performance (such as student ability). Then,. When

1 OLS under Measurement Error

ECON 370: Measurement Error 1 Violations of Gauss Markov Assumptions: Measurement Error Econometric Methods, ECON 370 1 OLS under Measurement Error We have found out that another source of endogeneity

Good luck! BUSINESS STATISTICS FINAL EXAM INSTRUCTIONS. Name:

Glo bal Leadership M BA BUSINESS STATISTICS FINAL EXAM Name: INSTRUCTIONS 1. Do not open this exam until instructed to do so. 2. Be sure to fill in your name before starting the exam. 3. You have two hours

Economic Brief. The Role of Option Value in College Decisions

Economic Brief April 2016, EB16-04 The Role of Option Value in College Decisions By Jessie Romero and Nicholas Trachter Despite the large and persistent wage premium earned by college graduates, college

Economics of Strategy (ECON 4550) Maymester 2015 Applications of Regression Analysis

Economics of Strategy (ECON 4550) Maymester 015 Applications of Regression Analysis Reading: ACME Clinic (ECON 4550 Coursepak, Page 47) and Big Suzy s Snack Cakes (ECON 4550 Coursepak, Page 51) Definitions

Answer: C. The strength of a correlation does not change if units change by a linear transformation such as: Fahrenheit = 32 + (5/9) * Centigrade

Statistics Quiz Correlation and Regression -- ANSWERS 1. Temperature and air pollution are known to be correlated. We collect data from two laboratories, in Boston and Montreal. Boston makes their measurements

Does Macro/Micro Course Sequencing Affect Student Performance in Principles of Economics Courses?

JOURNAL OF ECONOMICS AND FINANCE EDUCATION Volume 2 Number 2 Winter 2003 30 Does Macro/Micro Course Sequencing Affect Student Performance in Principles of Economics Courses? Dr. Andy Terry 1 and Dr. Ken

The puzzle of graduate wages. IFS Briefing Note BN185. Richard Blundell David Green Wenchao Jin

The puzzle of graduate wages IFS Briefing Note BN185 Richard Blundell David Green Wenchao Jin The puzzle of graduate wages Richard Blundell, David Green and Wenchao Jin 1 Institute for Fiscal Studies The

REVIEW OF NEW UNIVERSITY-WIDE TEACHING EVALUATION FORM

Excerpt from the PACTL Report to Senate of May 15, 1998 (S.98-123) Page 1 Provost's Advisory Committee on Teaching and Learning (PACTL) Report to Senate REVIEW OF NEW UNIVERSITY-WIDE TEACHING EVALUATION

No Way Out: How Prime-Age Workers Get Trapped in Minimum-Wage Jobs

No Way Out: How Prime-Age Workers Get Trapped in Minimum-Wage Jobs Heather Boushey Most minimum-wage workers are adults making significant contributions to the total family income. In the early 2000s,

Regression analysis in practice with GRETL

Regression analysis in practice with GRETL Prerequisites You will need the GNU econometrics software GRETL installed on your computer (http://gretl.sourceforge.net/), together with the sample files that

TEACHING PRINCIPLES OF ECONOMICS: INTERNET VS. TRADITIONAL CLASSROOM INSTRUCTION

21 TEACHING PRINCIPLES OF ECONOMICS: INTERNET VS. TRADITIONAL CLASSROOM INSTRUCTION Doris S. Bennett, Jacksonville State University Gene L. Padgham, Jacksonville State University Cynthia S. McCarty, Jacksonville

STATISTICS 151 SECTION 1 FINAL EXAM MAY

STATISTICS 151 SECTION 1 FINAL EXAM MAY 2 2009 This is an open book exam. Course text, personal notes and calculator are permitted. You have 3 hours to complete the test. Personal computers and cellphones

Econ 371 Problem Set #4 Answer Sheet. P rice = (0.485)BDR + (23.4)Bath + (0.156)Hsize + (0.002)LSize + (0.090)Age (48.

Econ 371 Problem Set #4 Answer Sheet 6.5 This question focuses on what s called a hedonic regression model; i.e., where the sales price of the home is regressed on the various attributes of the home. The

Elementary Statistics

Elementary Statistics Chapter 1 Dr. Ghamsary Page 1 Elementary Statistics M. Ghamsary, Ph.D. Chap 01 1 Elementary Statistics Chapter 1 Dr. Ghamsary Page 2 Statistics: Statistics is the science of collecting,

Simultaneous Equation Models As discussed last week, one important form of endogeneity is simultaneity. This arises when one or more of the

Simultaneous Equation Models As discussed last week, one important form of endogeneity is simultaneity. This arises when one or more of the explanatory variables is jointly determined with the dependent

Econ 371 Problem Set #3 Answer Sheet

Econ 371 Problem Set #3 Answer Sheet 4.1 In this question, you are told that a OLS regression analysis of third grade test scores as a function of class size yields the following estimated model. T estscore

Alissa Goodman and Ellen Greaves 1. Institute for Fiscal Studies

Alissa Goodman and Ellen Greaves 1 Institute for Fiscal Studies Does being married rather than cohabiting lead to more stability in relationships between parents? This assertion is made in the government

Chapter 8: Interval Estimates and Hypothesis Testing

Chapter 8: Interval Estimates and Hypothesis Testing Chapter 8 Outline Clint s Assignment: Taking Stock Estimate Reliability: Interval Estimate Question o Normal Distribution versus the Student t-distribution:

A Comparison of Student Learning Outcomes in Traditional and Online Personal Finance Courses

A Comparison of Student Learning Outcomes in Traditional and Online Personal Finance Courses Eddie J. Ary Associate Professor Frank D. Hickingbotham School of Business Ouachita Baptist University Arkadelphia,

Does Education Change Political Attitudes? Evidence from a Kenyan School Experiment

Does Education Change Political Attitudes? Evidence from a Kenyan School Experiment Edward Miguel, UC Berkeley and NBER Michael Kremer, Harvard and NBER Rebecca Thornton, University of Michigan Education,

University of Wisconsin Oshkosh

University of Wisconsin Oshkosh Oshkosh Scholar Submission Volume IV Fall 2009 The Correlation Between Happiness and Inequality Kiley Williams, author Dr. Marianne Johnson, Economics, faculty adviser Kiley

Chapter 7: Simple linear regression Learning Objectives

Chapter 7: Simple linear regression Learning Objectives Reading: Section 7.1 of OpenIntro Statistics Video: Correlation vs. causation, YouTube (2:19) Video: Intro to Linear Regression, YouTube (5:18) -

Teaching college microeconomics: Online vs. traditional classroom instruction

Teaching college microeconomics: Online vs. traditional classroom instruction ABSTRACT Cynthia McCarty Jacksonville State University Doris Bennett Jacksonville State University Shawn Carter Jacksonville

Labour Market Discrimination

Chapter 10 Labour Market Discrimination McGraw-Hill/Irwin Labor Economics, 4 th edition Copyright 2008 The McGraw-Hill Companies, Inc. All rights reserved. 10-2 Introduction Labour Market Discrimination

E205 Final: Version B

Name: Class: Date: E205 Final: Version B Multiple Choice Identify the choice that best completes the statement or answers the question. 1. The owner of a local nightclub has recently surveyed a random

Examining Professional and Academic Culture in Chilean Journalism and Mass Communication Education

Examining Professional and Academic Culture in Chilean Journalism and Mass Communication Education Claudia Mellado, University of Concepcion, Chile 60th Annual Conference of the International Communication

Can Annuity Purchase Intentions Be Influenced?

Can Annuity Purchase Intentions Be Influenced? Jodi DiCenzo, CFA, CPA Behavioral Research Associates, LLC Suzanne Shu, Ph.D. UCLA Anderson School of Management Liat Hadar, Ph.D. The Arison School of Business,

Hypothesis Tests for a Population Proportion

Hypothesis Tests for a Population Proportion MATH 130, Elements of Statistics I J. Robert Buchanan Department of Mathematics Fall 2015 Review: Steps of Hypothesis Testing 1. A statement is made regarding

DOES GENDER, CLASS STANDING, AND HIGH SCHOOL ECONOMICS INFLUENCE STUDENTS' ECONOMIC LEARNING. Submitted by:

DOES GENDER, CLASS STANDING, AND HIGH SCHOOL ECONOMICS INFLUENCE STUDENTS' ECONOMIC LEARNING Submitted by: Deborah E. Bridges Department of Economics, University of Nebraska at Kearney, Kearney NE 68849-4570

Lecture 3: Differences-in-Differences

Lecture 3: Differences-in-Differences Fabian Waldinger Waldinger () 1 / 55 Topics Covered in Lecture 1 Review of fixed effects regression models. 2 Differences-in-Differences Basics: Card & Krueger (1994).

Regression Analysis Using ArcMap. By Jennie Murack

Regression Analysis Using ArcMap By Jennie Murack Regression Basics How is Regression Different from other Spatial Statistical Analyses? With other tools you ask WHERE something is happening? Are there

SOME NOTES ON STATISTICAL INTERPRETATION. Below I provide some basic notes on statistical interpretation for some selected procedures.

1 SOME NOTES ON STATISTICAL INTERPRETATION Below I provide some basic notes on statistical interpretation for some selected procedures. The information provided here is not exhaustive. There is more to

Economics 375 Econometrics Fall 2012 Midterm Exam

Economics 375 Name: Econometrics Fall 2012 Midterm Exam 1. When funeral directors are asked to arrange to have bodies shipped home from elsewhere, they often consult directories of funeral homes to find

Chapter Three OVERVIEW OF CURRENT SCHOOL ADMINISTRATORS The first step in understanding the careers of school administrators is to describe the numbers and characteristics of those currently filling these

Topic 1 - Introduction to Labour Economics. Professor H.J. Schuetze Economics 370. What is Labour Economics?

Topic 1 - Introduction to Labour Economics Professor H.J. Schuetze Economics 370 What is Labour Economics? Let s begin by looking at what economics is in general Study of interactions between decision

An Application of the UTAUT Model for Understanding Student Perceptions Using Course Management Software

An Application of the UTAUT Model for Understanding Student Perceptions Using Course Management Software Jack T. Marchewka Chang Liu Operations Management and Information Systems Department Northern Illinois

DOES MORE SCHOOLING CAUSE BETTER HEALTH?

DOES MORE SCHOOLING CAUSE BETTER HEALTH? MEMORIAL LECTURE IN HONOR OF TADASHI YAMADA 2011 MEETING OF JAPAN HEALTH ECONOMICS ASSOCIATION MICHAEL GROSSMAN CITY UNIVERSITY OF NEW YORK GRADUATE CENTER AND

23/04/13. Integrating Global Talent in Norway: Statistical Report. Study sponsored by Abelia, Akademikerne, The Research Council, NHO, Tekna and SIU

23/04/13 Integrating Global Talent in Norway: Statistical Report Study sponsored by Abelia, Akademikerne, The Research Council, NHO, Tekna and SIU For information on obtaining additional copies, permission

Nonlinear Regression Functions. SW Ch 8 1/54/

Nonlinear Regression Functions SW Ch 8 1/54/ The TestScore STR relation looks linear (maybe) SW Ch 8 2/54/ But the TestScore Income relation looks nonlinear... SW Ch 8 3/54/ Nonlinear Regression General

APEX program evaluation study

APEX program evaluation study Are online courses less rigorous than in the regular classroom? Chung Pham Senior Research Fellow Aaron Diel Research Analyst Department of Accountability Research and Evaluation,

Public Housing and Public Schools: How Do Students Living in NYC Public Housing Fare in School?

Furman Center for real estate & urban policy New York University school of law wagner school of public service november 2008 Policy Brief Public Housing and Public Schools: How Do Students Living in NYC

Determining Future Success of College Students

Undergraduate Economic Review Volume 5 Issue 1 Article 7 2009 Determining Future Success of College Students Paul Oehrlein Illinois Wesleyan University Recommended Citation Oehrlein, Paul (2009) "Determining

A Comparison of Course Delivery Methods: An Exercise in Experimental Economics

JOURNAL OF ECONOMICS AND FINANCE EDUCATION Volume 7 Number 1 Summer 2008 21 A Comparison of Course Delivery Methods: An Exercise in Experimental Economics Roy Howsen and Stephen Lile 1 ABSTRACT This paper

Social Security Eligibility and the Labor Supply of Elderly Immigrants. George J. Borjas Harvard University and National Bureau of Economic Research

Social Security Eligibility and the Labor Supply of Elderly Immigrants George J. Borjas Harvard University and National Bureau of Economic Research Updated for the 9th Annual Joint Conference of the Retirement

CONTENTS OF DAY 2. II. Why Random Sampling is Important 9 A myth, an urban legend, and the real reason NOTES FOR SUMMER STATISTICS INSTITUTE COURSE

1 2 CONTENTS OF DAY 2 I. More Precise Definition of Simple Random Sample 3 Connection with independent random variables 3 Problems with small populations 8 II. Why Random Sampling is Important 9 A myth,

Academic Performance of Native and Transfer Students

Academic Performance of Native and Transfer Students Report 2010-02 John M. Krieg April, 2010 Office of Survey Research Western Washington University Table of Contents I. Acknowledgements 3 II. Executive

OBSERVATION. TD Economics DON T JUDGE A LABOR MARKET BY ITS UNEMPLOYMENT RATE

OBSERVATION TD Economics DON T JUDGE A LABOR MARKET BY ITS Highlights Over the last year, the Canadian unemployment rate has remained relatively static, while the U.S. rate has continued to fall. This

American Journal Of Business Education July/August 2012 Volume 5, Number 4

The Impact Of The Principles Of Accounting Experience On Student Preparation For Intermediate Accounting Linda G. Carrington, Ph.D., Sam Houston State University, USA ABSTRACT Both students and instructors

My factorial research design yields panel data in which i vignettes (i = 1,, 10) are nested

1 SUPPLEENTAL ATERIAL Analytic Strategy: Studies 1ab y factorial research design yields panel data in which i vignettes (i = 1,, 10) are nested within individuals ( = 1,, J). As a result, I estimated two-level

Wiswall, Labor Economics (Undergraduate), Exams 1. Final Exam, Labor Economics, Fall 2006, Wiswall

Wiswall, Labor Economics (Undergraduate), Exams 1 Final Exam, Labor Economics, Fall 2006, Wiswall Instructions: Write all answers on the separate answer sheet. Make sure you write your name on every page

Instrumental Variables & 2SLS

Instrumental Variables & 2SLS y 1 = β 0 + β 1 y 2 + β 2 z 1 +... β k z k + u y 2 = π 0 + π 1 z k+1 + π 2 z 1 +... π k z k + v Economics 20 - Prof. Schuetze 1 Why Use Instrumental Variables? Instrumental

Chapter Seven. Multiple regression An introduction to multiple regression Performing a multiple regression on SPSS

Chapter Seven Multiple regression An introduction to multiple regression Performing a multiple regression on SPSS Section : An introduction to multiple regression WHAT IS MULTIPLE REGRESSION? Multiple

Mind on Statistics. Chapter 15

Mind on Statistics Chapter 15 Section 15.1 1. A student survey was done to study the relationship between class standing (freshman, sophomore, junior, or senior) and major subject (English, Biology, French,

GRADUATE STUDENT SATISFACTION WITH AN ONLINE DISCRETE MATHEMATICS COURSE *

GRADUATE STUDENT SATISFACTION WITH AN ONLINE DISCRETE MATHEMATICS COURSE * Amber Settle DePaul University 243 S. Wabash Avenue Chicago, IL 60604 (312) 362-5324 asettle@cti.depaul.edu Chad Settle University

High School Graduation Rates in Maryland Technical Appendix

High School Graduation Rates in Maryland Technical Appendix Data All data for the brief were obtained from the National Center for Education Statistics Common Core of Data (CCD). This data represents the

Overview of Violations of the Basic Assumptions in the Classical Normal Linear Regression Model

Overview of Violations of the Basic Assumptions in the Classical Normal Linear Regression Model 1 September 004 A. Introduction and assumptions The classical normal linear regression model can be written

Gender Effects in the Alaska Juvenile Justice System

Gender Effects in the Alaska Juvenile Justice System Report to the Justice and Statistics Research Association by André Rosay Justice Center University of Alaska Anchorage JC 0306.05 October 2003 Gender

Journal of College Teaching & Learning July 2008 Volume 5, Number 7

Student Grade Motivation As A Determinant Of Performance On The Business Major Field ETS Exam Neil Terry, West Texas A&M University LaVelle Mills, West Texas A&M University Marc Sollosy, West Texas A&M

What s New in Econometrics? Lecture 8 Cluster and Stratified Sampling

What s New in Econometrics? Lecture 8 Cluster and Stratified Sampling Jeff Wooldridge NBER Summer Institute, 2007 1. The Linear Model with Cluster Effects 2. Estimation with a Small Number of Groups and

1) The confidence interval for a single coefficient in a multiple regression

Practice Questions Multiple Choice Questions Chapter 5 1) The confidence interval for a single coefficient in a multiple regression Answer: c a. makes little sense because the population parameter is unknown.

Title:Nudging children towards whole wheat bread: a field experiment on the influence of appealing bread roll shape on consumption during breakfast

Reviewer's report Title:Nudging children towards whole wheat bread: a field experiment on the influence of appealing bread roll shape on consumption during breakfast Version:1Date:16 April 2014 Reviewer:Dana

Econometric Analysis of Cross Section and Panel Data Second Edition. Jeffrey M. Wooldridge. The MIT Press Cambridge, Massachusetts London, England

Econometric Analysis of Cross Section and Panel Data Second Edition Jeffrey M. Wooldridge The MIT Press Cambridge, Massachusetts London, England Preface Acknowledgments xxi xxix I INTRODUCTION AND BACKGROUND

CHAPTER 13: EMPIRICAL EVIDENCE ON SECURITY RETURNS

CHAPTER 13: EMPIRICAL EVIDENCE ON SECURITY RETURNS Note: For end-of-chapter-problems in Chapter 13, the focus is on the estimation procedure. To keep the exercise feasible the sample was limited to returns

Persistence of Women and Minorities in STEM Field Majors: Is it the School That Matters?

Cornell University ILR School DigitalCommons@ILR Working Papers ILR Collection 3-2010 Persistence of Women and Minorities in STEM Field Majors: Is it the School That Matters? Amanda L. Griffith Wake Forest

Hypothesis Testing in the Linear Regression Model An Overview of t tests, D Prescott

Hypothesis Testing in the Linear Regression Model An Overview of t tests, D Prescott 1. Hypotheses as restrictions An hypothesis typically places restrictions on population regression coefficients. Consider

The Impact of Public Infrastructure on Private Investment in the US

The Impact of Public Infrastructure on Private Investment in the US Ben Miller Advisor: Mahmud Yesuf University Honors in Mathematics and Economics 1 Abstract Economists and politicians are concerned with

How different are service and manufacturing firms in the informal sector? (Short Note)

World Bank From the SelectedWorks of Mohammad Amin October, 2009 How different are service and manufacturing firms in the informal sector? (Short Note) Mohammad Amin Available at: http://works.bepress.com/mohammad_amin/14/

Statistics 151 Practice Midterm 1 Mike Kowalski

Statistics 151 Practice Midterm 1 Mike Kowalski Statistics 151 Practice Midterm 1 Multiple Choice (50 minutes) Instructions: 1. This is a closed book exam. 2. You may use the STAT 151 formula sheets and

A Comparative Statistical Profile of Kansas City Teaching Fellows

12/5/04 A Comparative Statistical Profile of Kansas City Teaching Fellows Michael Podgursky, Ryan Monroe, and Mark Ehlert Department of Economics University of Missouri - Columbia KCTF Compared to Other

BY Aaron Smith NUMBERS, FACTS AND TRENDS SHAPING THE WORLD FOR RELEASE MARCH 10, 2016 FOR MEDIA OR OTHER INQUIRIES:

NUMBERS, FACTS AND TRENDS SHAPING THE WORLD FOR RELEASE MARCH 10, 2016 BY Aaron Smith FOR MEDIA OR OTHER INQUIRIES: Aaron Smith, Associate Director, Research Dana Page, Senior Communications Manager 202.419.4372

The Value of a Private Education: Differential Returns and Selection on Observables

The Value of a Private Education: Differential Returns and Selection on Observables By Anil Nathan, Sovita Hean, and Ryan Elliot July 2015 COLLEGE OF THE HOLY CROSS, DEPARTMENT OF ECONOMICS AND ACCOUNTING

The Life-Cycle Motive and Money Demand: Further Evidence. Abstract

The Life-Cycle Motive and Money Demand: Further Evidence Jan Tin Commerce Department Abstract This study takes a closer look at the relationship between money demand and the life-cycle motive using panel

WORKING P A P E R. Choices and Information Needs for Workers Leaving the North Carolina State Retirement Plan

WORKING P A P E R Choices and Information Needs for Workers Leaving the North Carolina State Retirement Plan Accepting a Lump Sum Payment or Receiving an Annuity at Retirement ROBERT CLARK AND MELINDA

Donna Hawley Wolfe Professor Emeritus Wichita State University

Donna Hawley Wolfe Professor Emeritus Wichita State University RFP from KBOR and KSDE requested studies using the their respective longitudinal databases These databases include individual level information

CULTURAL STUDIES GRADUATE GROUP DEGREE REQUIREMENTS Revisions: June 2006, February 2009 Approved by Graduate Council: May 20, 2009

CULTURAL STUDIES GRADUATE GROUP DEGREE REQUIREMENTS Revisions: June 2006, February 2009 Approved by Graduate Council: May 20, 2009 M.A. PROGRAM 1) Admissions Requirements There are no admissions to the