Multilevel Analysis (ver. 1.0)
|
|
- Ashlynn Atkinson
- 7 years ago
- Views:
Transcription
1 Multlevel Analyss (ver. 1.0) Oscar Torres-Reyna Data Consultant
2 Motvaton Use multlevel model whenever your data s grouped (or nested) n more than one category (for example, states, countres, etc). Multlevel models allow: Study effects that vary by entty (or groups) Estmate group level averages Some advantages: Regular regresson gnores the average varaton between enttes. Indvdual regresson may face sample problems and lack of generalzaton
3 Varaton between enttes use bysort school: egen y_meanmean(y) twoway scatter y school, msze(tny) connected y_mean school, connect(l) clwdth(thck) clcolor(black) mcolor(black) msymbol(none), yttle(y) y school Score y_mean 3
4 statsby nter_b[_cons] slope_b[x1], by(school) savng(ols, replace): regress y x1 sort school merge school usng ols Indvdual regressons (no-poolng approach) drop _merge gen yhat_ols nter + slope*x1 sort school x1 separate y, by(school) separate yhat_ols, by(school) twoway connected yhat_ols1-yhat_ols65 x1 lft y x1, clwdth(thck) clcolor(black) legend(off) yttle(y) y Readng test 4
5 Varyng-ntercept model (null). xtmxed y school:, mle nolog y j[ ] α + ε Mxed-effects ML regresson Number of obs 4059 Group var able: school Number of groups 65 Obs per group: mn avg 6.4 max 198 Mean of state level ntercepts Wald ch(0). Log lkelhood Prob > ch. y Coef. Std. Err. z P> z [95% Conf. Interval] _cons Standard devaton at the school level (level ) Random-effects Parameters Estmate Std. Err. [95% Conf. Interval] Standard devaton at the ndvdual level (level ) school: Identty sd(_cons) sd(resdual) LR test vs. lnear regresson: chbar(01) Prob > chbar Intraclass ( sgma _ u) correlaton ( sgma _ u) + ( sgma _ e) sd(_ cons) sd(_ cons) + sd( resdual) _ 0.17 Ho: Random-effects 0 If the nterclass correlaton (IC) approaches 0 then the groupng by countes (or enttes) are of no use (you may as well run a smple regresson). If the IC approaches 1 then there s no varance to explan at the ndvdual level, everybody s the same. An ntraclass correlaton tells you about the correlaton of the observatons (cases) wthn a cluster ( 5
6 Varyng-ntercept model (one level-1 predctor). xtmxed y x1 school:, mle nolog y α + βx + ε j[ ] Mxed-effects ML regresson Number of obs 4059 Group var able: school Number of groups 65 Obs per group: mn avg 6.4 max 198 Mean of state level ntercepts Standard devaton at the school level (level ) Standard devaton at the ndvdual level (level ) Wald ch(1) Log lkelhood Prob > ch x _cons Random-effects Parameters Estmate Std. Err. [95% Conf. Interval] school: Identty y Coef. Std. Err. z P> z [95% Conf. Interval] sd(_cons) sd(resdual) LR test vs. lnear regresson: chbar(01) Prob > chbar Intraclass ( sgma _ u) correlaton ( sgma _ u) + ( sgma _ e) sd(_ cons) sd(_ cons) + sd( resdual) _ 0.14 Ho: Random-effects 0 If the nterclass correlaton (IC) approaches 0 then the groupng by countes (or enttes) are of no use (you may as well run a smple regresson). If the IC approaches 1 then there s no varance to explan at the ndvdual level, everybody s the same. An ntraclass correlaton tells you about the correlaton of the observatons (cases) wthn a cluster ( 6
7 Varyng-ntercept, varyng-coeffcent model y α β x + ε j[ ] + j[ ]. xtmxed y x1 school: x1, mle nolog covarance(unstructure) Mxed-effects ML regresson Number of obs 4059 Group var able: school Number of groups 65 Obs per group: mn avg 6.4 max 198 Mean of state level ntercepts Wald ch(1) Log lkelhood Prob > ch y Coef. Std. Err. z P> z [95% Conf. Interval] x _cons Standard devaton at the school level (level ) Standard devaton at the ndvdual level (level ) Random-effects Parameters Estmate Std. Err. [95% Conf. Interval] school: Unstructured sd(x1) sd(_cons) corr(x1,_cons) sd(resdual) LR test vs. lnear regresson: ch(3) Prob > ch Note: LR test s conservatve and provded only for reference. Ho: Random-effects 0 Intraclass ( sgma _ u) correlaton ( sgma _ u) + ( sgma _ e) sd(_ cons) + sd( x1) sd(_ cons) + sd( x1) + sd( resdual) _
8 Varyng-slope model y α β x + ε + j[ ]. xtmxed y x1 _all: R.x1, mle nolog Mxed-effects ML regresson Number of obs 4059 Group var able: _all Number of groups 1 Obs per group: mn 4059 avg max 4059 Mean of state level ntercepts Wald ch(1) Log lkelhood Prob > ch y Coef. Std. Err. z P> z [95% Conf. Interval] Standard devaton at the school level (level ) x _cons Random-effects Parameters Estmate Std. Err. [95% Conf. Interval] _all: Identty sd(r.x1) Standard devaton at the ndvdual level (level ) sd(resdual) LR test vs. lnear regresson: chbar(01) 0.00 Prob > chbar
9 Postestmaton 9
10 Comparng models usng lkelhood-raton test Use the lkelhood-rato test (lrtest) to compare models ftted by maxmum lkelhood. Ths test compares the log lkelhood (shown n the output) of two models and tests whether they are sgnfcantly dfferent. /*Fttng random ntercepts and storng results*/ quetly xtmxed y x1 school:, mle nolog estmates store r /*Fttng random coeffcents and storng results*/ quetly xtmxed y x1 school: x1, mle nolog covarance(unstructure) estmates store rc /*Runnng the lkelhood-rato test to compare*/ lrtest r rc. lrtest r rc Lkelhood-rato test LR ch() (Assumpton: r nested n rc) Prob > ch Note: LR test s conservatve The null hypothess s that there s no sgnfcant dfference between the two models. If Prob>ch<0.05, then you may reject the null and conclude that there s a statstcally sgnfcant dfference between the models. In the example above we reject the null and conclude that the random coeffcents model provdes a better ft (t has the lowest log lkelhood) 10
11 Varyng-ntercept, varyng-coeffcent model: postestmaton. xtmxed y x1 school: x1, mle nolog covarance(unstructure) varance Mxed-effects ML regresson Number of obs 4059 Group var able: school Number of groups 65 Obs per group: mn avg 6.4 max 198 Mean of state level ntercepts Wald ch(1) Log lkelhood Prob > ch y Coef. Std. Err. z P> z [95% Conf. Interval] x _cons Standard devaton at the school level (level ) Standard devaton at the ndvdual level (level ) Random-effects Parameters Estmate Std. Err. [95% Conf. Interval] school: Unstructured var(x1) var(_cons) cov(x1,_cons) var(resdual) LR test vs. lnear regresson: ch(3) Prob > ch Note: LR test s conservatve and provded only for reference. ( sgma _ u) var(_ cons) + var( x1) Intraclass _ correlaton 0.14 ( sgma _ u) + ( sgma _ e) var(_ cons) + var( x1) + var( resdual)
12 Postestmaton: varance-covarance matrx. xtmxed y x1 school: x1, mle nolog covarance(unstructure) varance Random-effects Parameters Estmate Std. Err. [95% Conf. Interval] school: Unstructured var(x1) var(_cons) cov(x1,_cons) var(resdual) LR test vs. lnear regresson: ch(3) Prob > ch Note: LR test s conservatve and provded only for reference.. estat recovarance Random-effects covarance matrx for level school x1 _cons x _cons Varance-covarance matrx. estat recovarance, correlaton Random-effects correlaton matrx for level school x1 _cons x1 1 _cons The correlaton between the ntercept and x1 shows a close relatonshp between the average of y and x1. 1
13 Postestmaton: estmatng random effects (group-level errors) y x α j[ ] + β j[ ] + ε y α j[ ] + β j[ ] x + uα + uβ + j[ ] ε Fxed-effects Random-effects To estmate the random effects u, use the command predct wth the opton reffects, ths wll gve you the best lnear unbased predctons (BLUPs) of the random effects whch bascally show the amount of varaton for both the ntercept and the estmated beta coeffcent(s). After runnng xtmxed, type predct u*, reffects Two new varables are created u1 BLUP r.e. for school: x /* u β */ u BLUP r.e. for school: _cons --- /* u α */ 13
14 Postestmaton: estmatng random effects (group-level errors) y x1 y x1 + u α + uβ To explore some results type: Fxed-effects Random-effects bysort school: generate groups(_n1) /*_n1 selects the frst case of each group */ lst school u u1 f school<10 & groups. lst school u u1 f school<10 & groups school u u1 Here u and u1 are the group level errors for the ntercept and the slope respectvely. For the frst school the equaton would be: y x ( ) + ( ) x x1 14
15 Postestmaton: estmatng ntercept/slope y x ( ) + ( ) x x 1 To estmate ntercepts and slopes per school type : gen ntercept _b[_cons] + u gen slope _b[x1] + u1 lst school ntercept slope f school<10 & groups Compare the coeffcents for school 1 above. lst school ntercept slope f school<10 & groups school ntercept slope
16 Postestmaton: fttng values Usng ntercept and slope you can estmate yhat, type gen yhat ntercept + (slope*x1) Or, after xtmxed type: predct yhat_ft, ftted lst school yhat yhat_ft f school<10 & groups. lst school yhat yhat_ft f school<10 & groups school yhat yhat_ft
17 You can plot ndvdual regressons, type Postestmaton: ftted values (graph) twoway connected yhat_ft x1 f school<10, connect(l) Ftted values: xb + Zu Readng test 17
18 After xtmxed you can get the resduals by typng: Postestmaton: resduals predct resd, resduals predct resd_std, rstandard /* resduals/sd(resdual) */ A quck check for normalty n the resduals qnorm resd_std Standardzed resduals Inverse Normal 18
19 DSS Onlne Tranng Secton UCLA Resources Prnceton DSS Lbgudes Books/References Useful lnks / Recommended books / References Beyond Fxed Versus Random Effects : A framework for mprovng substantve and statstcal analyss of panel, tme-seres cross-sectonal, and multlevel data / Brandom Bartels Robust Standard Errors for Panel Regressons wth Cross-Sectonal Dependence / Danel Hoechle, An Introducton to Modern Econometrcs Usng Stata/ Chrstopher F. Baum, Stata Press, 006. Data analyss usng regresson and multlevel/herarchcal models / Andrew Gelman, Jennfer Hll. Cambrdge ; New York : Cambrdge Unversty Press, 007. Data Analyss Usng Stata/ Ulrch Kohler, Frauke Kreuter, nd ed., Stata Press, 009. Desgnng Socal Inqury: Scentfc Inference n Qualtatve Research / Gary Kng, Robert O. Keohane, Sdney Verba, Prnceton Unversty Press, Econometrc analyss / Wllam H. Greene. 6th ed., Upper Saddle Rver, N.J. : Prentce Hall, 008. Introducton to econometrcs / James H. Stock, Mark W. Watson. nd ed., Boston: Pearson Addson Wesley, 007. Statstcal Analyss: an nterdscplnary ntroducton to unvarate & multvarate methods / Sam Kachgan, New York : Radus Press, c1986 Statstcs wth Stata (updated for verson 9) / Lawrence Hamlton, Thomson Books/Cole, 006 Unfyng Poltcal Methodology: The Lkelhood Theory of Statstcal Inference / Gary Kng, Cambrdge Unversty Press,
Causal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting
Causal, Explanatory Forecastng Assumes cause-and-effect relatonshp between system nputs and ts output Forecastng wth Regresson Analyss Rchard S. Barr Inputs System Cause + Effect Relatonshp The job of
More informationCHAPTER 14 MORE ABOUT REGRESSION
CHAPTER 14 MORE ABOUT REGRESSION We learned n Chapter 5 that often a straght lne descrbes the pattern of a relatonshp between two quanttatve varables. For nstance, n Example 5.1 we explored the relatonshp
More informationPanel Data Analysis Fixed and Random Effects using Stata (v. 4.2)
Panel Data Analysis Fixed and Random Effects using Stata (v. 4.2) Oscar Torres-Reyna otorres@princeton.edu December 2007 http://dss.princeton.edu/training/ Intro Panel data (also known as longitudinal
More informationTHE METHOD OF LEAST SQUARES THE METHOD OF LEAST SQUARES
The goal: to measure (determne) an unknown quantty x (the value of a RV X) Realsaton: n results: y 1, y 2,..., y j,..., y n, (the measured values of Y 1, Y 2,..., Y j,..., Y n ) every result s encumbered
More informationPRACTICE 1: MUTUAL FUNDS EVALUATION USING MATLAB.
PRACTICE 1: MUTUAL FUNDS EVALUATION USING MATLAB. INDEX 1. Load data usng the Edtor wndow and m-fle 2. Learnng to save results from the Edtor wndow. 3. Computng the Sharpe Rato 4. Obtanng the Treynor Rato
More informationEconomic Interpretation of Regression. Theory and Applications
Economc Interpretaton of Regresson Theor and Applcatons Classcal and Baesan Econometrc Methods Applcaton of mathematcal statstcs to economc data for emprcal support Economc theor postulates a qualtatve
More informationRegression Models for a Binary Response Using EXCEL and JMP
SEMATECH 997 Statstcal Methods Symposum Austn Regresson Models for a Bnary Response Usng EXCEL and JMP Davd C. Trndade, Ph.D. STAT-TECH Consultng and Tranng n Appled Statstcs San Jose, CA Topcs Practcal
More informationInternational University of Japan Public Management & Policy Analysis Program
Internatonal Unversty of Japan Publc Management & Polcy Analyss Program Practcal Gudes To Panel Data Modelng: A Step by Step Analyss Usng Stata * Hun Myoung Park, Ph.D. kucc65@uj.ac.jp 1. Introducton.
More informationMULTIPLE REGRESSION EXAMPLE
MULTIPLE REGRESSION EXAMPLE For a sample of n = 166 college students, the following variables were measured: Y = height X 1 = mother s height ( momheight ) X 2 = father s height ( dadheight ) X 3 = 1 if
More informationSIMPLE LINEAR CORRELATION
SIMPLE LINEAR CORRELATION Smple lnear correlaton s a measure of the degree to whch two varables vary together, or a measure of the ntensty of the assocaton between two varables. Correlaton often s abused.
More informationCHAPTER 5 RELATIONSHIPS BETWEEN QUANTITATIVE VARIABLES
CHAPTER 5 RELATIONSHIPS BETWEEN QUANTITATIVE VARIABLES In ths chapter, we wll learn how to descrbe the relatonshp between two quanttatve varables. Remember (from Chapter 2) that the terms quanttatve varable
More informationLecture 14: Implementing CAPM
Lecture 14: Implementng CAPM Queston: So, how do I apply the CAPM? Current readng: Brealey and Myers, Chapter 9 Reader, Chapter 15 M. Spegel and R. Stanton, 2000 1 Key Results So Far All nvestors should
More informationFrom the help desk: Swamy s random-coefficients model
The Stata Journal (2003) 3, Number 3, pp. 302 308 From the help desk: Swamy s random-coefficients model Brian P. Poi Stata Corporation Abstract. This article discusses the Swamy (1970) random-coefficients
More informationHow To Calculate The Accountng Perod Of Nequalty
Inequalty and The Accountng Perod Quentn Wodon and Shlomo Ytzha World Ban and Hebrew Unversty September Abstract Income nequalty typcally declnes wth the length of tme taen nto account for measurement.
More informationVariance estimation for the instrumental variables approach to measurement error in generalized linear models
he Stata Journal (2003) 3, Number 4, pp. 342 350 Varance estmaton for the nstrumental varables approach to measurement error n generalzed lnear models James W. Hardn Arnold School of Publc Health Unversty
More informationRECENT DEVELOPMENTS IN QUANTITATIVE COMPARATIVE METHODOLOGY:
Federco Podestà RECENT DEVELOPMENTS IN QUANTITATIVE COMPARATIVE METHODOLOGY: THE CASE OF POOLED TIME SERIES CROSS-SECTION ANALYSIS DSS PAPERS SOC 3-02 INDICE 1. Advantages and Dsadvantages of Pooled Analyss...
More informationSTATISTICAL DATA ANALYSIS IN EXCEL
Mcroarray Center STATISTICAL DATA ANALYSIS IN EXCEL Lecture 6 Some Advanced Topcs Dr. Petr Nazarov 14-01-013 petr.nazarov@crp-sante.lu Statstcal data analyss n Ecel. 6. Some advanced topcs Correcton for
More informationMerge/Append using R (draft)
Merge/Append using R (draft) Oscar Torres-Reyna Data Consultant otorres@princeton.edu January, 2011 http://dss.princeton.edu/training/ Intro Merge adds variables to a dataset. This document will use merge
More informationLatent Class Regression. Statistics for Psychosocial Research II: Structural Models December 4 and 6, 2006
Latent Class Regresson Statstcs for Psychosocal Research II: Structural Models December 4 and 6, 2006 Latent Class Regresson (LCR) What s t and when do we use t? Recall the standard latent class model
More information1. Measuring association using correlation and regression
How to measure assocaton I: Correlaton. 1. Measurng assocaton usng correlaton and regresson We often would lke to know how one varable, such as a mother's weght, s related to another varable, such as a
More informationLab 5 Linear Regression with Within-subject Correlation. Goals: Data: Use the pig data which is in wide format:
Lab 5 Linear Regression with Within-subject Correlation Goals: Data: Fit linear regression models that account for within-subject correlation using Stata. Compare weighted least square, GEE, and random
More informationForecasting the Direction and Strength of Stock Market Movement
Forecastng the Drecton and Strength of Stock Market Movement Jngwe Chen Mng Chen Nan Ye cjngwe@stanford.edu mchen5@stanford.edu nanye@stanford.edu Abstract - Stock market s one of the most complcated systems
More informationLecture 15 Panel Data Models
Lecture 15 Panel Data Models Panel Data Sets A panel data set, or longtudnal data set, s one where there are repeated observatons on the same unts. The unts may be ndvduals, households, enterprses, countres,
More informationSample Size Calculation for Longitudinal Studies
Sample Size Calculation for Longitudinal Studies Phil Schumm Department of Health Studies University of Chicago August 23, 2004 (Supported by National Institute on Aging grant P01 AG18911-01A1) Introduction
More informationStatistical Methods to Develop Rating Models
Statstcal Methods to Develop Ratng Models [Evelyn Hayden and Danel Porath, Österrechsche Natonalbank and Unversty of Appled Scences at Manz] Source: The Basel II Rsk Parameters Estmaton, Valdaton, and
More informationDiagnostic Tests of Cross Section Independence for Nonlinear Panel Data Models
DISCUSSION PAPER SERIES IZA DP No. 2756 Dagnostc ests of Cross Secton Independence for Nonlnear Panel Data Models Cheng Hsao M. Hashem Pesaran Andreas Pck Aprl 2007 Forschungsnsttut zur Zukunft der Arbet
More informationNonlinear relationships Richard Williams, University of Notre Dame, http://www3.nd.edu/~rwilliam/ Last revised February 20, 2015
Nonlinear relationships Richard Williams, University of Notre Dame, http://www.nd.edu/~rwilliam/ Last revised February, 5 Sources: Berry & Feldman s Multiple Regression in Practice 985; Pindyck and Rubinfeld
More informationMeasures of Fit for Logistic Regression
ABSTRACT Paper 1485-014 SAS Global Forum Measures of Ft for Logstc Regresson Paul D. Allson, Statstcal Horzons LLC and the Unversty of Pennsylvana One of the most common questons about logstc regresson
More informationGRAVITY DATA VALIDATION AND OUTLIER DETECTION USING L 1 -NORM
GRAVITY DATA VALIDATION AND OUTLIER DETECTION USING L 1 -NORM BARRIOT Jean-Perre, SARRAILH Mchel BGI/CNES 18.av.E.Beln 31401 TOULOUSE Cedex 4 (France) Emal: jean-perre.barrot@cnes.fr 1/Introducton The
More informationOnline Appendix for Forecasting the Equity Risk Premium: The Role of Technical Indicators
Onlne Appendx for Forecastng the Equty Rsk Premum: The Role of Techncal Indcators Chrstopher J. Neely Federal Reserve Bank of St. Lous neely@stls.frb.org Davd E. Rapach Sant Lous Unversty rapachde@slu.edu
More informationCHOLESTEROL REFERENCE METHOD LABORATORY NETWORK. Sample Stability Protocol
CHOLESTEROL REFERENCE METHOD LABORATORY NETWORK Sample Stablty Protocol Background The Cholesterol Reference Method Laboratory Network (CRMLN) developed certfcaton protocols for total cholesterol, HDL
More informationAn Evaluation of the Extended Logistic, Simple Logistic, and Gompertz Models for Forecasting Short Lifecycle Products and Services
An Evaluaton of the Extended Logstc, Smple Logstc, and Gompertz Models for Forecastng Short Lfecycle Products and Servces Charles V. Trappey a,1, Hsn-yng Wu b a Professor (Management Scence), Natonal Chao
More informationMarginal Benefit Incidence Analysis Using a Single Cross-section of Data. Mohamed Ihsan Ajwad and Quentin Wodon 1. World Bank.
Margnal Beneft Incdence Analyss Usng a Sngle Cross-secton of Data Mohamed Ihsan Ajwad and uentn Wodon World Bank August 200 Abstract In a recent paper, Lanjouw and Ravallon proposed an attractve and smple
More informationPart 1: quick summary 5. Part 2: understanding the basics of ANOVA 8
Statstcs Rudolf N. Cardnal Graduate-level statstcs for psychology and neuroscence NOV n practce, and complex NOV desgns Verson of May 4 Part : quck summary 5. Overvew of ths document 5. Background knowledge
More informationLectures on: Panel data analysis for social scientists, given at the University of Bergen, October 2006
Lectures on: Panel data analyss for socal scentsts, gven at the Unversty of Bergen, October 2006 You may fnd these lecture notes a useful complement to those I wll use for EC968. They cover a wder range
More informationChapter XX More advanced approaches to the analysis of survey data. Gad Nathan Hebrew University Jerusalem, Israel. Abstract
Household Sample Surveys n Developng and Transton Countres Chapter More advanced approaches to the analyss of survey data Gad Nathan Hebrew Unversty Jerusalem, Israel Abstract In the present chapter, we
More informationCan Auto Liability Insurance Purchases Signal Risk Attitude?
Internatonal Journal of Busness and Economcs, 2011, Vol. 10, No. 2, 159-164 Can Auto Lablty Insurance Purchases Sgnal Rsk Atttude? Chu-Shu L Department of Internatonal Busness, Asa Unversty, Tawan Sheng-Chang
More informationPortfolio Loss Distribution
Portfolo Loss Dstrbuton Rsky assets n loan ortfolo hghly llqud assets hold-to-maturty n the bank s balance sheet Outstandngs The orton of the bank asset that has already been extended to borrowers. Commtment
More informationAlthough ordinary least-squares (OLS) regression
egresson through the Orgn Blackwell Oxford, TEST 0141-98X 003 5 31000 Orgnal Joseph Teachng G. UK Artcle Publshng Esenhauer through Statstcs the Ltd Trust Orgn 001 KEYWODS: Teachng; egresson; Analyss of
More informationEstimation of Dispersion Parameters in GLMs with and without Random Effects
Mathematcal Statstcs Stockholm Unversty Estmaton of Dsperson Parameters n GLMs wth and wthout Random Effects Meng Ruoyan Examensarbete 2004:5 Postal address: Mathematcal Statstcs Dept. of Mathematcs Stockholm
More information1 De nitions and Censoring
De ntons and Censorng. Survval Analyss We begn by consderng smple analyses but we wll lead up to and take a look at regresson on explanatory factors., as n lnear regresson part A. The mportant d erence
More informationDiscussion Section 4 ECON 139/239 2010 Summer Term II
Discussion Section 4 ECON 139/239 2010 Summer Term II 1. Let s use the CollegeDistance.csv data again. (a) An education advocacy group argues that, on average, a person s educational attainment would increase
More informationModeling Ordered Choices
Modelng Ordered Choces Wllam H. Greene 1 Davd A. Hensher 2 January, 2009 1 Department of Economcs, Stern School of Busness, New York Unversty, New York, NY 10012, wgreene@stern.nyu.edu 2 Insttute of Transport
More informationOnline Appendix Supplemental Material for Market Microstructure Invariance: Empirical Hypotheses
Onlne Appendx Supplemental Materal for Market Mcrostructure Invarance: Emprcal Hypotheses Albert S. Kyle Unversty of Maryland akyle@rhsmth.umd.edu Anna A. Obzhaeva New Economc School aobzhaeva@nes.ru Table
More informationxtmixed & denominator degrees of freedom: myth or magic
xtmixed & denominator degrees of freedom: myth or magic 2011 Chicago Stata Conference Phil Ender UCLA Statistical Consulting Group July 2011 Phil Ender xtmixed & denominator degrees of freedom: myth or
More informationStatistical algorithms in Review Manager 5
Statstcal algorthms n Reve Manager 5 Jonathan J Deeks and Julan PT Hggns on behalf of the Statstcal Methods Group of The Cochrane Collaboraton August 00 Data structure Consder a meta-analyss of k studes
More informationAnalysis of Premium Liabilities for Australian Lines of Business
Summary of Analyss of Premum Labltes for Australan Lnes of Busness Emly Tao Honours Research Paper, The Unversty of Melbourne Emly Tao Acknowledgements I am grateful to the Australan Prudental Regulaton
More informationPlease follow the directions once you locate the Stata software in your computer. Room 114 (Business Lab) has computers with Stata software
STATA Tutorial Professor Erdinç Please follow the directions once you locate the Stata software in your computer. Room 114 (Business Lab) has computers with Stata software 1.Wald Test Wald Test is used
More informationHandling missing data in Stata a whirlwind tour
Handling missing data in Stata a whirlwind tour 2012 Italian Stata Users Group Meeting Jonathan Bartlett www.missingdata.org.uk 20th September 2012 1/55 Outline The problem of missing data and a principled
More informationExhaustive Regression. An Exploration of Regression-Based Data Mining Techniques Using Super Computation
Exhaustve Regresson An Exploraton of Regresson-Based Data Mnng Technques Usng Super Computaton Antony Daves, Ph.D. Assocate Professor of Economcs Duquesne Unversty Pttsburgh, PA 58 Research Fellow The
More informationMarginal Returns to Education For Teachers
The Onlne Journal of New Horzons n Educaton Volume 4, Issue 3 MargnalReturnstoEducatonForTeachers RamleeIsmal,MarnahAwang ABSTRACT FacultyofManagementand Economcs UnverstPenddkanSultan Idrs ramlee@fpe.ups.edu.my
More informationEvaluating the generalizability of an RCT using electronic health records data
Evaluatng the generalzablty of an RCT usng electronc health records data 3 nterestng questons Is our RCT representatve? How can we generalze RCT results? Can we use EHR* data as a control group? *) Electronc
More informationDETERMINANTS OF CAPITAL ADEQUACY RATIO IN SELECTED BOSNIAN BANKS
DETERMINANTS OF CAPITAL ADEQUACY RATIO IN SELECTED BOSNIAN BANKS Nađa DRECA International University of Sarajevo nadja.dreca@students.ius.edu.ba Abstract The analysis of a data set of observation for 10
More informationMultinomial and Ordinal Logistic Regression
Multinomial and Ordinal Logistic Regression ME104: Linear Regression Analysis Kenneth Benoit August 22, 2012 Regression with categorical dependent variables When the dependent variable is categorical,
More informationUnderstanding the Impact of Marketing Actions in Traditional Channels on the Internet: Evidence from a Large Scale Field Experiment
A research and educaton ntatve at the MT Sloan School of Management Understandng the mpact of Marketng Actons n Tradtonal Channels on the nternet: Evdence from a Large Scale Feld Experment Paper 216 Erc
More informationPSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 12
14 The Ch-squared dstrbuton PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 1 If a normal varable X, havng mean µ and varance σ, s standardsed, the new varable Z has a mean 0 and varance 1. When ths standardsed
More informationL10: Linear discriminants analysis
L0: Lnear dscrmnants analyss Lnear dscrmnant analyss, two classes Lnear dscrmnant analyss, C classes LDA vs. PCA Lmtatons of LDA Varants of LDA Other dmensonalty reducton methods CSCE 666 Pattern Analyss
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, http://www3.nd.edu/~rwilliam/ Last revised January 24, 2015 Rather than repeat material that is
More informationECON 142 SKETCH OF SOLUTIONS FOR APPLIED EXERCISE #2
University of California, Berkeley Prof. Ken Chay Department of Economics Fall Semester, 005 ECON 14 SKETCH OF SOLUTIONS FOR APPLIED EXERCISE # Question 1: a. Below are the scatter plots of hourly wages
More informationGeneralized Linear Models for Traffic Annuity Claims, with Application to Claims Reserving
Mathematcal Statstcs Stockholm Unversty Generalzed Lnear Models for Traffc Annuty Clams, wth Applcaton to Clams Reservng Patrca Mera Benner Examensarbete 2010:2 Postal address: Mathematcal Statstcs Dept.
More informationCorrelation and Regression
Correlation and Regression Scatterplots Correlation Explanatory and response variables Simple linear regression General Principles of Data Analysis First plot the data, then add numerical summaries Look
More informationNonlinear Regression Functions. SW Ch 8 1/54/
Nonlinear Regression Functions SW Ch 8 1/54/ The TestScore STR relation looks linear (maybe) SW Ch 8 2/54/ But the TestScore Income relation looks nonlinear... SW Ch 8 3/54/ Nonlinear Regression General
More informationGeneral Iteration Algorithm for Classification Ratemaking
General Iteraton Algorthm for Classfcaton Ratemakng by Luyang Fu and Cheng-sheng eter Wu ABSTRACT In ths study, we propose a flexble and comprehensve teraton algorthm called general teraton algorthm (GIA)
More informationFuzzy Regression and the Term Structure of Interest Rates Revisited
Fuzzy Regresson and the Term Structure of Interest Rates Revsted Arnold F. Shapro Penn State Unversty Smeal College of Busness, Unversty Park, PA 68, USA Phone: -84-865-396, Fax: -84-865-684, E-mal: afs@psu.edu
More informationMeta-analysis in Psychological Research.
Internatonal Journal of Psychologcal Research, 010. Vol. 3. No. 1. ISSN mpresa (prnted 011-084 ISSN electrónca (electronc 011-079 Sánchez-Meca, J., Marín-Martínez, F., (010. Meta-analyss n Psychologcal
More informationModeling Loss Given Default in SAS/STAT
Paper 1593-014 Modelng Loss Gven Default n SAS/SA Xao Yao, he Unversty of Ednburgh Busness School, UK Jonathan Crook, he Unversty of Ednburgh Busness School, UK Galna Andreeva, he Unversty of Ednburgh
More informationNPAR TESTS. One-Sample Chi-Square Test. Cell Specification. Observed Frequencies 1O i 6. Expected Frequencies 1EXP i 6
PAR TESTS If a WEIGHT varable s specfed, t s used to replcate a case as many tmes as ndcated by the weght value rounded to the nearest nteger. If the workspace requrements are exceeded and samplng has
More informationMilk Data Analysis. 1. Objective Introduction to SAS PROC MIXED Analyzing protein milk data using STATA Refit protein milk data using PROC MIXED
1. Objective Introduction to SAS PROC MIXED Analyzing protein milk data using STATA Refit protein milk data using PROC MIXED 2. Introduction to SAS PROC MIXED The MIXED procedure provides you with flexibility
More informationWorld currency options market efficiency
Arful Hoque (Australa) World optons market effcency Abstract The World Currency Optons (WCO) maket began tradng n July 2007 on the Phladelpha Stock Exchange (PHLX) wth the new features. These optons are
More informationCS 2750 Machine Learning. Lecture 3. Density estimation. CS 2750 Machine Learning. Announcements
Lecture 3 Densty estmaton Mlos Hauskrecht mlos@cs.ptt.edu 5329 Sennott Square Next lecture: Matlab tutoral Announcements Rules for attendng the class: Regstered for credt Regstered for audt (only f there
More informationBrigid Mullany, Ph.D University of North Carolina, Charlotte
Evaluaton And Comparson Of The Dfferent Standards Used To Defne The Postonal Accuracy And Repeatablty Of Numercally Controlled Machnng Center Axes Brgd Mullany, Ph.D Unversty of North Carolna, Charlotte
More informationBilgi Ekonomisi ve Yönetimi Dergisi / 2013 Cilt: VIII Sayı: II
Blg Ekonoms ve Yönetm Dergs / 2013 Clt: VIII Sayı: II CO2 EMISSIONS, RENEWABLE ENERGY CONSUMPTION, POPULATION DENSITY AND ECONOMIC GROWTH IN G7 COUNTRIES Abstract Fatma Fehme AYDIN 1 Ths study ams nvestgatng
More informationFrom the help desk: hurdle models
The Stata Journal (2003) 3, Number 2, pp. 178 184 From the help desk: hurdle models Allen McDowell Stata Corporation Abstract. This article demonstrates that, although there is no command in Stata for
More informationQuick Stata Guide by Liz Foster
by Liz Foster Table of Contents Part 1: 1 describe 1 generate 1 regress 3 scatter 4 sort 5 summarize 5 table 6 tabulate 8 test 10 ttest 11 Part 2: Prefixes and Notes 14 by var: 14 capture 14 use of the
More informationCorrelated Random Effects Panel Data Models
INTRODUCTION AND LINEAR MODELS Correlated Random Effects Panel Data Models IZA Summer School in Labor Economics May 13-19, 2013 Jeffrey M. Wooldridge Michigan State University 1. Introduction 2. The Linear
More informationCompeting-risks regression
Competing-risks regression Roberto G. Gutierrez Director of Statistics StataCorp LP Stata Conference Boston 2010 R. Gutierrez (StataCorp) Competing-risks regression July 15-16, 2010 1 / 26 Outline 1. Overview
More informationThe Racial and Gender Interest Rate Gap. in Small Business Lending: Improved Estimates Using Matching Methods*
The Racal and Gender Interest Rate Gap n Small Busness Lendng: Improved Estmates Usng Matchng Methods* Yue Hu and Long Lu Department of Economcs Unversty of Texas at San Antono Jan Ondrch and John Ynger
More informationDoes a Threshold Inflation Rate Exist? Quantile Inferences for Inflation and Its Variability
Does a Threshold Inflaton Rate Exst? Inferences for Inflaton and Its Varablty WenShwo Fang Department of Economcs Feng Cha Unversty Tachung, TAIWAN Stephen M. Mller* Department of Economcs Unversty of
More informationCharacterization of Assembly. Variation Analysis Methods. A Thesis. Presented to the. Department of Mechanical Engineering. Brigham Young University
Characterzaton of Assembly Varaton Analyss Methods A Thess Presented to the Department of Mechancal Engneerng Brgham Young Unversty In Partal Fulfllment of the Requrements for the Degree Master of Scence
More informationForecasting Irregularly Spaced UHF Financial Data: Realized Volatility vs UHF-GARCH Models
Forecastng Irregularly Spaced UHF Fnancal Data: Realzed Volatlty vs UHF-GARCH Models Franços-Érc Raccot *, LRSP Département des scences admnstratves, UQO Raymond Théoret Département Stratége des affares,
More informationFrom the help desk: Bootstrapped standard errors
The Stata Journal (2003) 3, Number 1, pp. 71 80 From the help desk: Bootstrapped standard errors Weihua Guan Stata Corporation Abstract. Bootstrapping is a nonparametric approach for evaluating the distribution
More informationData Visualization by Pairwise Distortion Minimization
Communcatons n Statstcs, Theory and Methods 34 (6), 005 Data Vsualzaton by Parwse Dstorton Mnmzaton By Marc Sobel, and Longn Jan Lateck* Department of Statstcs and Department of Computer and Informaton
More informationThe leverage statistic, h, also called the hat-value, is available to identify cases which influence the regression model more than others.
Outliers Outliers are data points which lie outside the general linear pattern of which the midline is the regression line. A rule of thumb is that outliers are points whose standardized residual is greater
More informationFixed and Random Effects in Panel Data Using Structural Equations Models
Fxed and Random Effects n Panel Data Usng Structural Equatons Models Kenneth A. Bollen Jenne E. Brand PWP-CCPR-8-3 January 8 Calforna Center for Populaton Research On-Lne Workng Paper Seres FIXED AND RANDOM
More informationMEASURING OPERATION EFFICIENCY OF THAI HOTELS INDUSTRY: EVIDENCE FROM META-FRONTIER ANALYSIS. Abstract
Internatonal Conference On Appled Economcs ICOAE 2011 315 MEASURING OPERATION EFFICIENCY OF THAI HOTELS INDUSTRY: EVIDENCE FROM METAFRONTIER ANALYSIS PHANIN KHRUEATHAI 1, AKARAPONG UNTONG 2, MINGSARN KAOSAARD
More informationIAPRI Quantitative Analysis Capacity Building Series. Multiple regression analysis & interpreting results
IAPRI Quantitative Analysis Capacity Building Series Multiple regression analysis & interpreting results How important is R-squared? R-squared Published in Agricultural Economics 0.45 Best article of the
More informationA Practitioner's Guide to Generalized Linear Models
A Practtoner's Gude to Generalzed Lnear Models A CAS Study Note Duncan Anderson, FIA Sholom Feldblum, FCAS Claudne Modln, FCAS Dors Schrmacher, FCAS Ernesto Schrmacher, ASA Neeza Thand, FCAS Thrd Edton
More informationCalibration and Linear Regression Analysis: A Self-Guided Tutorial
Calbraton and Lnear Regresson Analyss: A Self-Guded Tutoral Part The Calbraton Curve, Correlaton Coeffcent and Confdence Lmts CHM314 Instrumental Analyss Department of Chemstry, Unversty of Toronto Dr.
More informationDepartment of Economics Session 2012/2013. EC352 Econometric Methods. Solutions to Exercises from Week 10 + 0.0077 (0.052)
Department of Economics Session 2012/2013 University of Essex Spring Term Dr Gordon Kemp EC352 Econometric Methods Solutions to Exercises from Week 10 1 Problem 13.7 This exercise refers back to Equation
More informationbenefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).
REVIEW OF RISK MANAGEMENT CONCEPTS LOSS DISTRIBUTIONS AND INSURANCE Loss and nsurance: When someone s subject to the rsk of ncurrng a fnancal loss, the loss s generally modeled usng a random varable or
More informationPrediction of Disability Frequencies in Life Insurance
Predcton of Dsablty Frequences n Lfe Insurance Bernhard Köng Fran Weber Maro V. Wüthrch October 28, 2011 Abstract For the predcton of dsablty frequences, not only the observed, but also the ncurred but
More informationControl Charts with Supplementary Runs Rules for Monitoring Bivariate Processes
Control Charts wth Supplementary Runs Rules for Montorng varate Processes Marcela. G. Machado *, ntono F.. Costa * * Producton Department, Sao Paulo State Unversty, Campus of Guaratnguetá, 56-4 Guaratnguetá,
More informationThe simple linear Regression Model
The smple lear Regresso Model Correlato coeffcet s o-parametrc ad just dcates that two varables are assocated wth oe aother, but t does ot gve a deas of the kd of relatoshp. Regresso models help vestgatg
More informationMarginal Effects for Continuous Variables Richard Williams, University of Notre Dame, http://www3.nd.edu/~rwilliam/ Last revised February 21, 2015
Marginal Effects for Continuous Variables Richard Williams, University of Notre Dame, http://www3.nd.edu/~rwilliam/ Last revised February 21, 2015 References: Long 1997, Long and Freese 2003 & 2006 & 2014,
More informationRisk-Adjusted Performance: A two-model Approach Application in Amman Stock Exchange
Internatonal Journal of Busness and Socal Scence Vol. 3 No. 7; Aprl 01 Rsk-Adjusted Performance: A two-model Approach Applcaton n Amman Stock Exchange Hussan Al Bekhet 1 Al Matar Abstract The purpose of
More information14.74 Lecture 5: Health (2)
14.74 Lecture 5: Health (2) Esther Duflo February 17, 2004 1 Possble Interventons Last tme we dscussed possble nterventons. Let s take one: provdng ron supplements to people, for example. From the data,
More informationWage inequality and returns to schooling in Europe: a semi-parametric approach using EU-SILC data
MPRA Munch Personal RePEc Archve Wage nequalty and returns to schoolng n Europe: a sem-parametrc approach usng EU-SILC data Marco Bagett and Sergo Sccchtano Unversty La Sapenza Rome, Mnstry of Economc
More informationMedia Mix Modeling vs. ANCOVA. An Analytical Debate
Meda M Modelng vs. ANCOVA An Analytcal Debate What s the best way to measure ncremental sales, or lft, generated from marketng nvestment dollars? 2 Measurng ROI From Promotonal Spend Where possble to mplement,
More informationRate-Based Daily Arrival Process Models with Application to Call Centers
Submtted to Operatons Research manuscrpt (Please, provde the manuscrpt number!) Authors are encouraged to submt new papers to INFORMS journals by means of a style fle template, whch ncludes the journal
More informationStandard errors of marginal effects in the heteroskedastic probit model
Standard errors of marginal effects in the heteroskedastic probit model Thomas Cornelißen Discussion Paper No. 320 August 2005 ISSN: 0949 9962 Abstract In non-linear regression models, such as the heteroskedastic
More information