Final Exam. Question 1 (20 points) 2 (25 points) 3 (30 points) 4 (25 points) 5 (10 points) 6 (40 points) Total (150 points) Bonus question (10)
|
|
- Hilda Ellis
- 6 years ago
- Views:
Transcription
1 Name Economics 170 Spring 2004 Honor pledge: I have neither given nor received aid on this exam including the preparation of my one page formula list and the preparation of the Stata assignment for the exam Final Exam John F. Stewart Instructions: Complete each part of this exam in the space provided. If you need additional space, use the backs of pages but clear indicate where your answers are. Neatness and clarity of exposition count. You must attach your formula list and the output from the Stata assignment to this exam. Question 1 (20 points) 2 (25 points) 3 (30 points) 4 (25 points) 5 (10 points) 6 (40 points) Total (150 points) Bonus question (10) 1. Definitions: Briefly Define each of the following terms as they relate to the material covered in class Over identification 1.2. Durbin-Watson statistic 1.3. Serial correlation 1.4. adjusted R exact multicollinearity Econ 170 Final Page 1 of 10
2 2. Short Answer, multiple choice, (5 points each) Use the following list of Ramanathan s assumptions for the linear regression model. 1. The regression model is linear in unknown coefficients a and b i. Y t = a + b 1 X 1t b k X kt + m t for t=1,2,...,n 2. Not all observations on X are the same, i.e. Var(X) > 0 3. The error term m t is a random variable with E( m t X t ) = 0 4. X t is given and nonrandom, implying that it is uncorrelated with ms that is Cov(X t, m s ) = 0 5. Given X t, m t has constant varaince. Var(m t X)= s 2 6. Given X t, m t and m s are independently distributed for all t s so Cov(m t, m s X) = 0 7. The number of observation (n) must be greater than the number of regression coefficients estimate 8. For a given X, m t is normally distributed. m t X ~ N(0, s 2 ) 2.1. A failure of assumption 5 is called a) multicollinearity b) serial correlation c) heteroscedasticity d) both b) and c) 2.2. A failure assumption 6 is called a) multicollinearity b) serial correlation c) heteroscedasticity d) both b) and c) 2.3. If error terms are heteroscedastic, OLS (ordinary least squares) estimates of the parameter of the regression equation will be (check all that apply) a) biased b) unbiased c) efficient d) not efficient e) consistent 2.4. If one independent variable is omitted from the regression and that variable is correlated with one or more independent variables that are included in the regression then assumption number will be violated and the estimated regression will a) still be unbiased but will be inefficient or b) will be biased Assuming that all 8 assumptions hold, the nr 2 (where R 2 =1-(ESS/TSS) from the OLS regression) is distributed a) F under H 0 : B$ $ 1 = β2 =... = β k = 0 a) P 2 under H 0 : B$ β$... 1 = 2 = = β k = 0 b) F under H 0 : B$ 1 = β1,......, β $ k = β k b) P 2 under H 0 : B$ 1 = β1,......, β $ k = βk Econ 170 Final Page 2 of 10
3 Problems: (points as indicated) 3. (30 points) Consider a simple regression model where Y = α +βx + µ where Y is college GPA X is high school GPA µ is a random error You are given the following information about the data Term Value n, number of observations 427 Y Sample mean of Y X Sample mean of Y ( Yi Y ) 1= 1... n 2 = TSS (total sum of squares) ( X i X ) 1= 1... n ( Yi Y )( X i X ) i = 1... n ( $ ) µ i i=1... n 2 OLS regression) 2 = ESS (the error sum of squares from the Show your work Calculate the sample standard deviation of X Calculate the value of the OLS estimate of β, β $ = 3.3. Calculate the value of the OLS estimate of a, α $ = 3.4. Calculate the R 2 for the OLS regression R 2 = Econ 170 Final Page 3 of 10
4 3.5. Calculate the estimated error variance of the OLS model s 2 = 3.6. My daughter had a high school GPA=4.0. Given the additional information that the standard error of prediction for her observation is.04 and that the 95% critical value from the relevant normal distribution is 1.65, calculate a 95% confidence interval on her predicted college GPA. 4. Consider the following time series model: Y t = α +βx t + µ t. We suspect that the model is characterize by a second order auto regressive process. µ t = ρ 1 µ t-1 + ρ 2 µ t-2 + ε t where ε t is white noise i.e it is iid (10 points) Describe step by step how you could test the hypothesis H0: µ t = ε τ against the alternative H1: µ t = ρ 1 µ t-1 + ρ 2 µ t-2 + ε t 4.2. (5 points) Suppose that after doing the test proposed in 4.1., that you cannot reject the null hypothesis of autocorrelation, what are the consequences of estimating the original model with OLS. (What properties will OLS estimates have in this situation?) 4.3. (10 points) Again assuming that we cannot reject the null hypothesis of autocorrelation,, describe step by step the procedure that would result in consistent and asysmtotically efficient estimators of the parameters of this model. Econ 170 Final Page 4 of 10
5 5. (10 points) Consider a sample of 60 individuals who applied for medical school. The variable Y measures whether or not the individual was accepted to medical school (accept = 1 if the student was accepted and accept = 0 if the student was not accepted). The variable mcat measure the individual s score on the med school entrance exam, and the variable gpa measures the students college grade point average (4 points) Consider estimating the model accept = a + b 1 mcat = b 2 gpa + m using OLS methods. a) Which assumption(s) required for OLS estimators to be unbiased and efficient is (are) necessarily violated (use the list of assumptions from Question 2)? b) Standard hypothesis test of the OLS coefficient estimates (t tests and F tests) are or are not valid. Explain your answer (6 points) The table below show the model estimated using probit. Probit estimates Number of obs = 60 LR chi2(2) = Prob > chi2 = Log likelihood = Pseudo R2 = accept Coef. Std. Err. z P> z [95% Conf. Interval] gpa mcat _cons Sam has a 4.0 college gpa and a mcat score of 66, what is the based on the probit model, what is the predicted probability that Sam will get accepted into medical school. Explain your answer. Cumulative standard normal probability f(x) x Econ 170 Final Page 5 of 10
6 6. (40 points) This question uses Part A of the Final Exam Homework Stata assignment. Note: I am trying to cover a lot of ground with one data set. So please treat the questions as sequential and only use the information that is specifically requested for each part. 6.1.a. (5 points) First consider your OLS estimation results for Model 1 and Model 2 from the Final Exam Homework assignment. The economic model is the cross state variation in average performance on the SAT score, depends on how much the state spends per pupil on education and possibly other factors. Compare the results you obtained from these two model (particularly concentrating on the differences). What explanation would you offer as to why the two models differ? 6.1.b. (5 points) Though you were not asked to do it on the assignment, if, after running Model 2, you had run White s general test you would have gotten the following output.. whitetst White's general test statistic : Chi-sq( 5) P-value =.0551 What have we tested for with this test? How was the test actually done? And, how do you interpret the above results? How do these test results change your interpretation of the OLS estimators you obtained for Model 2, if at all Now consider Model 2, Model 3, and Model 4. For this section we have added have added the states poverty rate as another determinant of SAT scores. In Model 3 it is added separate entering linearly variable; in Model 4 poverty rate enters both linearly and interacted with spending. Model 2: sat_tot = α + β 1 spend01 + β 2 pr_02 + µ Model 3: sat_tot = α + β 1 spend01 + β 2 pr_02 + β 3 pov_rate + µ Model 4: sat_tot = α + β 1 spend01 + β 2 pr_02 + β 3 pov_rate + β4(pov_rate spend01) + µ 6.2.a. (5 points) Using your estimated results for Model 4, State A has a poverty rate of 5% and State B has a poverty rate of 15%. An additional $1 of spending per pupil in will result in how many additional points on the SAT scores in State A?, in State B? (show you work, and note in the data a 10% poverty rate Econ 170 Final Page 6 of 10
7 appears as 10", not.1) 6.2.b. (5 points) Using the Wald Test from your STATA assignment, can you reject the specification in Model 2 (as H 0 when the alternative hypothesis (H 1 ) is Model 4? Explain. 6.2.c. (5 points) Suppose our interest was in rejecting Model 2 (as H 0 ) when the alternative hypothesis (H 1 ) is Model 3, Do you have enough information on your print outs to do this test? Explain. 6.2.d. (5 points) Using the information you generate in part 5 of the STATA assignment, where does North Carolina rank compared to the other states in average SAT scores? Rank, Where do you predict North Carolina would rank in average SAT scores if all states had participation in the exams at the same level? Rank Now consider a model of SAT scores in which we also consider some additional factors and we consider the determinants of the participation rate pr_02. Model 5: 1) sat_tot = α + β 1 spend01 + β 2 pr_02 + β 3 pov_rate + β 4 col_grad + µ 2) pr_02 = γ + γ 1 spend01 + γ 2 sat_tot + γ 3 fam_y + β 4 col_grad + υ 6.3.a. (5 points) In theory, using OLS to estimate equation 1) of model 5 will result in parameter estimates that are (check those that apply) biased efficient unbiased inefficient asymptotically efficient not asymptotically efficient consistent not consistent 6.3.b. (5 points) Using your STATA output, compare the estimates you obtained to OLS and 2SLS procedures. How do they differ? Econ 170 Final Page 7 of 10
8 Econ 170 Final Page 8 of 10
9 6.4. Bonus Question (10 points) Along with your answer to 5.3., consider the additional output that was obtained from an OLS regression 1') sat_tot = α + β 1 spend01 + β 2 pr_02 + g 0 er_pr + β 3 pov_rate + β 4 col_grad + µ where er_pr are the predicted residuals for an OLS estimation of the reduced form equation for pr_02.. reg sat_tot pr_02 er_pr spend01 pov_rate col_grad Source SS df MS Number of obs = F( 5, 44) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = sat_tot Coef. Std. Err. t P> t [95% Conf. Interval] pr_ er_pr spend pov_rate col_grad _cons What can you add to your answer to 6.3 with this additional information? Econ 170 Final Page 9 of 10
10 Economics 170 Spring 2004 Final Exam Assignment John F Stewart This sheet describes STATA exercises to be completed prior to the final exam. You will bring this sheet and a printed copy of the STATA output you generated in doing these this exercise to the final exam. Some questions on the exam will require information you will generate in this assignment. Your output will be turned in with your final exam. This work is an Honor Code assignment. You are not allowed to get help from any individual in completing this assignment. You may use your notes (including do files posted from lectures), the book, STATA Help and manuals, and your previous homework assignments (including keys from the web site). Data Set: final_hw_s04.dta. This is a cross sectional data set of the SAT scores and other information for 50 states in The variable definitions are as follows. state state abbreviation pov_rate percent of state population living in poverty pr_02 SAT participation rate (% of the students who took the SAT exam in 2002) sat_tot average total SAT score in state in 2002 spend01 per pupil spending on public education in the state in 2001 pr_spend (pov_rate) x (spend01) interaction of spending and poverty rate fam_y median family income in state col_grad percent of population over age 25 with college degree pr_02bak an extra copy of the values in pr_02 (see instructions) 1. Use sum to get a table of summary statistics for all the variables in the data set. 2. Generate the OLS (reg) regression estimates for the following models Model Dependent variable Independent variables 1 sat_tot spend01 2 sat_tot spend01 pr_02 3 sat_tot spend01 pr_02 pov_rate 4 sat_tot spend01 pr_02 pov_rate pr_spend 3. For Model 4: do a Wald test for the hypothesis that both the coefficient on pov_rate equals and the coeficient on pr_spend equal zero. 4. Using Model 4, create predicted SAT scores for all 50 states under the assumption that participation rate (pr_02) in each state is equal to the average participation rate for the entire sample of fifty states (pr_02 for each state is equal to the mean for all states.) Call the predicted SAT scores sat_hat. After generating your predictions sort sat_ tot and list state stat_tot and then sort sat_hat and list sat_tot state sat_hat IMPORTANT: When you have finished with question 4, make sure you restore the original values before you continue to the next part. You can do this easily by replace pr_02=pr_02bak 5. Consider the following model two equation model were participation rates are considered to be endogenous. Model 5: 1) sat_tot = α + β 1 pr_02 + β 2 spend01 + β 3 col_grad + β 4 pov_rate + µ 2) pr_02 = γ + γ 1 sat_tot + γ 2 spend01 + γ 3 col_grad + β 4 fam_y + υ Generate parameter estimates for equation 1) of the system using OLS (reg). Generate parameter estimates for equation 1) of the system using two stage least squares. (use the ivreg procedure in STATA.) Econ 170 Final Page 10 of 10
Final Exam. 1. Definitions: Briefly Define each of the following terms as they relate to the material covered in class.
Name Answer Key Economics 170 Spring 2003 Honor pledge: I have neither given nor received aid on this exam including the preparation of my one page formula list and the preparation of the Stata assignment
More informationECON Introductory Econometrics. Lecture 7: OLS with Multiple Regressors Hypotheses tests
ECON4150 - Introductory Econometrics Lecture 7: OLS with Multiple Regressors Hypotheses tests Monique de Haan (moniqued@econ.uio.no) Stock and Watson Chapter 7 Lecture outline 2 Hypothesis test for single
More informationHandout 12. Endogeneity & Simultaneous Equation Models
Handout 12. Endogeneity & Simultaneous Equation Models In which you learn about another potential source of endogeneity caused by the simultaneous determination of economic variables, and learn how to
More informationLecture 4: Multivariate Regression, Part 2
Lecture 4: Multivariate Regression, Part 2 Gauss-Markov Assumptions 1) Linear in Parameters: Y X X X i 0 1 1 2 2 k k 2) Random Sampling: we have a random sample from the population that follows the above
More informationLab 07 Introduction to Econometrics
Lab 07 Introduction to Econometrics Learning outcomes for this lab: Introduce the different typologies of data and the econometric models that can be used Understand the rationale behind econometrics Understand
More informationECO220Y Simple Regression: Testing the Slope
ECO220Y Simple Regression: Testing the Slope Readings: Chapter 18 (Sections 18.3-18.5) Winter 2012 Lecture 19 (Winter 2012) Simple Regression Lecture 19 1 / 32 Simple Regression Model y i = β 0 + β 1 x
More informationApplied Statistics and Econometrics
Applied Statistics and Econometrics Lecture 6 Saul Lach September 2017 Saul Lach () Applied Statistics and Econometrics September 2017 1 / 53 Outline of Lecture 6 1 Omitted variable bias (SW 6.1) 2 Multiple
More informationQuantitative Methods Final Exam (2017/1)
Quantitative Methods Final Exam (2017/1) 1. Please write down your name and student ID number. 2. Calculator is allowed during the exam, but DO NOT use a smartphone. 3. List your answers (together with
More informationAnswer all questions from part I. Answer two question from part II.a, and one question from part II.b.
B203: Quantitative Methods Answer all questions from part I. Answer two question from part II.a, and one question from part II.b. Part I: Compulsory Questions. Answer all questions. Each question carries
More information4 Instrumental Variables Single endogenous variable One continuous instrument. 2
Econ 495 - Econometric Review 1 Contents 4 Instrumental Variables 2 4.1 Single endogenous variable One continuous instrument. 2 4.2 Single endogenous variable more than one continuous instrument..........................
More informationECON 497 Final Exam Page 1 of 12
ECON 497 Final Exam Page of 2 ECON 497: Economic Research and Forecasting Name: Spring 2008 Bellas Final Exam Return this exam to me by 4:00 on Wednesday, April 23. It may be e-mailed to me. It may be
More informationEssential of Simple regression
Essential of Simple regression We use simple regression when we are interested in the relationship between two variables (e.g., x is class size, and y is student s GPA). For simplicity we assume the relationship
More informationECON 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.
More informationEconometrics. 8) Instrumental variables
30C00200 Econometrics 8) Instrumental variables Timo Kuosmanen Professor, Ph.D. http://nomepre.net/index.php/timokuosmanen Today s topics Thery of IV regression Overidentification Two-stage least squates
More informationECON Introductory Econometrics. Lecture 5: OLS with One Regressor: Hypothesis Tests
ECON4150 - Introductory Econometrics Lecture 5: OLS with One Regressor: Hypothesis Tests Monique de Haan (moniqued@econ.uio.no) Stock and Watson Chapter 5 Lecture outline 2 Testing Hypotheses about one
More informationECON3150/4150 Spring 2016
ECON3150/4150 Spring 2016 Lecture 4 - The linear regression model Siv-Elisabeth Skjelbred University of Oslo Last updated: January 26, 2016 1 / 49 Overview These lecture slides covers: The linear regression
More informationECON3150/4150 Spring 2015
ECON3150/4150 Spring 2015 Lecture 3&4 - The linear regression model Siv-Elisabeth Skjelbred University of Oslo January 29, 2015 1 / 67 Chapter 4 in S&W Section 17.1 in S&W (extended OLS assumptions) 2
More information4 Instrumental Variables Single endogenous variable One continuous instrument. 2
Econ 495 - Econometric Review 1 Contents 4 Instrumental Variables 2 4.1 Single endogenous variable One continuous instrument. 2 4.2 Single endogenous variable more than one continuous instrument..........................
More informationBinary Dependent Variables
Binary Dependent Variables In some cases the outcome of interest rather than one of the right hand side variables - is discrete rather than continuous Binary Dependent Variables In some cases the outcome
More informationLecture 8: Instrumental Variables Estimation
Lecture Notes on Advanced Econometrics Lecture 8: Instrumental Variables Estimation Endogenous Variables Consider a population model: y α y + β + β x + β x +... + β x + u i i i i k ik i Takashi Yamano
More informationLecture 4: Multivariate Regression, Part 2
Lecture 4: Multivariate Regression, Part 2 Gauss-Markov Assumptions 1) Linear in Parameters: Y X X X i 0 1 1 2 2 k k 2) Random Sampling: we have a random sample from the population that follows the above
More informationProblem Set #3-Key. wage Coef. Std. Err. t P> t [95% Conf. Interval]
Problem Set #3-Key Sonoma State University Economics 317- Introduction to Econometrics Dr. Cuellar 1. Use the data set Wage1.dta to answer the following questions. a. For the regression model Wage i =
More informationCHAPTER 6: SPECIFICATION VARIABLES
Recall, we had the following six assumptions required for the Gauss-Markov Theorem: 1. The regression model is linear, correctly specified, and has an additive error term. 2. The error term has a zero
More informationPractice exam questions
Practice exam questions Nathaniel Higgins nhiggins@jhu.edu, nhiggins@ers.usda.gov 1. The following question is based on the model y = β 0 + β 1 x 1 + β 2 x 2 + β 3 x 3 + u. Discuss the following two hypotheses.
More informationMeasurement Error. Often a data set will contain imperfect measures of the data we would ideally like.
Measurement Error Often a data set will contain imperfect measures of the data we would ideally like. Aggregate Data: (GDP, Consumption, Investment are only best guesses of theoretical counterparts and
More informationECON Introductory Econometrics. Lecture 16: Instrumental variables
ECON4150 - Introductory Econometrics Lecture 16: Instrumental variables Monique de Haan (moniqued@econ.uio.no) Stock and Watson Chapter 12 Lecture outline 2 OLS assumptions and when they are violated Instrumental
More informationECON 497 Midterm Spring
ECON 497 Midterm Spring 2009 1 ECON 497: Economic Research and Forecasting Name: Spring 2009 Bellas Midterm You have three hours and twenty minutes to complete this exam. Answer all questions and explain
More informationLecture 5. In the last lecture, we covered. This lecture introduces you to
Lecture 5 In the last lecture, we covered. homework 2. The linear regression model (4.) 3. Estimating the coefficients (4.2) This lecture introduces you to. Measures of Fit (4.3) 2. The Least Square Assumptions
More informationAt this point, if you ve done everything correctly, you should have data that looks something like:
This homework is due on July 19 th. Economics 375: Introduction to Econometrics Homework #4 1. One tool to aid in understanding econometrics is the Monte Carlo experiment. A Monte Carlo experiment allows
More informationHandout 11: Measurement Error
Handout 11: Measurement Error In which you learn to recognise the consequences for OLS estimation whenever some of the variables you use are not measured as accurately as you might expect. A (potential)
More informationAnswers: Problem Set 9. Dynamic Models
Answers: Problem Set 9. Dynamic Models 1. Given annual data for the period 1970-1999, you undertake an OLS regression of log Y on a time trend, defined as taking the value 1 in 1970, 2 in 1972 etc. The
More informationQuestion 1a 1b 1c 1d 1e 2a 2b 2c 2d 2e 2f 3a 3b 3c 3d 3e 3f M ult: choice Points
Economics 102: Analysis of Economic Data Cameron Spring 2016 May 12 Department of Economics, U.C.-Davis Second Midterm Exam (Version A) Compulsory. Closed book. Total of 30 points and worth 22.5% of course
More information1: a b c d e 2: a b c d e 3: a b c d e 4: a b c d e 5: a b c d e. 6: a b c d e 7: a b c d e 8: a b c d e 9: a b c d e 10: a b c d e
Economics 102: Analysis of Economic Data Cameron Spring 2016 Department of Economics, U.C.-Davis Final Exam (A) Tuesday June 7 Compulsory. Closed book. Total of 58 points and worth 45% of course grade.
More informationEconometrics Midterm Examination Answers
Econometrics Midterm Examination Answers March 4, 204. Question (35 points) Answer the following short questions. (i) De ne what is an unbiased estimator. Show that X is an unbiased estimator for E(X i
More informationInstrumental Variables, Simultaneous and Systems of Equations
Chapter 6 Instrumental Variables, Simultaneous and Systems of Equations 61 Instrumental variables In the linear regression model y i = x iβ + ε i (61) we have been assuming that bf x i and ε i are uncorrelated
More informationStatistical Inference with Regression Analysis
Introductory Applied Econometrics EEP/IAS 118 Spring 2015 Steven Buck Lecture #13 Statistical Inference with Regression Analysis Next we turn to calculating confidence intervals and hypothesis testing
More informationProblem Set #5-Key Sonoma State University Dr. Cuellar Economics 317- Introduction to Econometrics
Problem Set #5-Key Sonoma State University Dr. Cuellar Economics 317- Introduction to Econometrics C1.1 Use the data set Wage1.dta to answer the following questions. Estimate regression equation wage =
More informationProblem set - Selection and Diff-in-Diff
Problem set - Selection and Diff-in-Diff 1. You want to model the wage equation for women You consider estimating the model: ln wage = α + β 1 educ + β 2 exper + β 3 exper 2 + ɛ (1) Read the data into
More informationProblem Set 10: Panel Data
Problem Set 10: Panel Data 1. Read in the data set, e11panel1.dta from the course website. This contains data on a sample or 1252 men and women who were asked about their hourly wage in two years, 2005
More informationEconometrics Homework 1
Econometrics Homework Due Date: March, 24. by This problem set includes questions for Lecture -4 covered before midterm exam. Question Let z be a random column vector of size 3 : z = @ (a) Write out z
More information1. The shoe size of five randomly selected men in the class is 7, 7.5, 6, 6.5 the shoe size of 4 randomly selected women is 6, 5.
Economics 3 Introduction to Econometrics Winter 2004 Professor Dobkin Name Final Exam (Sample) You must answer all the questions. The exam is closed book and closed notes you may use calculators. You must
More informationEconometrics I KS. Module 2: Multivariate Linear Regression. Alexander Ahammer. This version: April 16, 2018
Econometrics I KS Module 2: Multivariate Linear Regression Alexander Ahammer Department of Economics Johannes Kepler University of Linz This version: April 16, 2018 Alexander Ahammer (JKU) Module 2: Multivariate
More informationAutocorrelation. Think of autocorrelation as signifying a systematic relationship between the residuals measured at different points in time
Autocorrelation Given the model Y t = b 0 + b 1 X t + u t Think of autocorrelation as signifying a systematic relationship between the residuals measured at different points in time This could be caused
More informationQuestion 1 [17 points]: (ch 11)
Question 1 [17 points]: (ch 11) A study analyzed the probability that Major League Baseball (MLB) players "survive" for another season, or, in other words, play one more season. They studied a model of
More informationWarwick Economics Summer School Topics in Microeconometrics Instrumental Variables Estimation
Warwick Economics Summer School Topics in Microeconometrics Instrumental Variables Estimation Michele Aquaro University of Warwick This version: July 21, 2016 1 / 31 Reading material Textbook: Introductory
More informationMultiple Regression Analysis: Estimation. Simple linear regression model: an intercept and one explanatory variable (regressor)
1 Multiple Regression Analysis: Estimation Simple linear regression model: an intercept and one explanatory variable (regressor) Y i = β 0 + β 1 X i + u i, i = 1,2,, n Multiple linear regression model:
More informationExam ECON3150/4150: Introductory Econometrics. 18 May 2016; 09:00h-12.00h.
Exam ECON3150/4150: Introductory Econometrics. 18 May 2016; 09:00h-12.00h. This is an open book examination where all printed and written resources, in addition to a calculator, are allowed. If you are
More informationUNIVERSITY OF TORONTO Faculty of Arts and Science
UNIVERSITY OF TORONTO Faculty of Arts and Science December 2013 Final Examination STA442H1F/2101HF Methods of Applied Statistics Jerry Brunner Duration - 3 hours Aids: Calculator Model(s): Any calculator
More informationMATH 644: Regression Analysis Methods
MATH 644: Regression Analysis Methods FINAL EXAM Fall, 2012 INSTRUCTIONS TO STUDENTS: 1. This test contains SIX questions. It comprises ELEVEN printed pages. 2. Answer ALL questions for a total of 100
More informationEconometrics. 9) Heteroscedasticity and autocorrelation
30C00200 Econometrics 9) Heteroscedasticity and autocorrelation Timo Kuosmanen Professor, Ph.D. http://nomepre.net/index.php/timokuosmanen Today s topics Heteroscedasticity Possible causes Testing for
More informationECONOMICS AND ECONOMIC METHODS PRELIM EXAM Statistics and Econometrics August 2013
ECONOMICS AND ECONOMIC METHODS PRELIM EXAM Statistics and Econometrics August 2013 Instructions: Answer all six (6) questions. Point totals for each question are given in parentheses. The parts within
More informationCase of single exogenous (iv) variable (with single or multiple mediators) iv à med à dv. = β 0. iv i. med i + α 1
Mediation Analysis: OLS vs. SUR vs. ISUR vs. 3SLS vs. SEM Note by Hubert Gatignon July 7, 2013, updated November 15, 2013, April 11, 2014, May 21, 2016 and August 10, 2016 In Chap. 11 of Statistical Analysis
More informationSection I. Define or explain the following terms (3 points each) 1. centered vs. uncentered 2 R - 2. Frisch theorem -
First Exam: Economics 388, Econometrics Spring 006 in R. Butler s class YOUR NAME: Section I (30 points) Questions 1-10 (3 points each) Section II (40 points) Questions 11-15 (10 points each) Section III
More informationIntroduction to Econometrics. Multiple Regression (2016/2017)
Introduction to Econometrics STAT-S-301 Multiple Regression (016/017) Lecturer: Yves Dominicy Teaching Assistant: Elise Petit 1 OLS estimate of the TS/STR relation: OLS estimate of the Test Score/STR relation:
More informationIntroduction to Regression
Introduction to Regression ιατµηµατικό Πρόγραµµα Μεταπτυχιακών Σπουδών Τεχνο-Οικονοµικά Συστήµατα ηµήτρης Φουσκάκης Introduction Basic idea: Use data to identify relationships among variables and use these
More informationLECTURE 11. Introduction to Econometrics. Autocorrelation
LECTURE 11 Introduction to Econometrics Autocorrelation November 29, 2016 1 / 24 ON PREVIOUS LECTURES We discussed the specification of a regression equation Specification consists of choosing: 1. correct
More informationEcon 510 B. Brown Spring 2014 Final Exam Answers
Econ 510 B. Brown Spring 2014 Final Exam Answers Answer five of the following questions. You must answer question 7. The question are weighted equally. You have 2.5 hours. You may use a calculator. Brevity
More informationRecent Advances in the Field of Trade Theory and Policy Analysis Using Micro-Level Data
Recent Advances in the Field of Trade Theory and Policy Analysis Using Micro-Level Data July 2012 Bangkok, Thailand Cosimo Beverelli (World Trade Organization) 1 Content a) Classical regression model b)
More informationQuestion 1 carries a weight of 25%; Question 2 carries 20%; Question 3 carries 20%; Question 4 carries 35%.
UNIVERSITY OF EAST ANGLIA School of Economics Main Series PGT Examination 017-18 ECONOMETRIC METHODS ECO-7000A Time allowed: hours Answer ALL FOUR Questions. Question 1 carries a weight of 5%; Question
More information1 A Review of Correlation and Regression
1 A Review of Correlation and Regression SW, Chapter 12 Suppose we select n = 10 persons from the population of college seniors who plan to take the MCAT exam. Each takes the test, is coached, and then
More informationRegression #8: Loose Ends
Regression #8: Loose Ends Econ 671 Purdue University Justin L. Tobias (Purdue) Regression #8 1 / 30 In this lecture we investigate a variety of topics that you are probably familiar with, but need to touch
More informationECON 4230 Intermediate Econometric Theory Exam
ECON 4230 Intermediate Econometric Theory Exam Multiple Choice (20 pts). Circle the best answer. 1. The Classical assumption of mean zero errors is satisfied if the regression model a) is linear in the
More informationNonrecursive Models Highlights Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised April 6, 2015
Nonrecursive Models Highlights Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised April 6, 2015 This lecture borrows heavily from Duncan s Introduction to Structural
More information14.32 Final : Spring 2001
14.32 Final : Spring 2001 Please read the entire exam before you begin. You have 3 hours. No books or notes should be used. Calculators are allowed. There are 105 points. Good luck! A. True/False/Sometimes
More informationMediation Analysis: OLS vs. SUR vs. 3SLS Note by Hubert Gatignon July 7, 2013, updated November 15, 2013
Mediation Analysis: OLS vs. SUR vs. 3SLS Note by Hubert Gatignon July 7, 2013, updated November 15, 2013 In Chap. 11 of Statistical Analysis of Management Data (Gatignon, 2014), tests of mediation are
More informationSoc 63993, Homework #7 Answer Key: Nonlinear effects/ Intro to path analysis
Soc 63993, Homework #7 Answer Key: Nonlinear effects/ Intro to path analysis Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised February 20, 2015 Problem 1. The files
More informationEconomics 308: Econometrics Professor Moody
Economics 308: Econometrics Professor Moody References on reserve: Text Moody, Basic Econometrics with Stata (BES) Pindyck and Rubinfeld, Econometric Models and Economic Forecasts (PR) Wooldridge, Jeffrey
More informationIn Class Review Exercises Vartanian: SW 540
In Class Review Exercises Vartanian: SW 540 1. Given the following output from an OLS model looking at income, what is the slope and intercept for those who are black and those who are not black? b SE
More informationECON Introductory Econometrics. Lecture 6: OLS with Multiple Regressors
ECON4150 - Introductory Econometrics Lecture 6: OLS with Multiple Regressors Monique de Haan (moniqued@econ.uio.no) Stock and Watson Chapter 6 Lecture outline 2 Violation of first Least Squares assumption
More information1 Motivation for Instrumental Variable (IV) Regression
ECON 370: IV & 2SLS 1 Instrumental Variables Estimation and Two Stage Least Squares Econometric Methods, ECON 370 Let s get back to the thiking in terms of cross sectional (or pooled cross sectional) data
More informationPlease discuss each of the 3 problems on a separate sheet of paper, not just on a separate page!
Econometrics - Exam May 11, 2011 1 Exam Please discuss each of the 3 problems on a separate sheet of paper, not just on a separate page! Problem 1: (15 points) A researcher has data for the year 2000 from
More informationGeneral Linear Model (Chapter 4)
General Linear Model (Chapter 4) Outcome variable is considered continuous Simple linear regression Scatterplots OLS is BLUE under basic assumptions MSE estimates residual variance testing regression coefficients
More informationThis exam contains 5 questions. Each question is worth 10 points. Therefore, this exam is worth 50 points.
GROUND RULES: This exam contains 5 questions. Each question is worth 10 points. Therefore, this exam is worth 50 points. Print your name at the top of this page in the upper right hand corner. This is
More informationProblem Set 1 ANSWERS
Economics 20 Prof. Patricia M. Anderson Problem Set 1 ANSWERS Part I. Multiple Choice Problems 1. If X and Z are two random variables, then E[X-Z] is d. E[X] E[Z] This is just a simple application of one
More informationInference. ME104: Linear Regression Analysis Kenneth Benoit. August 15, August 15, 2012 Lecture 3 Multiple linear regression 1 1 / 58
Inference ME104: Linear Regression Analysis Kenneth Benoit August 15, 2012 August 15, 2012 Lecture 3 Multiple linear regression 1 1 / 58 Stata output resvisited. reg votes1st spend_total incumb minister
More informationApplied Statistics and Econometrics
Applied Statistics and Econometrics Lecture 5 Saul Lach September 2017 Saul Lach () Applied Statistics and Econometrics September 2017 1 / 44 Outline of Lecture 5 Now that we know the sampling distribution
More informationECON Introductory Econometrics. Lecture 4: Linear Regression with One Regressor
ECON4150 - Introductory Econometrics Lecture 4: Linear Regression with One Regressor Monique de Haan (moniqued@econ.uio.no) Stock and Watson Chapter 4 Lecture outline 2 The OLS estimators The effect of
More informationECON2228 Notes 10. Christopher F Baum. Boston College Economics. cfb (BC Econ) ECON2228 Notes / 48
ECON2228 Notes 10 Christopher F Baum Boston College Economics 2014 2015 cfb (BC Econ) ECON2228 Notes 10 2014 2015 1 / 48 Serial correlation and heteroskedasticity in time series regressions Chapter 12:
More informationStat 500 Midterm 2 12 November 2009 page 0 of 11
Stat 500 Midterm 2 12 November 2009 page 0 of 11 Please put your name on the back of your answer book. Do NOT put it on the front. Thanks. Do not start until I tell you to. The exam is closed book, closed
More informationFinal Exam - Solutions
Ecn 102 - Analysis of Economic Data University of California - Davis March 19, 2010 Instructor: John Parman Final Exam - Solutions You have until 5:30pm to complete this exam. Please remember to put your
More informationECON3150/4150 Spring 2016
ECON3150/4150 Spring 2016 Lecture 6 Multiple regression model Siv-Elisabeth Skjelbred University of Oslo February 5th Last updated: February 3, 2016 1 / 49 Outline Multiple linear regression model and
More informationTesting methodology. It often the case that we try to determine the form of the model on the basis of data
Testing methodology It often the case that we try to determine the form of the model on the basis of data The simplest case: we try to determine the set of explanatory variables in the model Testing for
More informationCan you tell the relationship between students SAT scores and their college grades?
Correlation One Challenge Can you tell the relationship between students SAT scores and their college grades? A: The higher SAT scores are, the better GPA may be. B: The higher SAT scores are, the lower
More information5. Let W follow a normal distribution with mean of μ and the variance of 1. Then, the pdf of W is
Practice Final Exam Last Name:, First Name:. Please write LEGIBLY. Answer all questions on this exam in the space provided (you may use the back of any page if you need more space). Show all work but do
More informationFixed and Random Effects Models: Vartanian, SW 683
: Vartanian, SW 683 Fixed and random effects models See: http://teaching.sociology.ul.ie/dcw/confront/node45.html When you have repeated observations per individual this is a problem and an advantage:
More informationHypothesis Tests and Confidence Intervals. in Multiple Regression
ECON4135, LN6 Hypothesis Tests and Confidence Intervals Outline 1. Why multipple regression? in Multiple Regression (SW Chapter 7) 2. Simpson s paradox (omitted variables bias) 3. Hypothesis tests and
More informationEmpirical Application of Simple Regression (Chapter 2)
Empirical Application of Simple Regression (Chapter 2) 1. The data file is House Data, which can be downloaded from my webpage. 2. Use stata menu File Import Excel Spreadsheet to read the data. Don t forget
More informationMS&E 226: Small Data
MS&E 226: Small Data Lecture 15: Examples of hypothesis tests (v5) Ramesh Johari ramesh.johari@stanford.edu 1 / 32 The recipe 2 / 32 The hypothesis testing recipe In this lecture we repeatedly apply the
More informationCircle the single best answer for each multiple choice question. Your choice should be made clearly.
TEST #1 STA 4853 March 6, 2017 Name: Please read the following directions. DO NOT TURN THE PAGE UNTIL INSTRUCTED TO DO SO Directions This exam is closed book and closed notes. There are 32 multiple choice
More informationSpecification Error: Omitted and Extraneous Variables
Specification Error: Omitted and Extraneous Variables Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised February 5, 05 Omitted variable bias. Suppose that the correct
More information1. You have data on years of work experience, EXPER, its square, EXPER2, years of education, EDUC, and the log of hourly wages, LWAGE
1. You have data on years of work experience, EXPER, its square, EXPER, years of education, EDUC, and the log of hourly wages, LWAGE You estimate the following regressions: (1) LWAGE =.00 + 0.05*EDUC +
More informationEconometrics Part Three
!1 I. Heteroskedasticity A. Definition 1. The variance of the error term is correlated with one of the explanatory variables 2. Example -- the variance of actual spending around the consumption line increases
More informationPractice 2SLS with Artificial Data Part 1
Practice 2SLS with Artificial Data Part 1 Yona Rubinstein July 2016 Yona Rubinstein (LSE) Practice 2SLS with Artificial Data Part 1 07/16 1 / 16 Practice with Artificial Data In this note we use artificial
More informationIntroduction to Econometrics. Multiple Regression
Introduction to Econometrics The statistical analysis of economic (and related) data STATS301 Multiple Regression Titulaire: Christopher Bruffaerts Assistant: Lorenzo Ricci 1 OLS estimate of the TS/STR
More informationProblem Set 5 ANSWERS
Economics 20 Problem Set 5 ANSWERS Prof. Patricia M. Anderson 1, 2 and 3 Suppose that Vermont has passed a law requiring employers to provide 6 months of paid maternity leave. You are concerned that women
More informationWeek 3: Simple Linear Regression
Week 3: Simple Linear Regression Marcelo Coca Perraillon University of Colorado Anschutz Medical Campus Health Services Research Methods I HSMP 7607 2017 c 2017 PERRAILLON ALL RIGHTS RESERVED 1 Outline
More informationECON2228 Notes 7. Christopher F Baum. Boston College Economics. cfb (BC Econ) ECON2228 Notes / 41
ECON2228 Notes 7 Christopher F Baum Boston College Economics 2014 2015 cfb (BC Econ) ECON2228 Notes 6 2014 2015 1 / 41 Chapter 8: Heteroskedasticity In laying out the standard regression model, we made
More informationSTATISTICS 110/201 PRACTICE FINAL EXAM
STATISTICS 110/201 PRACTICE FINAL EXAM Questions 1 to 5: There is a downloadable Stata package that produces sequential sums of squares for regression. In other words, the SS is built up as each variable
More informationEconometrics Review questions for exam
Econometrics Review questions for exam Nathaniel Higgins nhiggins@jhu.edu, 1. Suppose you have a model: y = β 0 x 1 + u You propose the model above and then estimate the model using OLS to obtain: ŷ =
More informationNonlinear Regression Functions
Nonlinear Regression Functions (SW Chapter 8) Outline 1. Nonlinear regression functions general comments 2. Nonlinear functions of one variable 3. Nonlinear functions of two variables: interactions 4.
More information