for var trstprl trstlgl trstplc trstplt trstep: reg X trust10 stfeco yrbrn hinctnt edulvl pltcare polint wrkprty
|
|
- Corey Cross
- 6 years ago
- Views:
Transcription
1 for var trstprl trstlgl trstplc trstplt trstep: reg X trust10 stfeco yrbrn hinctnt edulvl pltcare polint wrkprty -> reg trstprl trust10 stfeco yrbrn hinctnt edulvl pltcare polint wrkprty Source SS df MS Number of obs = F( 8, 22522) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = trstprl Coef. Std. Err. t P> t [95% Conf. Interval] trust stfeco yrbrn hinctnt edulvl pltcare polintr wrkprty _cons > reg trstlgl trust10 stfeco yrbrn hinctnt edulvl pltcare polint wrkprty Source SS df MS Number of obs = F( 8, 22554) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = trstlgl Coef. Std. Err. t P> t [95% Conf. Interval] trust stfeco yrbrn hinctnt edulvl pltcare polintr wrkprty _cons
2 -> reg trstplc trust10 stfeco yrbrn hinctnt edulvl pltcare polint wrkprty Source SS df MS Number of obs = F( 8, 22795) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = trstplc Coef. Std. Err. t P> t [95% Conf. Interval] trust stfeco yrbrn hinctnt edulvl pltcare polintr wrkprty _cons
3 -> reg trstplt trust10 stfeco yrbrn hinctnt edulvl pltcare polint wrkprty Source SS df MS Number of obs = F( 8, 22694) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = trstplt Coef. Std. Err. t P> t [95% Conf. Interval] trust stfeco yrbrn hinctnt edulvl pltcare polintr wrkprty _cons
4 -> reg trstep trust10 stfeco yrbrn hinctnt edulvl pltcare polint wrkprty Source SS df MS Number of obs = F( 8, 19746) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = trstep Coef. Std. Err. t P> t [95% Conf. Interval] trust stfeco yrbrn hinctnt edulvl pltcare polintr wrkprty _cons
5 . sureg (trstprl trust10 stfeco yrbrn hinctnt edulvl pltcare polint wrkprty) (trstlgl trust10 stfeco yrbrn hinctnt edulvl pltcare polint wrkprty) (trstplc trust10 stfeco yrbrn hinctnt e > dulvl pltcare polint wrkprty) (trstplt trust10 stfeco yrbrn hinctnt edulvl pltcare polint wrkprty) (trstep trust10 stfeco yrbrn hinctnt edulvl pltcare polint wrkprty) Seemingly unrelated regression Equation Obs Parms RMSE "R-sq" chi2 P trstprl trstlgl trstplc trstplt trstep Coef. Std. Err. z P> z [95% Conf. Interval] trstprl trust stfeco yrbrn hinctnt edulvl pltcare polintr wrkprty _cons trstlgl trust stfeco yrbrn hinctnt edulvl pltcare polintr wrkprty _cons trstplc trust stfeco yrbrn hinctnt edulvl pltcare polintr wrkprty _cons
6 trstplt trust stfeco yrbrn hinctnt edulvl pltcare polintr wrkprty _cons trstep trust stfeco yrbrn hinctnt edulvl pltcare polintr wrkprty _cons
7 reg corrupt2003 Trust GINI gdppc2000pwt wbstable Source SS df MS Number of obs = F( 4, 53) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = corrupt2003 Coef. Std. Err. t P> t [95% Conf. Interval] Trust GINI gdppc2000pwt wbstable _cons reg corrupt2003 Trust GINI gdppc2000pwt wbstable,cluster(eastbloc) Regression with robust standard errors Number of obs = 58 F( 0, 1) =. Prob > F =. R-squared = Number of clusters (eastbloc) = 2 Root MSE = Robust corrupt2003 Coef. Std. Err. t P> t [95% Conf. Interval] Trust GINI gdppc2000pwt wbstable _cons
8 xtreg corrupt2003 Trust GINI gdppc2000pwt wbstable,i(eastbloc) re Random-effects GLS regression Number of obs = 58 Group variable (i): eastbloc Number of groups = 2 R-sq: within = Obs per group: min = 15 between = avg = 29.0 overall = max = 43 Random effects u_i ~ Gaussian Wald chi2(4) = corr(u_i, X) = 0 (assumed) Prob > chi2 = corrupt2003 Coef. Std. Err. z P> z [95% Conf. Interval] Trust GINI gdppc2000pwt wbstable _cons sigma_u 0 sigma_e rho 0 (fraction of variance due to u_i) xi: reg corrupt2003 Trust GINI gdppc2000pwt wbstable i.eastbloc i.eastbloc _Ieastbloc_0-1 (naturally coded; _Ieastbloc_0 omitted) Source SS df MS Number of obs = F( 5, 52) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = corrupt2003 Coef. Std. Err. t P> t [95% Conf. Interval] Trust GINI gdppc2000pwt wbstable _Ieastbloc_ _cons
9 reg trust10 trstprl stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl Source SS df MS Number of obs = F( 9, 22426) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = trust10 Coef. Std. Err. t P> t [95% Conf. Interval] trstprl stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl _cons diest,fb(6.3f) fse(6.3f) trust10 most people can be trusted or you can't be too careful Coef. Std. Err. t P> t trstprl trust in country's parliament stflife how satisfied with life as a whole stfeco how satisfied with present state of economy in country happy how happy are you sclact take part in social activities compared to others of same age yrbrn year of birth hinctnt household's total net income imgfrnd any immigrant friends edulvl highest level of education _cons
10 . reg trust10 trstprl stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl,robust Regression with robust standard errors Number of obs = F( 9, 22426) = Prob > F = R-squared = Root MSE = Robust trust10 Coef. Std. Err. t P> t [95% Conf. Interval] trstprl stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl _cons
11 reg trust10 trstprl stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl,cluster(country) Regression with robust standard errors Number of obs = F( 9, 14) = Prob > F = R-squared = Number of clusters (country) = 15 Root MSE = Robust trust10 Coef. Std. Err. t P> t [95% Conf. Interval] trstprl stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl _cons
12 . xi: reg trust10 trstprl stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl i.country i.country _Icountry_1-15 (naturally coded; _Icountry_1 omitted) Source SS df MS Number of obs = F( 23, 22412) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = trust10 Coef. Std. Err. t P> t [95% Conf. Interval] trstprl stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl _Icountry_ _Icountry_ _Icountry_ _Icountry_ _Icountry_ _Icountry_ _Icountry_ _Icountry_ _Icountry_ _Icountry_ _Icountry_ _Icountry_ _Icountry_ _Icountry_ _cons tabl country country (country) code freq Switzerland Czech Estonia Finland Britain Greece Hungary Ireland Israel Netherlands Norway Poland Portugal Sweden Slovenia Total 29516
13 xtreg trust10 trstprl stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl, i(country) re Random-effects GLS regression Number of obs = Group variable (i): country Number of groups = 15 R-sq: within = Obs per group: min = 874 between = avg = overall = max = 1985 Random effects u_i ~ Gaussian Wald chi2(9) = corr(u_i, X) = 0 (assumed) Prob > chi2 = trust10 Coef. Std. Err. z P> z [95% Conf. Interval] trstprl stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl _cons sigma_u sigma_e rho (fraction of variance due to u_i) R-sq: within R squared within groups (countries) between: R squared between groups (countries) sigma_u : variance of error terms within groups sigma_e: variance of error terms across groups
14 reg trust trstprl stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl Source SS df MS Number of obs = F( 9, 22426) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = trust Coef. Std. Err. t P> t [95% Conf. Interval] trstprl stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl _cons reg trust trstprl stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl,cluster(country) Regression with robust standard errors Number of obs = F( 9, 14) = Prob > F = R-squared = Number of clusters (country) = 15 Root MSE = Robust trust Coef. Std. Err. t P> t [95% Conf. Interval] trstprl stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl _cons
15 . predict trustregpred (option xb assumed; fitted values) (7038 missing values generated). sum trustregpred,det Fitted values Percentiles Smallest 1% % % Obs % Sum of Wgt % Mean Largest Std. Dev % % Variance % Skewness % Kurtosis sum trustregpred if (trustregpred > 1 trustregpred < 0) & trustregpred ~=. Variable Obs Mean Std. Dev. Min Max trustregpred
16 for var trstprl stflife stfeco happy sclact hinctnt imgfrnd edulvl: zz X trust -> zz trstprl trust trust in country's parliament Variable Obs Mean Std. Dev. Min Max min max diff > zz stflife trust how satisfied with life as a whole Variable Obs Mean Std. Dev. Min Max min max diff > zz stfeco trust how satisfied with present state of economy in country Variable Obs Mean Std. Dev. Min Max min max diff > zz happy trust how happy are you Variable Obs Mean Std. Dev. Min Max min max diff > zz sclact trust take part in social activities compared to others of same age Variable Obs Mean Std. Dev. Min Max min max diff > zz hinctnt trust household's total net income, all sources Variable Obs Mean Std. Dev. Min Max min max diff
17 -> zz imgfrnd trust any immigrant friends Variable Obs Mean Std. Dev. Min Max min max diff > zz edulvl trust highest level of education Variable Obs Mean Std. Dev. Min Max min max diff
18 . sum yrbrn,det year of birth Percentiles Smallest 1% % % Obs % Sum of Wgt % 1957 Mean Largest Std. Dev % % Variance % Skewness % Kurtosis z yrbrn 1925 trust 1984 Variable Obs Mean Std. Dev. Min Max min max diff
19 pf trust trstprl stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl Probit estimates Number of obs = LR chi2(9) = Prob > chi2 = Log likelihood = Pseudo R2 = trust Coef. Std. Err. z P> z [95% Conf. Interval] trstprl stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl _cons Goodness of fit measures for probit McKelvey-Zavoina R Square: Proportion Predicted Correctly (Model): Mean of Dependent Variable: Proportion Predicted Correctly (Null): Proportional Reduction in Error: diest,fb(6.3f) fse(6.3f) trust trust dummy Coef. Std. Err. z P> z trstprl trust in country's parliament stflife how satisfied with life stfeco how satisfied with present state of economy in country happy how happy are you sclact take part in social activities compared to others of same age yrbrn year of birth hinctnt household's total net income imgfrnd any immigrant friends edulvl highest level of education _cons
20 . for var trstprl stflife stfeco happy sclact hinctnt imgfrnd edulvl: zz X trust -> zz trstprl trust trust in country's parliament Variable Obs Mean Std. Dev. Min Max min max diff > zz stflife trust how satisfied with life as a whole Variable Obs Mean Std. Dev. Min Max min max diff > zz stfeco trust how satisfied with present state of economy in country Variable Obs Mean Std. Dev. Min Max min max diff > zz happy trust how happy are you Variable Obs Mean Std. Dev. Min Max min max diff > zz sclact trust take part in social activities compared to others of same age Variable Obs Mean Std. Dev. Min Max min max diff > zz hinctnt trust household's total net income, all sources Variable Obs Mean Std. Dev. Min Max min max diff
21
22 -> zz imgfrnd trust any immigrant friends Variable Obs Mean Std. Dev. Min Max min max diff > zz edulvl trust highest level of education Variable Obs Mean Std. Dev. Min Max min max diff z yrbrn 1925 trust 1984 Variable Obs Mean Std. Dev. Min Max min max diff
23 prchange,help probit: Changes in Predicted Probabilities for trust min->max 0->1 -+1/2 -+sd/2 MargEfct trstprl stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl Pr(y x) trstprl stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl x= sd(x)= Pr(y x): probability of observing each y for specified x values Avg Chg : average of absolute value of the change across categories Min->Max: change in predicted probability as x changes from its minimum to its maximum 0->1: change in predicted probability as x changes from 0 to 1 -+1/2: change in predicted probability as x changes from 1/2 unit below base value to 1/2 unit above -+sd/2: change in predicted probability as x changes from 1/2 standard dev below base to 1/2 standard dev above MargEfct: the partial derivative of the predicted probability/rate with respect to a given independent variable
24 . prchange,fromto probit: Changes in Predicted Probabilities for trust from: to: dif: from: to: dif: from: to: dif: from: to: dif: x=min x=max min->max x=0 x=1 0->1 x-1/2 x+1/2 -+1/2 x-1/2sd x+1/2sd -+sd/2 MargEfct trstprl stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl Pr(y x) trstprl stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl x= sd(x)=
25 prchange,x(trstprl=1 stflife=1) probit: Changes in Predicted Probabilities for trust min->max 0->1 -+1/2 -+sd/2 MargEfct trstprl stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl Pr(y x) trstprl stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl x= sd(x)=
26 prtab sclact imgfrnd probit: Predicted probabilities of positive outcome for trust take part in social activities compared to others of same any immigrant friends age yes, several yes, a few no, none at all much less than most less than most about the same more than most much more than most trstprl stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl x=
27 estsimp probit trust trstprl stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl,nolog dropsims Probit estimates Number of obs = LR chi2(9) = Prob > chi2 = Log likelihood = Pseudo R2 = trust Coef. Std. Err. z P> z [95% Conf. Interval] trstprl stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl _cons Simulating main parameters. Please wait... % of simulations completed: 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Number of simulations : 1000 Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10
28 . setx trstprl min (stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl ) mean. simqi Quantity of Interest Mean Std. Err. [95% Conf. Interval] Pr(trust=0) Pr(trust=1) setx trstprl max (stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl ) mean. simqi Quantity of Interest Mean Std. Err. [95% Conf. Interval] Pr(trust=0) Pr(trust=1) setx trstprl 5 (stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl ) mean. simqi Quantity of Interest Mean Std. Err. [95% Conf. Interval] Pr(trust=0) Pr(trust=1) setx trstprl 5 (stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl ) mean. simqi Quantity of Interest Mean Std. Err. [95% Conf. Interval] Pr(trust=0) Pr(trust=1)
29 . setx trstprl mean stflife min (stfeco happy sclact yrbrn hinctnt imgfrnd edulvl ) mean. simqi Quantity of Interest Mean Std. Err. [95% Conf. Interval] Pr(trust=0) Pr(trust=1) setx trstprl max stflife min (stfeco happy sclact yrbrn hinctnt imgfrnd edulvl ) mean. simqi Quantity of Interest Mean Std. Err. [95% Conf. Interval] Pr(trust=0) Pr(trust=1)
30 setx trstprl mean (stflife stfeco happy) min (sclact yrbrn hinctnt imgfrnd edulvl ) mean. simqi Quantity of Interest Mean Std. Err. [95% Conf. Interval] Pr(trust=0) Pr(trust=1) setx trstprl mean (stflife stfeco happy) max (sclact yrbrn hinctnt imgfrnd edulvl ) mean. simqi Quantity of Interest Mean Std. Err. [95% Conf. Interval] Pr(trust=0) Pr(trust=1)
31 pf trust trstprl stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl Probit estimates Number of obs = LR chi2(9) = Prob > chi2 = Log likelihood = Pseudo R2 = trust Coef. Std. Err. z P> z [95% Conf. Interval] trstprl stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl _cons Goodness of fit measures for probit McKelvey-Zavoina R Square: Proportion Predicted Correctly (Model): Mean of Dependent Variable: Proportion Predicted Correctly (Null): Proportional Reduction in Error: probit trust trstprl stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl,robust Iteration 0: log pseudolikelihood = Iteration 1: log pseudolikelihood = Iteration 2: log pseudolikelihood = Iteration 3: log pseudolikelihood = Probit estimates Number of obs = Wald chi2(9) = Prob > chi2 = Log pseudolikelihood = Pseudo R2 = Robust trust Coef. Std. Err. z P> z [95% Conf. Interval] trstprl stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl _cons
32 . probit trust trstprl stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl,cluster(country) Probit estimates Number of obs = Wald chi2(9) = Prob > chi2 = Log pseudolikelihood = Pseudo R2 = (standard errors adjusted for clustering on country) Robust trust Coef. Std. Err. z P> z [95% Conf. Interval] trstprl stflife stfeco happy sclact yrbrn hinctnt imgfrnd edulvl _cons
33 predict trustprobitpred (option p assumed; Pr(trust)) (7038 missing values generated). sum trustprobitres Variable Obs Mean Std. Dev. Min Max trustprobi~s predict trustlogitpred (option p assumed; Pr(trust)) (7038 missing values generated). sum trustlogitres Variable Obs Mean Std. Dev. Min Max trustlogit~s corr trustregpred trustprobitpred trustlogitpred (obs=22478) trustr~d trustp~d trustl~d trustregpred trustprobi~d trustlogit~d
34 sort trustregpred. clist trustregpred trustlogitpred trustprobitpred if (trustregpred > 1 trustregpred < 0) & trustregpred ~=. trustre~d trustlo~d trustpr~d
35
36
37
38
39
Guideline on evaluating the impact of policies -Quantitative approach-
Guideline on evaluating the impact of policies -Quantitative approach- 1 2 3 1 The term treatment derives from the medical sciences and has more meaning when is used in that context. However, this term
More informationBios 312 Midterm: Appendix of Results March 1, Race of mother: Coded as 0==black, 1==Asian, 2==White. . table race white
Appendix. Use these results to answer 2012 Midterm questions Dataset Description Data on 526 infants with very low (
More informationExample Analysis with STATA
Example Analysis with STATA Exploratory Data Analysis Means and Variance by Time and Group Correlation Individual Series Derived Variable Analysis Fitting a Line to Each Subject Summarizing Slopes by Group
More informationExample Analysis with STATA
Example Analysis with STATA Exploratory Data Analysis Means and Variance by Time and Group Correlation Individual Series Derived Variable Analysis Fitting a Line to Each Subject Summarizing Slopes by Group
More informationExperiment Outcome &Literature Review. Presented by Fang Liyu
Experiment Outcome &Literature Review Presented by Fang Liyu Experiment outcome 1. Data from JD Sample size: 1) Data contains 3325 products in 8 days 2) There are 2000-3000 missing values in each data
More information* STATA.OUTPUT -- Chapter 5
* STATA.OUTPUT -- Chapter 5.*bwt/confounder example.infile bwt smk gest using bwt.data.correlate (obs=754) bwt smk gest -------------+----- bwt 1.0000 smk -0.1381 1.0000 gest 0.3629 0.0000 1.0000.regress
More informationECON Introductory Econometrics Seminar 6
ECON4150 - Introductory Econometrics Seminar 6 Stock and Watson EE10.1 April 28, 2015 Stock and Watson EE10.1 ECON4150 - Introductory Econometrics Seminar 6 April 28, 2015 1 / 21 Guns data set Some U.S.
More informationMultilevel/ Mixed Effects Models: A Brief Overview
Multilevel/ Mixed Effects Models: A Brief Overview Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised March 27, 2018 These notes borrow very heavily, often/usually
More informationSOCY7706: Longitudinal Data Analysis Instructor: Natasha Sarkisian Two Wave Panel Data Analysis
SOCY7706: Longitudinal Data Analysis Instructor: Natasha Sarkisian Two Wave Panel Data Analysis In any longitudinal analysis, we can distinguish between analyzing trends vs individual change that is, model
More informationTrunkierte Regression: simulierte Daten
Trunkierte Regression: simulierte Daten * Datengenerierung set seed 26091952 set obs 48 obs was 0, now 48 gen age=_n+17 gen yhat=2000+200*(age-18) gen wage = yhat + 2000*invnorm(uniform()) replace wage=max(0,wage)
More information(LDA lecture 4/15/08: Transition model for binary data. -- TL)
(LDA lecture 4/5/08: Transition model for binary data -- TL) (updated 4/24/2008) log: G:\public_html\courses\LDA2008\Data\CTQ2log log type: text opened on: 5 Apr 2008, 2:27:54 *** read in data ******************************************************
More informationThis is a quick-and-dirty example for some syntax and output from pscore and psmatch2.
This is a quick-and-dirty example for some syntax and output from pscore and psmatch2. It is critical that when you run your own analyses, you generate your own syntax. Both of these procedures have very
More informationFoley Retreat Research Methods Workshop: Introduction to Hierarchical Modeling
Foley Retreat Research Methods Workshop: Introduction to Hierarchical Modeling Amber Barnato MD MPH MS University of Pittsburgh Scott Halpern MD PhD University of Pennsylvania Learning objectives 1. List
More informationApplication: Effects of Job Training Program (Data are the Dehejia and Wahba (1999) version of Lalonde (1986).)
Application: Effects of Job Training Program (Data are the Dehejia and Wahba (1999) version of Lalonde (1986).) There are two data sets; each as the same treatment group of 185 men. JTRAIN2 includes 260
More informationROBUST ESTIMATION OF STANDARD ERRORS
ROBUST ESTIMATION OF STANDARD ERRORS -- log: Z:\LDA\DataLDA\sitka_Lab8.log log type: text opened on: 18 Feb 2004, 11:29:17. ****The observed mean responses in each of the 4 chambers; for 1988 and 1989.
More informationİnsan Tunalı November 29, 2018 Econ 511: Econometrics I. ANSWERS TO ASSIGNMENT 10: Part II STATA Supplement
İnsan Tunalı November 29, 2018 Econ 511: Econometrics I STATA Exercise 1 ANSWERS TO ASSIGNMENT 10: Part II STATA Supplement TASK 1: --- name: log: g:\econ511\heter_housinglog log type: text opened
More informationNotes on PS2
17.871 - Notes on PS2 Mike Sances MIT April 2, 2012 Mike Sances (MIT) 17.871 - Notes on PS2 April 2, 2012 1 / 9 Interpreting Regression: Coecient regress success_rate dist Source SS df MS Number of obs
More information********************************************************************************************** *******************************
1 /* Workshop of impact evaluation MEASURE Evaluation-INSP, 2015*/ ********************************************************************************************** ******************************* DEMO: Propensity
More informationECONOMICS AND ECONOMIC METHODS PRELIM EXAM Statistics and Econometrics May 2011
ECONOMICS AND ECONOMIC METHODS PRELIM EXAM Statistics and Econometrics May 2011 Instructions: Answer all five (5) questions. Point totals for each question are given in parentheses. The parts within each
More informationThe Multivariate Regression Model
The Multivariate Regression Model Example Determinants of College GPA Sample of 4 Freshman Collect data on College GPA (4.0 scale) Look at importance of ACT Consider the following model CGPA ACT i 0 i
More informationSUGGESTED SOLUTIONS Winter Problem Set #1: The results are attached below.
450-2 Winter 2008 Problem Set #1: SUGGESTED SOLUTIONS The results are attached below. 1. The balanced panel contains larger firms (sales 120-130% bigger than the full sample on average), which are more
More informationCorrelated Random Effects Panel Data Models
NONLINEAR MODELS Correlated Random Effects Panel Data Models IZA Summer School in Labor Economics May 13-19, 2013 Jeffrey M. Wooldridge Michigan State University 1. Why Nonlinear Models? 2. CRE versus
More informationUnit 5 Logistic Regression Homework #7 Practice Problems. SOLUTIONS Stata version
Unit 5 Logistic Regression Homework #7 Practice Problems SOLUTIONS Stata version Before You Begin Download STATA data set illeetvilaine.dta from the course website page, ASSIGNMENTS (Homeworks and Exams)
More informationAppendix C: Lab Guide for Stata
Appendix C: Lab Guide for Stata 2011 1. The Lab Guide is divided into sections corresponding to class lectures. Each section includes both a review, which everyone should complete and an exercise, which
More informationSurvey commands in STATA
Survey commands in STATA Carlo Azzarri DECRG Sample survey: Albania 2005 LSMS 4 strata (Central, Coastal, Mountain, Tirana) 455 Primary Sampling Units (PSU) 8 HHs by PSU * 455 = 3,640 HHs svy command:
More informationTable. XTMIXED Procedure in STATA with Output Systolic Blood Pressure, use "k:mydirectory,
Table XTMIXED Procedure in STATA with Output Systolic Blood Pressure, 2001. use "k:mydirectory,. xtmixed sbp nage20 nage30 nage40 nage50 nage70 nage80 nage90 winter male dept2 edu_bachelor median_household_income
More informationEco311, Final Exam, Fall 2017 Prof. Bill Even. Your Name (Please print) Directions. Each question is worth 4 points unless indicated otherwise.
Your Name (Please print) Directions Each question is worth 4 points unless indicated otherwise. Place all answers in the space provided below or within each question. Round all numerical answers to the
More informationADVANCED ECONOMETRICS I
ADVANCED ECONOMETRICS I Practice Exercises (1/2) Instructor: Joaquim J. S. Ramalho E.mail: jjsro@iscte-iul.pt Personal Website: http://home.iscte-iul.pt/~jjsro Office: D5.10 Course Website: http://home.iscte-iul.pt/~jjsro/advancedeconometricsi.htm
More informationMilk Data Analysis. 1. Objective: analyzing protein milk data using STATA.
1. Objective: analyzing protein milk data using STATA. 2. Dataset: Protein milk data set (in the class website) Data description: Percentage protein content of milk samples at weekly intervals from each
More informationECONOMICS AND ECONOMIC METHODS PRELIM EXAM Statistics and Econometrics May 2014
ECONOMICS AND ECONOMIC METHODS PRELIM EXAM Statistics and Econometrics May 2014 Instructions: Answer all five (5) questions. Point totals for each question are given in parentheses. The parts within each
More informationNumber of obs = R-squared = Root MSE = Adj R-squared =
Appendix for the details of statistical test results Statistical Package used:stata/se 11.1 1. ANOVA result with dependent variable: current level of happiness, independent variables: sexs, ages, and survey
More informationInteractions made easy
Interactions made easy André Charlett Neville Q Verlander Health Protection Agency Centre for Infections Motivation Scientific staff within institute using Stata to fit many types of regression models
More informationUNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS
UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Exam: ECON4137 Applied Micro Econometrics Date of exam: Thursday, May 31, 2018 Grades are given: June 15, 2018 Time for exam: 09.00 to 12.00 The problem set covers
More informationCOMPARING MODEL ESTIMATES: THE LINEAR PROBABILITY MODEL AND LOGISTIC REGRESSION
PLS 802 Spring 2018 Professor Jacoby COMPARING MODEL ESTIMATES: THE LINEAR PROBABILITY MODEL AND LOGISTIC REGRESSION This handout shows the log of a STATA session that compares alternative estimates of
More informationSociology 7704: Regression Models for Categorical Data Instructor: Natasha Sarkisian. Preliminary Data Screening
r's age when 1st child born 2 4 6 Density.2.4.6.8 Density.5.1 Sociology 774: Regression Models for Categorical Data Instructor: Natasha Sarkisian Preliminary Data Screening A. Examining Univariate Normality
More informationApplied Econometrics
Applied Econometrics Lecture 3 Nathaniel Higgins ERS and JHU 20 September 2010 Outline of today s lecture Schedule and Due Dates Making OLS make sense Uncorrelated X s Correlated X s Omitted variable bias
More informationElementary tests. proc ttest; title3 'Two-sample t-test: Does consumption depend on Damper Type?'; class damper; var dampin dampout diff ;
Elementary tests /********************** heat2.sas *****************************/ title2 'Standard elementary tests'; options pagesize=35; %include 'heatread.sas'; /* Basically the data step from heat1.sas
More informationRead and Describe the SENIC Data
Read and Describe the SENIC Data If the data come in an Excel spreadsheet (very common), blanks are ideal for missing values. The spreadsheet must be.xls, not.xlsx. Beware of trying to read a.csv file
More informationExploring Functional Forms: NBA Shots. NBA Shots 2011: Success v. Distance. . bcuse nbashots11
NBA Shots 2011: Success v. Distance. bcuse nbashots11 Contains data from http://fmwww.bc.edu/ec-p/data/wooldridge/nbashots11.dta obs: 199,119 vars: 15 25 Oct 2012 09:08 size: 24,690,756 ------------- storage
More informationFlorida. Difference-in-Difference Models 8/23/2016
Florida Difference-in-Difference Models Bill Evans Health Economics 8/25/1997, State of Florida settles out of court in their suits against tobacco manufacturers Awarded $13 billion over 25 years Use $200m
More information(R) / / / / / / / / / / / / Statistics/Data Analysis
Series de Tiempo FE-UNAM Thursday September 20 14:47:14 2012 Page 1 (R) / / / / / / / / / / / / Statistics/Data Analysis User: Prof. Juan Francisco Islas{space -4} Project: UNIDAD II ----------- name:
More informationWeek 10: Heteroskedasticity
Week 10: Heteroskedasticity Marcelo Coca Perraillon University of Colorado Anschutz Medical Campus Health Services Research Methods I HSMP 7607 2017 c 2017 PERRAILLON ARR 1 Outline The problem of (conditional)
More informationCategorical Data Analysis
Categorical Data Analysis Hsueh-Sheng Wu Center for Family and Demographic Research October 4, 200 Outline What are categorical variables? When do we need categorical data analysis? Some methods for categorical
More informationTopics in Biostatistics Categorical Data Analysis and Logistic Regression, part 2. B. Rosner, 5/09/17
Topics in Biostatistics Categorical Data Analysis and Logistic Regression, part 2 B. Rosner, 5/09/17 1 Outline 1. Testing for effect modification in logistic regression analyses 2. Conditional logistic
More informationUsing Stata 11 & higher for Logistic Regression Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised March 28, 2015
Using Stata 11 & higher for Logistic Regression Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised March 28, 2015 NOTE: The routines spost13, lrdrop1, and extremes
More informationThe study obtains the following results: Homework #2 Basics of Logistic Regression Page 1. . version 13.1
Soc 73994, Homework #2: Basics of Logistic Regression Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised January 14, 2018 All answers should be typed and mailed to
More informationThe relationship between innovation and economic growth in emerging economies
Mladen Vuckovic The relationship between innovation and economic growth in emerging economies 130 - Organizational Response To Globally Driven Institutional Changes Abstract This paper will investigate
More informationUnit 2 Regression and Correlation 2 of 2 - Practice Problems SOLUTIONS Stata Users
Unit 2 Regression and Correlation 2 of 2 - Practice Problems SOLUTIONS Stata Users Data Set for this Assignment: Download from the course website: Stata Users: framingham_1000.dta Source: Levy (1999) National
More informationSoci Statistics for Sociologists
University of North Carolina Chapel Hill Soci708-001 Statistics for Sociologists Fall 2009 Professor François Nielsen Stata Commands for Module 11 Multiple Regression For further information on any command
More information17.871: PS3 Key. Part I
17.871: PS3 Key Part I. use "cces12.dta", clear. reg CC424 CC334A [aweight=v103] if CC334A!= 8 & CC424 < 6 // Need to remove values that do not fit on the linear scale. This entails discarding all respondents
More informationAll analysis examples presented can be done in Stata 10.1 and are included in this chapter s output.
Chapter 9 Stata v10.1 Analysis Examples Syntax and Output General Notes on Stata 10.1 Given that this tool is used throughout the ASDA textbook this chapter includes only the syntax and output for the
More informationComputer Handout Two
Computer Handout Two /******* senic2.sas ***********/ %include 'senicdef.sas'; /* Effectively, Copy the file senicdef.sas to here */ title2 'Elementary statistical tests'; proc freq; title3 'Use proc freq
More informationNever Smokers Exposure Case Control Yes No
Question 0.4 Never Smokers Exosure Case Control Yes 33 7 50 No 86 4 597 29 428 647 OR^ Never Smokers (33)(4)/(7)(86) 4.29 Past or Present Smokers Exosure Case Control Yes 7 4 2 No 52 3 65 69 7 86 OR^ Smokers
More informationThe Effect of Occupational Danger on Individuals Wage Rates. A fundamental problem confronting the implementation of many healthcare
The Effect of Occupational Danger on Individuals Wage Rates Jonathan Lee Econ 170-001 Spring 2003 PID: 703969503 A fundamental problem confronting the implementation of many healthcare policies is the
More informationESS Round 8 Sample Design Data File: User Guide
ESS Round 8 Sample Design Data File: User Guide Peter Lynn INSTITUTE FOR SOCIAL AND ECONOMIC RESEARCH, UNIVERSITY OF ESSEX 07 February 2019 v2 Contents Page Number 1. Introduction 1 2. Variables 2 2.1
More informationMidterm Exam. Friday the 29th of October, 2010
Midterm Exam Friday the 29th of October, 2010 Name: General Comments: This exam is closed book. However, you may use two pages, front and back, of notes and formulas. Write your answers on the exam sheets.
More informationPSC 508. Jim Battista. Dummies. Univ. at Buffalo, SUNY. Jim Battista PSC 508
PSC 508 Jim Battista Univ. at Buffalo, SUNY Dummies Dummy variables Sometimes we want to include categorical variables in our models Numerical variables that don t necessarily have any inherent order and
More informationlog: F:\stata_parthenope_01.smcl opened on: 17 Mar 2012, 18:21:56
log: F:\stata_parthenope_01.smcl opened on: 17 Mar 2012, 18:21:56 (20 cities >100k pop). de obs: 20 20 cities >100k pop vars: 13 size: 1,040 storage display value variable name type format label variable
More informationChapter 2 Part 1B. Measures of Location. September 4, 2008
Chapter 2 Part 1B Measures of Location September 4, 2008 Class will meet in the Auditorium except for Tuesday, October 21 when we meet in 102a. Skill set you should have by the time we complete Chapter
More informationYou can find the consultant s raw data here:
Problem Set 1 Econ 475 Spring 2014 Arik Levinson, Georgetown University 1 [Travel Cost] A US city with a vibrant tourist industry has an industrial accident (a spill ) The mayor wants to sue the company
More informationGroup Comparisons: Using What If Scenarios to Decompose Differences Across Groups
Group Comparisons: Using What If Scenarios to Decompose Differences Across Groups Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised February 15, 2015 We saw that the
More informationLecture 2a: Model building I
Epidemiology/Biostats VHM 812/802 Course Winter 2015, Atlantic Veterinary College, PEI Javier Sanchez Lecture 2a: Model building I Index Page Predictors (X variables)...2 Categorical predictors...2 Indicator
More informationAcaStat How To Guide. AcaStat. Software. Copyright 2016, AcaStat Software. All rights Reserved.
AcaStat How To Guide AcaStat Software Copyright 2016, AcaStat Software. All rights Reserved. http://www.acastat.com Table of Contents Frequencies... 3 List Variables... 4 Descriptives... 5 Explore Means...
More informationenergy usage summary (both house designs) Friday, June 15, :51:26 PM 1
energy usage summary (both house designs) Friday, June 15, 18 02:51:26 PM 1 The UNIVARIATE Procedure type = Basic Statistical Measures Location Variability Mean 13.87143 Std Deviation 2.36364 Median 13.70000
More informationIntroduction of STATA
Introduction of STATA News: There is an introductory course on STATA offered by CIS Description: Intro to STATA On Tue, Feb 13th from 4:00pm to 5:30pm in CIT 269 Seats left: 4 Windows, 7 Macintosh For
More informationCompartmental Pharmacokinetic Analysis. Dr Julie Simpson
Compartmental Pharmacokinetic Analysis Dr Julie Simpson Email: julieas@unimelb.edu.au BACKGROUND Describes how the drug concentration changes over time using physiological parameters. Gut compartment Absorption,
More informationECON Introductory Econometrics Seminar 9
ECON4150 - Introductory Econometrics Seminar 9 Stock and Watson EE13.1 May 4, 2015 Stock and Watson EE13.1 ECON4150 - Introductory Econometrics Seminar 9 May 4, 2015 1 / 18 Empirical exercise E13.1: Data
More informationMAPPING CITIES/REGIONS IN KNOWLEDGE SPACE DAVID RIGBY GEOGRAPHY & STATISTICS
MAPPING CITIES/REGIONS IN KNOWLEDGE SPACE DAVID RIGBY GEOGRAPHY & STATISTICS OUTLINE Motivation Building knowledge spaces Example of Europe Example of Norway Mapping firms/cities/regions in knowledge space
More informationStata v 12 Illustration. One Way Analysis of Variance
Stata v 12 Illustration Page 1. Preliminary Download anovaplot.. 2. Descriptives Graphs. 3. Descriptives Numerical 4. Assessment of Normality.. 5. Analysis of Variance Model Estimation.. 6. Tests of Equality
More informationModule 6 Case Studies in Longitudinal Data Analysis
Module 6 Case Studies in Longitudinal Data Analysis Benjamin French, PhD Radiation Effects Research Foundation SISCR 2018 July 24, 2018 Learning objectives This module will focus on the design of longitudinal
More informationFinal Exam Spring Bread-and-Butter Edition
Final Exam Spring 1996 Bread-and-Butter Edition An advantage of the general linear model approach or the neoclassical approach used in Judd & McClelland (1989) is the ability to generate and test complex
More informationModule 20 Case Studies in Longitudinal Data Analysis
Module 20 Case Studies in Longitudinal Data Analysis Benjamin French, PhD Radiation Effects Research Foundation University of Pennsylvania SISCR 2016 July 29, 2016 Learning objectives This module will
More informationPREDICTIVE MODEL OF TOTAL INCOME FROM SALARIES/WAGES IN THE CONTEXT OF PASAY CITY
Page22 PREDICTIVE MODEL OF TOTAL INCOME FROM SALARIES/WAGES IN THE CONTEXT OF PASAY CITY Wilson Cordova wilson.cordova@cksc.edu.ph Chiang Kai Shek College, Philippines Abstract There are varied sources
More informationPubHlth Introduction to Biostatistics. 1. Summarizing Data Illustration: STATA version 10 or 11. A Visit to Yellowstone National Park, USA
PubHlth 540 - Introduction to Biostatistics 1. Summarizing Data Illustration: Stata (version 10 or 11) A Visit to Yellowstone National Park, USA Source: Chatterjee, S; Handcock MS and Simonoff JS A Casebook
More informationWeek 11: Collinearity
Week 11: Collinearity Marcelo Coca Perraillon University of Colorado Anschutz Medical Campus Health Services Research Methods I HSMP 7607 2017 c 2017 PERRAILLON ARR 1 Outline Regression and holding other
More informationMeasuring Impact of Increase in High-Skilled Workers on the Livelihoods of Medium- and Low-Skilled Workers
Columbia University in the City of New York Measuring Impact of Increase in High-Skilled Workers on the Livelihoods of Medium- and Low-Skilled Workers May 7, 2015 Advisor: Prof. Lance Freeman Reader: Prof.
More informationNested or Hierarchical Structure School 1 School 2 School 3 School 4 Neighborhood1 xxx xx. students nested within schools within neighborhoods
Multilevel Cross-Classified and Multi-Membership Models Don Hedeker Division of Epidemiology & Biostatistics Institute for Health Research and Policy School of Public Health University of Illinois at Chicago
More informationMixed Mode Surveys in Business Research: A Natural Experiment. Dr Andrew Engeli March 14 th 2018
Mixed Mode Surveys in Business Research: A Natural Experiment Dr Andrew Engeli March 14 th 2018 Structure of todays presentation The general context The natural experiment Resources Conclusion Coverage
More informationrat cortex data: all 5 experiments Friday, June 15, :04:07 AM 1
rat cortex data: all 5 experiments Friday, June 15, 218 1:4:7 AM 1 Obs experiment stimulated notstimulated difference 1 1 689 657 32 2 1 656 623 33 3 1 668 652 16 4 1 66 654 6 5 1 679 658 21 6 1 663 646
More informationFORECASTING LABOUR PRODUCTIVITY IN THE EUROPEAN UNION MEMBER STATES: IS LABOUR PRODUCTIVITY CHANGING AS EXPECTED?
Interdisciplinary Description of Complex Systems 16(3-B), 504-523, 2018 FORECASTING LABOUR PRODUCTIVITY IN THE EUROPEAN UNION MEMBER STATES: IS LABOUR PRODUCTIVITY CHANGING AS EXPECTED? Berislav Žmuk*,
More information!! NOTE: SAS Institute Inc., SAS Campus Drive, Cary, NC USA ! NOTE: The SAS System used:!
1 The SAS System NOTE: Copyright (c) 2002-2010 by SAS Institute Inc., Cary, NC, USA. NOTE: SAS (r) Proprietary Software 9.3 (TS1M0) Licensed to UNIVERSITY OF TORONTO/COMPUTING & COMMUNICATIONS, Site 70072784.
More informationProblem Points Score USE YOUR TIME WISELY SHOW YOUR WORK TO RECEIVE PARTIAL CREDIT
STAT 512 EXAM I STAT 512 Name (7 pts) Problem Points Score 1 40 2 25 3 28 USE YOUR TIME WISELY SHOW YOUR WORK TO RECEIVE PARTIAL CREDIT WRITE LEGIBLY. ANYTHING UNREADABLE WILL NOT BE GRADED GOOD LUCK!!!!
More informationFull-time schooling, part-time schooling, and wages: returns and risks in Portugal
Full-time schooling, part-time schooling, and wages: returns and risks in Portugal Corrado Andini (PRESENTER), University of Madeira and CEEAplA Pedro Telhado Pereira, University of Madeira, CEEAplA, IZA
More informationLogistic Regression, Part III: Hypothesis Testing, Comparisons to OLS
Logistic Regression, Part III: Hypothesis Testing, Comparisons to OLS Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised February 22, 2015 This handout steals heavily
More informationCenter for Demography and Ecology
Center for Demography and Ecology University of Wisconsin-Madison A Comparative Evaluation of Selected Statistical Software for Computing Multinomial Models Nancy McDermott CDE Working Paper No. 95-01
More informationMultiple Imputation and Multiple Regression with SAS and IBM SPSS
Multiple Imputation and Multiple Regression with SAS and IBM SPSS See IntroQ Questionnaire for a description of the survey used to generate the data used here. *** Mult-Imput_M-Reg.sas ***; options pageno=min
More informationUnit 6: Simple Linear Regression Lecture 2: Outliers and inference
Unit 6: Simple Linear Regression Lecture 2: Outliers and inference Statistics 101 Thomas Leininger June 18, 2013 Types of outliers in linear regression Types of outliers How do(es) the outlier(s) influence
More informationComparison of Different Empirical Estimation Procedures
* Factors Influencing Inter-Modal Facility Location Decisions: Comparison of Different Empirical Estimation Procedures ABSTRACT Steven Peterson School of Economic Sciences Washington State University 103
More informationFailure to take the sampling scheme into account can lead to inaccurate point estimates and/or flawed estimates of the standard errors.
Analyzing Complex Survey Data: Some key issues to be aware of Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised January 20, 2018 Be sure to read the Stata Manual s
More informationDoes Economic Freedom Stimulate Human Development?
ERASMUS UNIVERSITY ROTTERDAM Does Economic Freedom Stimulate Human Development? An empirical study on how governments can affect the well- being of developing countries Sebastiaan Claessen and Niccolo
More informationDealing with missing data in practice: Methods, applications, and implications for HIV cohort studies
Dealing with missing data in practice: Methods, applications, and implications for HIV cohort studies Belen Alejos Ferreras Centro Nacional de Epidemiología Instituto de Salud Carlos III 19 de Octubre
More informationBiostatistics 208. Lecture 1: Overview & Linear Regression Intro.
Biostatistics 208 Lecture 1: Overview & Linear Regression Intro. Steve Shiboski Division of Biostatistics, UCSF January 8, 2019 1 Organization Office hours by appointment (Mission Hall 2540) E-mail to
More informationTiming Production Runs
Class 7 Categorical Factors with Two or More Levels 189 Timing Production Runs ProdTime.jmp An analysis has shown that the time required in minutes to complete a production run increases with the number
More informationLongitudinal Data Analysis, p.12
Biostatistics 140624 2011 EXAM STATA LOG ( NEEDED TO ANSWER EXAM QUESTIONS) Multiple Linear Regression, p2 Longitudinal Data Analysis, p12 Multiple Logistic Regression, p20 Ordered Logistic Regression,
More information3. The lab guide uses the data set cda_scireview3.dta. These data cannot be used to complete assignments.
Lab Guide Written by Trent Mize for ICPSRCDA14 [Last updated: 17 July 2017] 1. The Lab Guide is divided into sections corresponding to class lectures. Each section should be reviewed before starting the
More information. *increase the memory or there will problems. set memory 40m (40960k)
Exploratory Data Analysis on the Correlation Structure In longitudinal data analysis (and multi-level data analysis) we model two key components of the data: 1. Mean structure. Correlation structure (after
More information(February draft)
For an International NGO Background statistics, cross tabs, summaries, graphs, t-tests and regression analysis for Nepal response survey data (February 2017 - draft) Contents Confidence level/statistical
More informationFull-time schooling, part-time schooling, and wages: returns and risks in Portugal
Full-time schooling, part-time schooling, and wages: returns and risks in Portugal Corrado Andini, University of Madeira, CEEAplA and IZA Pedro Telhado Pereira, University of Madeira, CEEAplA, IZA and
More informationStata Program Notes Biostatistics: A Guide to Design, Analysis, and Discovery Second Edition Chapter 12: Analysis of Variance
Stata Program Notes Biostatistics: A Guide to Design, Analysis, and Discovery Second Edition Chapter 12: Analysis of Variance Program Note 12.1 - One-Way ANOVA and Multiple Comparisons The Stata command
More informationX. Mixed Effects Analysis of Variance
X. Mixed Effects Analysis of Variance Analysis of variance with multiple observations per patient These analyses are complicated by the fact that multiple observations on the same patient are correlated
More information