ECONOMETRICS I. Cheating and the violation of any of the above instructions, lead to the cancellation of the student s paper.
|
|
|
- Shannon Wright
- 6 years ago
- Views:
Transcription
1 Jorge Mendes Maria Jordão Midterm (A) Spring 2014 Undergraduate degree in Information Management 22/04/2014 ECONOMETRICS I Name: Number: Grade: Time to completion: 80 minutes This is a CLOSED book exam. No notes, books, tablets, laptops and cell phones are allowed. The use of a calculator is allowed, but the invigilators can erase the device memory at any time. Using a calculator containing information that would be unacceptable in paper form (e.g., programs or notes entered by a student) is prohibited. Using a calculator with built-in notes or student created programs is considered cheating. Mobile phones are not permitted in the examination room under any circumstances The use of lead pencils or erasable ball points to write answers is strictly disallowed. Students are not allowed to seek clarifications regarding the exam questions. No student is allowed to leave the examination room until half an hour has elapsed after the start of the exam. Students are not allowed to remove the staple from this exam handout. Students must make every effort to write legibly in their exam answer book. This minimizes the risk of marks being lost due to indecipherable content. Whenever omitted, use a 5% significance level. Period (.) is used as decimal marks. Formulate every statistical procedure used to justify your answers. Cheating and the violation of any of the above instructions, lead to the cancellation of the student s paper.
2 SET I To answer the questions for this problem, please consider the results in APPENDIX 1. A group of students from NOVA IMS decided to study users popularity in social networks, namely on facebook. To that end, these students collected information about the number of photo tags (nr_tags), the number of friends (nr_friends), the users age (age) and time (in months) since the user joined facebook (time_member). The group of students started by estimating a simple regression model, in an attempt to explain the number of photo tags with the time since the user joined facebook. Having this model in mind: 1) (2, 00) Statistically and economically interpret the effect from the time since the user joined facebook on the user s popularity. 2) (2, 00) Give a meaning to the value obtained for the coefficient of determination. After thinking a bit more about the subject, students decided to also include the user s age and number of friends on facebook as explanatory variables. Considering the results obtained when estimating such model: 3) (3, 50) Do you agree with the students decision? Justify your answer with the statistical inference you find appropriate and the goodness of fit. 4) (2, 00) Assess the overall significance for the second model estimated. SET II With the aim of studying the Chief Executive Officers salary in a given country and in a given year, a researcher has established the following equation: Where: ln(sal i ) = β 0 + β 1 antde i + β 2 antde i 2 + β 3 ln(vend i ) + β 4 ln(val_merc i ) + u i sal: annual salary, in thousands of euros antde: years as CEO in the company vend: company sales, in millions of euros val_merc: company market value, in millions of euros During research development, several models were estimated, including those presented in APPENDIX II. Based on those results, answer the following questions: 5) (4, 00) Statistically and economically interpret the effects from variables antde, vend and val_merc on the CEO s salary. 6) (2, 50) Consider an individual with an annual salary of million euros, being a CEO for 2 years in a company with a market value of 23.2 million euros and sales of 6.2 million euros. What is the estimation error associated with the estimate for this CEO s salary? 2/9
3 SET III 7) (2, 00) The analysis of a correlation matrix is essential when applying econometrics. To what extent do you agree with this statement? Explain. 8) (2, 00) Show that the OLS estimator (β = (X X) 1 X Y) is unbiased. 3/9
4 APPENDIX 1 The REG Procedure Dependent Variable: nr_tags Number of Observations Read 80 Number of Observations Used 80 Analysis of Variance Source DF Sum of Squares Mean Square F Value Pr > F Model Error Corrected Total Root MSE R-Square Dependent Mean Adj R-Sq Coeff Var Parameter Estimates Parameter Standard Variable DF Estimate Error t Value Pr > t Intercept time_member The REG Procedure Dependent Variable: nr_tags Number of Observations Read 80 Number of Observations Used 80 Analysis of Variance Source DF Sum of Squares Mean Square F Value Pr > F Model <.0001 Error Corrected Total Root MSE R-Square Dependent Mean Adj R-Sq Coeff Var Parameter Estimates Parameter Standard Variable DF Estimate Error t Value Pr > t Intercept nr_friends <.0001 age <.0001 time_member /9
5 APPENDIX 2 The REG Procedure Dependent Variable: ln_sal Number of Observations Read 177 Number of Observations Used 177 Analysis of Variance Source DF Sum of Squares Mean Square F Value Pr > F Model <.0001 Error Corrected Total Root MSE R-Square Dependent Mean Adj R-Sq Coeff Var Parameter Estimates ParameterStandard Variable DF Estimate Error t ValuePr > t Intercept <.0001 ln_vend <.0001 ln_val_merc The REG Procedure Dependent Variable: ln_sal Number of Observations Read 177 Number of Observations Used 177 Analysis of Variance Source DF Sum of Squares Mean Square F Value Pr > F Model <.0001 Error Corrected Total Root MSE R-Square Dependent Mean Adj R-Sq Coeff Var Parameter Estimates Variable DF Parameter Estimate Standard Error t Value Pr > t Intercept <.0001 antde antde ln_vend <.0001 ln_val_merc /9
6 Statistical Inference: t-test: t i = β i β i var(β i) ~t (n k 1) F-test: F = SSR R SSR UR q SSR UR n k 1 = R 2 2 UR R R q 1 R2 ~F (q;n k 1) UR n k 1 Where k stands for the number of regressors, q stands for the number of constraints, n stands for the number of observations and subscripts R and UR indicate the Restricted and the Unrestricted models, respectively. Moments: Cov(X, Y) = E(X Y) E(X)E(Y) Var(X) = E(X 2 ) E 2 (X) Var(X ± Y) = Var(X) + Var(Y) ± 2cov((X, Y) Cov(X, Y) Corr(X, Y) = Var(X) Var(Y) 6/9
7 Percentiles of the t distribution with n degrees of freedom, t (n) n q 0,6 0,7 0,8 0,9 0,95 0,975 0,99 0,995 0,999 0, ,325 0,727 1,376 3,078 6,314 12,706 31,821 63, , , ,289 0,617 1,061 1,886 2,920 4,303 6,965 9,925 22,327 31, ,277 0,584 0,978 1,638 2,353 3,182 4,541 5,841 10,215 12, ,271 0,569 0,941 1,533 2,132 2,776 3,747 4,604 7,173 8, ,267 0,559 0,920 1,476 2,015 2,571 3,365 4,032 5,893 6, ,265 0,553 0,906 1,440 1,943 2,447 3,143 3,707 5,208 5, ,263 0,549 0,896 1,415 1,895 2,365 2,998 3,499 4,785 5, ,262 0,546 0,889 1,397 1,860 2,306 2,896 3,355 4,501 5, ,261 0,543 0,883 1,383 1,833 2,262 2,821 3,250 4,297 4, ,260 0,542 0,879 1,372 1,812 2,228 2,764 3,169 4,144 4, ,260 0,540 0,876 1,363 1,796 2,201 2,718 3,106 4,025 4, ,259 0,539 0,873 1,356 1,782 2,179 2,681 3,055 3,930 4, ,259 0,538 0,870 1,350 1,771 2,160 2,650 3,012 3,852 4, ,258 0,537 0,868 1,345 1,761 2,145 2,624 2,977 3,787 4, ,258 0,536 0,866 1,341 1,753 2,131 2,602 2,947 3,733 4, ,258 0,535 0,865 1,337 1,746 2,120 2,583 2,921 3,686 4, ,257 0,534 0,863 1,333 1,740 2,110 2,567 2,898 3,646 3, ,257 0,534 0,862 1,330 1,734 2,101 2,552 2,878 3,610 3, ,257 0,533 0,861 1,328 1,729 2,093 2,539 2,861 3,579 3, ,257 0,533 0,860 1,325 1,725 2,086 2,528 2,845 3,552 3, ,257 0,532 0,859 1,323 1,721 2,080 2,518 2,831 3,527 3, ,256 0,532 0,858 1,321 1,717 2,074 2,508 2,819 3,505 3, ,256 0,532 0,858 1,319 1,714 2,069 2,500 2,807 3,485 3, ,256 0,531 0,857 1,318 1,711 2,064 2,492 2,797 3,467 3, ,256 0,531 0,856 1,316 1,708 2,060 2,485 2,787 3,450 3, ,256 0,531 0,856 1,315 1,706 2,056 2,479 2,779 3,435 3, ,256 0,531 0,855 1,314 1,703 2,052 2,473 2,771 3,421 3, ,256 0,530 0,855 1,313 1,701 2,048 2,467 2,763 3,408 3, ,256 0,530 0,854 1,311 1,699 2,045 2,462 2,756 3,396 3, ,256 0,530 0,854 1,310 1,697 2,042 2,457 2,750 3,385 3, ,255 0,529 0,852 1,306 1,690 2,030 2,438 2,724 3,340 3, ,255 0,529 0,851 1,303 1,684 2,021 2,423 2,704 3,307 3, ,255 0,528 0,850 1,301 1,679 2,014 2,412 2,690 3,281 3, ,255 0,528 0,849 1,299 1,676 2,009 2,403 2,678 3,261 3, ,254 0,527 0,848 1,296 1,671 2,000 2,390 2,660 3,232 3, ,254 0,527 0,847 1,294 1,667 1,994 2,381 2,648 3,211 3, ,254 0,526 0,846 1,292 1,664 1,990 2,374 2,639 3,195 3, ,254 0,526 0,846 1,291 1,662 1,987 2,368 2,632 3,183 3, ,254 0,526 0,845 1,290 1,660 1,984 2,364 2,626 3,174 3, ,254 0,526 0,844 1,287 1,655 1,976 2,351 2,609 3,145 3, ,254 0,525 0,843 1,286 1,653 1,972 2,345 2,601 3,131 3, ,253 0,525 0,842 1,282 1,646 1,962 2,330 2,581 3,098 3, ,253 0,524 0,842 1,282 1,645 1,960 2,326 2,576 3,090 3,291 7/9
8 F distribution with n and m degrees of freedom and probability q m Prob. n ,025 0,002 0,026 0,057 0,082 0,100 0,113 0,124 0,132 0,139 0,144 0,153 0,159 0, , , , , , , , , , , , , , ,464 0, , , , , , , , , , , , , ,919 0,025 0,001 0,026 0,062 0,094 0,119 0,138 0,153 0,165 0,175 0,183 0,196 0,206 0, ,950 18,513 19,000 19,164 19,247 19,296 19,330 19,353 19,371 19,385 19,396 19,413 19,424 19,433 0,975 38,506 39,000 39,165 39,248 39,298 39,331 39,355 39,373 39,387 39,398 39,415 39,427 39,435 0,025 0,001 0,026 0,065 0,100 0,129 0,152 0,170 0,185 0,197 0,207 0,224 0,236 0, ,950 10,128 9,552 9,277 9,117 9,013 8,941 8,887 8,845 8,812 8,786 8,745 8,715 8,692 0,975 17,443 16,044 15,439 15,101 14,885 14,735 14,624 14,540 14,473 14,419 14,337 14,277 14,232 0,025 0,001 0,025 0,066 0,104 0,135 0,161 0,181 0,198 0,212 0,224 0,243 0,257 0, ,950 7,709 6,944 6,591 6,388 6,256 6,163 6,094 6,041 5,999 5,964 5,912 5,873 5,844 0,975 12,218 10,649 9,979 9,605 9,364 9,197 9,074 8,980 8,905 8,844 8,751 8,684 8,633 0,025 0,001 0,025 0,067 0,107 0,140 0,167 0,189 0,208 0,223 0,236 0,257 0,273 0, ,950 6,608 5,786 5,409 5,192 5,050 4,950 4,876 4,818 4,772 4,735 4,678 4,636 4,604 0,975 10,007 8,434 7,764 7,388 7,146 6,978 6,853 6,757 6,681 6,619 6,525 6,456 6,403 0,025 0,001 0,025 0,068 0,109 0,143 0,172 0,195 0,215 0,231 0,246 0,268 0,286 0, ,950 5,987 5,143 4,757 4,534 4,387 4,284 4,207 4,147 4,099 4,060 4,000 3,956 3,922 0,975 8,813 7,260 6,599 6,227 5,988 5,820 5,695 5,600 5,523 5,461 5,366 5,297 5,244 0,025 0,001 0,025 0,068 0,110 0,146 0,176 0,200 0,221 0,238 0,253 0,277 0,296 0, ,950 5,591 4,737 4,347 4,120 3,972 3,866 3,787 3,726 3,677 3,637 3,575 3,529 3,494 0,975 8,073 6,542 5,890 5,523 5,285 5,119 4,995 4,899 4,823 4,761 4,666 4,596 4,543 0,025 0,001 0,025 0,069 0,111 0,148 0,179 0,204 0,226 0,244 0,259 0,285 0,304 0, ,950 5,318 4,459 4,066 3,838 3,687 3,581 3,500 3,438 3,388 3,347 3,284 3,237 3,202 0,975 7,571 6,059 5,416 5,053 4,817 4,652 4,529 4,433 4,357 4,295 4,200 4,130 4,076 0,025 0,001 0,025 0,069 0,112 0,150 0,181 0,207 0,230 0,248 0,265 0,291 0,312 0, ,950 5,117 4,256 3,863 3,633 3,482 3,374 3,293 3,230 3,179 3,137 3,073 3,025 2,989 0,975 7,209 5,715 5,078 4,718 4,484 4,320 4,197 4,102 4,026 3,964 3,868 3,798 3,744 0,025 0,001 0,025 0,069 0,113 0,151 0,183 0,210 0,233 0,252 0,269 0,296 0,318 0, ,950 4,965 4,103 3,708 3,478 3,326 3,217 3,135 3,072 3,020 2,978 2,913 2,865 2,828 0,975 6,937 5,456 4,826 4,468 4,236 4,072 3,950 3,855 3,779 3,717 3,621 3,550 3,496 0,025 0,001 0,025 0,070 0,114 0,153 0,186 0,214 0,238 0,259 0,276 0,305 0,328 0, ,950 4,747 3,885 3,490 3,259 3,106 2,996 2,913 2,849 2,796 2,753 2,687 2,637 2,599 0,975 6,554 5,096 4,474 4,121 3,891 3,728 3,607 3,512 3,436 3,374 3,277 3,206 3,152 0,025 0,001 0,025 0,070 0,115 0,155 0,189 0,218 0,242 0,263 0,282 0,312 0,336 0, ,950 4,600 3,739 3,344 3,112 2,958 2,848 2,764 2,699 2,646 2,602 2,534 2,484 2,445 0,975 6,298 4,857 4,242 3,892 3,663 3,501 3,380 3,285 3,209 3,147 3,050 2,979 2,923 0,025 0,001 0,025 0,070 0,116 0,156 0,191 0,220 0,245 0,267 0,286 0,317 0,342 0, ,950 4,494 3,634 3,239 3,007 2,852 2,741 2,657 2,591 2,538 2,494 2,425 2,373 2,333 0,975 6,115 4,687 4,077 3,729 3,502 3,341 3,219 3,125 3,049 2,986 2,889 2,817 2,761 0,025 0,001 0,025 0,070 0,116 0,157 0,192 0,222 0,248 0,270 0,290 0,322 0,347 0, ,950 4,414 3,555 3,160 2,928 2,773 2,661 2,577 2,510 2,456 2,412 2,342 2,290 2,250 0,975 5,978 4,560 3,954 3,608 3,382 3,221 3,100 3,005 2,929 2,866 2,769 2,696 2,640 0,025 0,001 0,025 0,071 0,117 0,158 0,193 0,224 0,250 0,273 0,293 0,325 0,352 0, ,950 4,351 3,493 3,098 2,866 2,711 2,599 2,514 2,447 2,393 2,348 2,278 2,225 2,184 0,975 5,871 4,461 3,859 3,515 3,289 3,128 3,007 2,913 2,837 2,774 2,676 2,603 2,547 0,025 0,001 0,025 0,071 0,118 0,161 0,197 0,229 0,257 0,281 0,302 0,337 0,366 0, ,950 4,171 3,316 2,922 2,690 2,534 2,421 2,334 2,266 2,211 2,165 2,092 2,037 1,995 0,975 5,568 4,182 3,589 3,250 3,026 2,867 2,746 2,651 2,575 2,511 2,412 2,338 2,280 0,025 0,001 0,025 0,071 0,119 0,162 0,200 0,232 0,260 0,285 0,307 0,344 0,374 0, ,950 4,085 3,232 2,839 2,606 2,449 2,336 2,249 2,180 2,124 2,077 2,003 1,948 1,904 0,975 5,424 4,051 3,463 3,126 2,904 2,744 2,624 2,529 2,452 2,388 2,288 2,213 2,154 0,025 0,001 0,025 0,071 0,119 0,163 0,201 0,234 0,263 0,288 0,310 0,348 0,379 0, ,950 4,034 3,183 2,790 2,557 2,400 2,286 2,199 2,130 2,073 2,026 1,952 1,895 1,850 0,975 5,340 3,975 3,390 3,054 2,833 2,674 2,553 2,458 2,381 2,317 2,216 2,140 2,081 0,025 0,001 0,025 0,072 0,120 0,164 0,202 0,236 0,265 0,291 0,314 0,353 0,385 0, ,950 3,978 3,128 2,736 2,503 2,346 2,231 2,143 2,074 2,017 1,969 1,893 1,836 1,790 0,025 0,001 0,025 0,072 0,120 0,164 0,202 0,236 0,265 0,291 0,314 0,353 0,385 0,412 0,025 0,001 0,025 0,072 0,120 0,164 0,203 0,237 0,266 0,292 0,316 0,355 0,387 0, ,950 3,960 3,111 2,719 2,486 2,329 2,214 2,126 2,056 1,999 1,951 1,875 1,817 1,772 0,975 5,218 3,864 3,284 2,950 2,730 2,571 2,450 2,355 2,277 2,213 2,111 2,035 1,974 0,025 0,001 0,025 0,072 0,120 0,164 0,203 0,237 0,267 0,293 0,316 0,356 0,389 0, ,950 3,947 3,098 2,706 2,473 2,316 2,201 2,113 2,043 1,986 1,938 1,861 1,803 1,757 0,975 5,196 3,844 3,265 2,932 2,711 2,552 2,432 2,336 2,259 2,194 2,092 2,015 1,955 0,025 0,001 0,025 0,072 0,120 0,164 0,203 0,238 0,267 0,294 0,317 0,357 0,390 0, ,950 3,936 3,087 2,696 2,463 2,305 2,191 2,103 2,032 1,975 1,927 1,850 1,792 1,746 0,975 5,179 3,828 3,250 2,917 2,696 2,537 2,417 2,321 2,244 2,179 2,077 2,000 1,939 0,025 0,001 0,025 0,072 0,121 0,165 0,205 0,239 0,270 0,297 0,321 0,362 0,396 0, ,950 3,888 3,041 2,650 2,417 2,259 2,144 2,056 1,985 1,927 1,878 1,801 1,742 1,694 0,975 5,100 3,758 3,182 2,850 2,630 2,472 2,351 2,256 2,178 2,113 2,010 1,932 1,870 Exemplo: P(F (n,m) 4,10)=0,95 para n=2 e m=10 8/9
9 SCRATCH 9/9
Econometrics 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
ECON2228 Notes 2. Christopher F Baum. Boston College Economics. cfb (BC Econ) ECON2228 Notes / 47
ECON2228 Notes 2 Christopher F Baum Boston College Economics 2014 2015 cfb (BC Econ) ECON2228 Notes 2 2014 2015 1 / 47 Chapter 2: The simple regression model Most of this course will be concerned with
MGEC11H3Y L01 Introduction to Regression Analysis Term Test Friday July 5, PM Instructor: Victor Yu
Last Name (Print): Solution First Name (Print): Student Number: MGECHY L Introduction to Regression Analysis Term Test Friday July, PM Instructor: Victor Yu Aids allowed: Time allowed: Calculator and one
Multiple Linear Regression CIVL 7012/8012
Multiple Linear Regression CIVL 7012/8012 2 Multiple Regression Analysis (MLR) Allows us to explicitly control for many factors those simultaneously affect the dependent variable This is important for
STAT 512 MidTerm I (2/21/2013) Spring 2013 INSTRUCTIONS
STAT 512 MidTerm I (2/21/2013) Spring 2013 Name: Key INSTRUCTIONS 1. This exam is open book/open notes. All papers (but no electronic devices except for calculators) are allowed. 2. There are 5 pages in
WISE MA/PhD Programs Econometrics Instructor: Brett Graham Spring Semester, Academic Year Exam Version: A
WISE MA/PhD Programs Econometrics Instructor: Brett Graham Spring Semester, 2016-17 Academic Year Exam Version: A INSTRUCTIONS TO STUDENTS 1 The time allowed for this examination paper is 2 hours. 2 This
Econometrics 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
Apart from this page, you are not permitted to read the contents of this question paper until instructed to do so by an invigilator.
B. Sc. Examination by course unit 2014 MTH5120 Statistical Modelling I Duration: 2 hours Date and time: 16 May 2014, 1000h 1200h Apart from this page, you are not permitted to read the contents of this
MATH 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
WISE MA/PhD Programs Econometrics Instructor: Brett Graham Spring Semester, Academic Year Exam Version: A
WISE MA/PhD Programs Econometrics Instructor: Brett Graham Spring Semester, 2016-17 Academic Year Exam Version: A INSTRUCTIONS TO STUDENTS 1 The time allowed for this examination paper is 2 hours. 2 This
Answer 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
WISE International Masters
WISE International Masters ECONOMETRICS Instructor: Brett Graham INSTRUCTIONS TO STUDENTS 1 The time allowed for this examination paper is 2 hours. 2 This examination paper contains 32 questions. You are
WISE International Masters
WISE International Masters ECONOMETRICS Instructor: Brett Graham INSTRUCTIONS TO STUDENTS 1 The time allowed for this examination paper is 2 hours. 2 This examination paper contains 32 questions. You are
ECON3150/4150 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
Analysis of Variance. Source DF Squares Square F Value Pr > F. Model <.0001 Error Corrected Total
Math 221: Linear Regression and Prediction Intervals S. K. Hyde Chapter 23 (Moore, 5th Ed.) (Neter, Kutner, Nachsheim, and Wasserman) The Toluca Company manufactures refrigeration equipment as well as
EC4051 Project and Introductory Econometrics
EC4051 Project and Introductory Econometrics Dudley Cooke Trinity College Dublin Dudley Cooke (Trinity College Dublin) Intro to Econometrics 1 / 23 Project Guidelines Each student is required to undertake
Chapter 1: Linear Regression with One Predictor Variable also known as: Simple Linear Regression Bivariate Linear Regression
BSTT523: Kutner et al., Chapter 1 1 Chapter 1: Linear Regression with One Predictor Variable also known as: Simple Linear Regression Bivariate Linear Regression Introduction: Functional relation between
Econometrics 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
M(t) = 1 t. (1 t), 6 M (0) = 20 P (95. X i 110) i=1
Math 66/566 - Midterm Solutions NOTE: These solutions are for both the 66 and 566 exam. The problems are the same until questions and 5. 1. The moment generating function of a random variable X is M(t)
Section 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
Midterm Exam 1 Solution
EECS 126 Probability and Random Processes University of California, Berkeley: Fall 2015 Kannan Ramchandran September 22, 2015 Midterm Exam 1 Solution Last name First name SID Name of student on your left:
STAT 3900/4950 MIDTERM TWO Name: Spring, 2015 (print: first last ) Covered topics: Two-way ANOVA, ANCOVA, SLR, MLR and correlation analysis
STAT 3900/4950 MIDTERM TWO Name: Spring, 205 (print: first last ) Covered topics: Two-way ANOVA, ANCOVA, SLR, MLR and correlation analysis Instructions: You may use your books, notes, and SPSS/SAS. NO
MATHEMATICAL METHODS (CAS) PILOT STUDY
Victorian CertiÞcate of Education 2005 SUPERVISOR TO ATTACH PROCESSING LABEL HERE STUDENT NUMBER Letter Figures Words MATHEMATICAL METHODS (CAS) PILOT STUDY Written examination 2 (Analysis task) Monday
Paper: ST-161. Techniques for Evidence-Based Decision Making Using SAS Ian Stockwell, The Hilltop UMBC, Baltimore, MD
Paper: ST-161 Techniques for Evidence-Based Decision Making Using SAS Ian Stockwell, The Hilltop Institute @ UMBC, Baltimore, MD ABSTRACT SAS has many tools that can be used for data analysis. From Freqs
14.32 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
Wooldridge, Introductory Econometrics, 4th ed. Chapter 2: The simple regression model
Wooldridge, Introductory Econometrics, 4th ed. Chapter 2: The simple regression model Most of this course will be concerned with use of a regression model: a structure in which one or more explanatory
ECO375 Tutorial 4 Wooldridge: Chapter 6 and 7
ECO375 Tutorial 4 Wooldridge: Chapter 6 and 7 Matt Tudball University of Toronto St. George October 6, 2017 Matt Tudball (University of Toronto) ECO375H1 October 6, 2017 1 / 36 ECO375 Tutorial 4 Welcome
MTH4107 / MTH4207: Introduction to Probability
Main Examination period 2018 MTH4107 / MTH4207: Introduction to Probability Duration: 2 hours Student number Desk number Make and model of calculator used Apart from this page, you are not permitted to
ECO220Y 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
Reading Time: 15 minutes Writing Time: 1 hour. Structure of Booklet. Number of questions to be answered
Reading Time: 15 minutes Writing Time: 1 hour Student Name: Structure of Booklet Number of questions Number of questions to be answered Number of marks 10 10 40 Students are permitted to bring into the
Multiple Regression: Inference
Multiple Regression: Inference The t-test: is ˆ j big and precise enough? We test the null hypothesis: H 0 : β j =0; i.e. test that x j has no effect on y once the other explanatory variables are controlled
STA 302 H1F / 1001 HF Fall 2007 Test 1 October 24, 2007
STA 302 H1F / 1001 HF Fall 2007 Test 1 October 24, 2007 LAST NAME: SOLUTIONS FIRST NAME: STUDENT NUMBER: ENROLLED IN: (circle one) STA 302 STA 1001 INSTRUCTIONS: Time: 90 minutes Aids allowed: calculator.
ECON3150/4150 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
1: 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.
MATHEMATICAL METHODS (CAS) PILOT STUDY
Victorian Certificate of Education 2004 SUPERVISOR TO ATTACH PROCESSING LABEL HERE MATHEMATICAL METHODS (CAS) PILOT STUDY Written examination 2 (Analysis task) Monday 8 November 2004 Reading time: 9.00
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)
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
T-test: means of Spock's judge versus all other judges 1 12:10 Wednesday, January 5, judge1 N Mean Std Dev Std Err Minimum Maximum
T-test: means of Spock's judge versus all other judges 1 The TTEST Procedure Variable: pcwomen judge1 N Mean Std Dev Std Err Minimum Maximum OTHER 37 29.4919 7.4308 1.2216 16.5000 48.9000 SPOCKS 9 14.6222
Midterm 1 ECO Undergraduate Econometrics
Midterm ECO 23 - Undergraduate Econometrics Prof. Carolina Caetano INSTRUCTIONS Reading and understanding the instructions is your responsibility. Failure to comply may result in loss of points, and there
MATHEMATICAL METHODS
Victorian Certificate of Education 2016 SUPERVISOR TO ATTACH PROCESSING LABEL HERE Letter STUDENT NUMBER MATHEMATICAL METHODS Written examination 1 Wednesday 2 November 2016 Reading time: 9.00 am to 9.15
Midterm Exam 1, section 1. Tuesday, February hour, 30 minutes
San Francisco State University Michael Bar ECON 312 Spring 2018 Midterm Exam 1, section 1 Tuesday, February 20 1 hour, 30 minutes Name: Instructions 1. This is closed book, closed notes exam. 2. You can
Linear Models in Econometrics
Linear Models in Econometrics Nicky Grant At the most fundamental level econometrics is the development of statistical techniques suited primarily to answering economic questions and testing economic theories.
Simple Linear Regression
Simple Linear Regression In simple linear regression we are concerned about the relationship between two variables, X and Y. There are two components to such a relationship. 1. The strength of the relationship.
MATHEMATICAL METHODS (CAS)
Victorian Certificate of Education 2015 SUPERVISOR TO ATTACH PROCESSING LABEL HERE Letter STUDENT NUMBER MATHEMATICAL METHODS (CAS) Written examination 1 Wednesday 4 November 2015 Reading time: 9.00 am
Econometrics I KS. Module 1: Bivariate Linear Regression. Alexander Ahammer. This version: March 12, 2018
Econometrics I KS Module 1: Bivariate Linear Regression Alexander Ahammer Department of Economics Johannes Kepler University of Linz This version: March 12, 2018 Alexander Ahammer (JKU) Module 1: Bivariate
Multiple 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:
ECON 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
Final Exam. Economics 835: Econometrics. Fall 2010
Final Exam Economics 835: Econometrics Fall 2010 Please answer the question I ask - no more and no less - and remember that the correct answer is often short and simple. 1 Some short questions a) For each
Stat 500 Midterm 2 8 November 2007 page 0 of 4
Stat 500 Midterm 2 8 November 2007 page 0 of 4 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. You are welcome to read this front
STATISTICS 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
ECON 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
WISE MA/PhD Programs Econometrics Instructor: Brett Graham Spring Semester, Academic Year Exam Version: A
WISE MA/PhD Programs Econometrics Instructor: Brett Graham Spring Semester, 2015-16 Academic Year Exam Version: A INSTRUCTIONS TO STUDENTS 1 The time allowed for this examination paper is 2 hours. 2 This
FURTHER MATHEMATICS Units 3 & 4 - Written Examination 2
THIS BOX IS FOR ILLUSTRATIVE PURPOSES ONLY 2016 Examination Package - Trial Examination 4 of 5 Figures STUDENT NUMBER Letter Words FURTHER MATHEMATICS Units 3 & 4 - Written Examination 2 (TSSM s 2014 trial
The Simple Linear Regression Model
The Simple Linear Regression Model Lesson 3 Ryan Safner 1 1 Department of Economics Hood College ECON 480 - Econometrics Fall 2017 Ryan Safner (Hood College) ECON 480 - Lesson 3 Fall 2017 1 / 77 Bivariate
Econometrics I Lecture 3: The Simple Linear Regression Model
Econometrics I Lecture 3: The Simple Linear Regression Model Mohammad Vesal Graduate School of Management and Economics Sharif University of Technology 44716 Fall 1397 1 / 32 Outline Introduction Estimating
5. 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
ECON3150/4150 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
Correlation and Regression
Correlation and Regression October 25, 2017 STAT 151 Class 9 Slide 1 Outline of Topics 1 Associations 2 Scatter plot 3 Correlation 4 Regression 5 Testing and estimation 6 Goodness-of-fit STAT 151 Class
STAT 350: Summer Semester Midterm 1: Solutions
Name: Student Number: STAT 350: Summer Semester 2008 Midterm 1: Solutions 9 June 2008 Instructor: Richard Lockhart Instructions: This is an open book test. You may use notes, text, other books and a calculator.
CAS MA575 Linear Models
CAS MA575 Linear Models Boston University, Fall 2013 Midterm Exam (Correction) Instructor: Cedric Ginestet Date: 22 Oct 2013. Maximal Score: 200pts. Please Note: You will only be graded on work and answers
1 A Non-technical Introduction to Regression
1 A Non-technical Introduction to Regression Chapters 1 and Chapter 2 of the textbook are reviews of material you should know from your previous study (e.g. in your second year course). They cover, in
STAT 510 Final Exam Spring 2015
STAT 510 Final Exam Spring 2015 Instructions: The is a closed-notes, closed-book exam No calculator or electronic device of any kind may be used Use nothing but a pen or pencil Please write your name and
Stat 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
MBA Statistics COURSE #4
MBA Statistics 51-651-00 COURSE #4 Simple and multiple linear regression What should be the sales of ice cream? Example: Before beginning building a movie theater, one must estimate the daily number of
ECONOMICS AND ECONOMIC METHODS PRELIM EXAM Statistics and Econometrics August 2007
ECONOMICS AND ECONOMIC METHODS PRELIM EXAM Statistics and Econometrics August 2007 Instructions: Answer all six (6) questions. Point totals are given in parentheses. The parts within each question receive
1 Linear Regression Analysis The Mincer Wage Equation Data Econometric Model Estimation... 11
Econ 495 - Econometric Review 1 Contents 1 Linear Regression Analysis 4 1.1 The Mincer Wage Equation................. 4 1.2 Data............................. 6 1.3 Econometric Model.....................
Question 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
Midterm Exam 1 (Solutions)
EECS 6 Probability and Random Processes University of California, Berkeley: Spring 07 Kannan Ramchandran February 3, 07 Midterm Exam (Solutions) Last name First name SID Name of student on your left: Name
CHAPTER 2: Assumptions and Properties of Ordinary Least Squares, and Inference in the Linear Regression Model
CHAPTER 2: Assumptions and Properties of Ordinary Least Squares, and Inference in the Linear Regression Model Prof. Alan Wan 1 / 57 Table of contents 1. Assumptions in the Linear Regression Model 2 / 57
Statistical 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
ECON 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
Inference for Regression
Inference for Regression Section 9.4 Cathy Poliak, Ph.D. cathy@math.uh.edu Office in Fleming 11c Department of Mathematics University of Houston Lecture 13b - 3339 Cathy Poliak, Ph.D. cathy@math.uh.edu
Lecture 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
Lab 6 - Simple Regression
Lab 6 - Simple Regression Spring 2017 Contents 1 Thinking About Regression 2 2 Regression Output 3 3 Fitted Values 5 4 Residuals 6 5 Functional Forms 8 Updated from Stata tutorials provided by Prof. Cichello
Table of z values and probabilities for the standard normal distribution. z is the first column plus the top row. Each cell shows P(X z).
Table of z values and probabilities for the standard normal distribution. z is the first column plus the top row. Each cell shows P(X z). For example P(X.04) =.8508. For z < 0 subtract the value from,
University of California at Berkeley Fall Introductory Applied Econometrics Final examination. Scores add up to 125 points
EEP 118 / IAS 118 Elisabeth Sadoulet and Kelly Jones University of California at Berkeley Fall 2008 Introductory Applied Econometrics Final examination Scores add up to 125 points Your name: SID: 1 1.
Concordia University (5+5)Q 1.
(5+5)Q 1. Concordia University Department of Mathematics and Statistics Course Number Section Statistics 360/1 40 Examination Date Time Pages Mid Term Test May 26, 2004 Two Hours 3 Instructor Course Examiner
MA 262, Spring 2018, Midterm 1 Version 01 (Green)
MA 262, Spring 2018, Midterm 1 Version 01 (Green) INSTRUCTIONS 1. Switch off your phone upon entering the exam room. 2. Do not open the exam booklet until you are instructed to do so. 3. Before you open
1 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
unadjusted model for baseline cholesterol 22:31 Monday, April 19,
unadjusted model for baseline cholesterol 22:31 Monday, April 19, 2004 1 Class Level Information Class Levels Values TRETGRP 3 3 4 5 SEX 2 0 1 Number of observations 916 unadjusted model for baseline cholesterol
1 Independent Practice: Hypothesis tests for one parameter:
1 Independent Practice: Hypothesis tests for one parameter: Data from the Indian DHS survey from 2006 includes a measure of autonomy of the women surveyed (a scale from 0-10, 10 being the most autonomous)
1 Warm-Up: 2 Adjusted R 2. Introductory Applied Econometrics EEP/IAS 118 Spring Sylvan Herskowitz Section #
Introductory Applied Econometrics EEP/IAS 118 Spring 2015 Sylvan Herskowitz Section #10 4-1-15 1 Warm-Up: Remember that exam you took before break? We had a question that said this A researcher wants to
Simple Linear Regression: One Quantitative IV
Simple Linear Regression: One Quantitative IV Linear regression is frequently used to explain variation observed in a dependent variable (DV) with theoretically linked independent variables (IV). For example,
TMA4255 Applied Statistics V2016 (5)
TMA4255 Applied Statistics V2016 (5) Part 2: Regression Simple linear regression [11.1-11.4] Sum of squares [11.5] Anna Marie Holand To be lectured: January 26, 2016 wiki.math.ntnu.no/tma4255/2016v/start
The simple linear regression model discussed in Chapter 13 was written as
1519T_c14 03/27/2006 07:28 AM Page 614 Chapter Jose Luis Pelaez Inc/Blend Images/Getty Images, Inc./Getty Images, Inc. 14 Multiple Regression 14.1 Multiple Regression Analysis 14.2 Assumptions of the Multiple
Section 3: Simple Linear Regression
Section 3: Simple Linear Regression Carlos M. Carvalho The University of Texas at Austin McCombs School of Business http://faculty.mccombs.utexas.edu/carlos.carvalho/teaching/ 1 Regression: General Introduction
Table 1: Fish Biomass data set on 26 streams
Math 221: Multiple Regression S. K. Hyde Chapter 27 (Moore, 5th Ed.) The following data set contains observations on the fish biomass of 26 streams. The potential regressors from which we wish to explain
Question 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
Math 51 Midterm 1 July 6, 2016
Math 51 Midterm 1 July 6, 2016 Name: SUID#: Circle your section: Section 01 Section 02 (1:30-2:50PM) (3:00-4:20PM) Complete the following problems. In order to receive full credit, please show all of your
INSIGHT YEAR 12 Trial Exam Paper
INSIGHT YEAR 12 Trial Exam Paper 2013 MATHEMATICAL METHODS (CAS) STUDENT NAME: Written examination 1 QUESTION AND ANSWER BOOK Reading time: 15 minutes Writing time: 1 hour Structure of book Number of questions
Data Mining and Data Warehousing. Henryk Maciejewski. Data Mining Predictive modelling: regression
Data Mining and Data Warehousing Henryk Maciejewski Data Mining Predictive modelling: regression Algorithms for Predictive Modelling Contents Regression Classification Auxiliary topics: Estimation of prediction
2. (3.5) (iii) Simply drop one of the independent variables, say leisure: GP A = β 0 + β 1 study + β 2 sleep + β 3 work + u.
BOSTON COLLEGE Department of Economics EC 228 Econometrics, Prof. Baum, Ms. Yu, Fall 2003 Problem Set 3 Solutions Problem sets should be your own work. You may work together with classmates, but if you
Statistical Inference. Part IV. Statistical Inference
Part IV Statistical Inference As of Oct 5, 2017 Sampling Distributions of the OLS Estimator 1 Statistical Inference Sampling Distributions of the OLS Estimator Testing Against One-Sided Alternatives Two-Sided
ANALYSES OF NCGS DATA FOR ALCOHOL STATUS CATEGORIES 1 22:46 Sunday, March 2, 2003
ANALYSES OF NCGS DATA FOR ALCOHOL STATUS CATEGORIES 1 22:46 Sunday, March 2, 2003 The MEANS Procedure DRINKING STATUS=1 Analysis Variable : TRIGL N Mean Std Dev Minimum Maximum 164 151.6219512 95.3801744
Chapter 12 - Lecture 2 Inferences about regression coefficient
Chapter 12 - Lecture 2 Inferences about regression coefficient April 19th, 2010 Facts about slope Test Statistic Confidence interval Hypothesis testing Test using ANOVA Table Facts about slope In previous
STOCKHOLM UNIVERSITY Department of Economics Course name: Empirical Methods Course code: EC40 Examiner: Lena Nekby Number of credits: 7,5 credits Date of exam: Friday, June 5, 009 Examination time: 3 hours
Econ 1123: Section 2. Review. Binary Regressors. Bivariate. Regression. Omitted Variable Bias
Contact Information Elena Llaudet Sections are voluntary. My office hours are Thursdays 5pm-7pm in Littauer Mezzanine 34-36 (Note room change) You can email me administrative questions to ellaudet@gmail.com.
Contest Quiz 3. Question Sheet. In this quiz we will review concepts of linear regression covered in lecture 2.
Updated: November 17, 2011 Lecturer: Thilo Klein Contact: tk375@cam.ac.uk Contest Quiz 3 Question Sheet In this quiz we will review concepts of linear regression covered in lecture 2. NOTE: Please round
Economics 345: Applied Econometrics Section A01 University of Victoria Midterm Examination #2 Version 2 Fall 2016 Instructor: Martin Farnham
Economics 345: Applied Econometrics Section A01 University of Victoria Midterm Examination #2 Version 2 Fall 2016 Instructor: Martin Farnham Last name (family name): First name (given name): Student ID
CSE548, AMS542: Analysis of Algorithms, Fall 2016 Date: Nov 30. Final In-Class Exam. ( 7:05 PM 8:20 PM : 75 Minutes )
CSE548, AMS542: Analysis of Algorithms, Fall 2016 Date: Nov 30 Final In-Class Exam ( 7:05 PM 8:20 PM : 75 Minutes ) This exam will account for either 15% or 30% of your overall grade depending on your
Multivariate Regression: Part I
Topic 1 Multivariate Regression: Part I ARE/ECN 240 A Graduate Econometrics Professor: Òscar Jordà Outline of this topic Statement of the objective: we want to explain the behavior of one variable as a