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Research Methods Lecture 5 Advanced STATA IAN WALKER Module Leader S2.109

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Presentation on theme: "Research Methods Lecture 5 Advanced STATA IAN WALKER Module Leader S2.109"— Presentation transcript:

1 Research Methods Lecture 5 Advanced STATA IAN WALKER Module Leader S2.109

2 Housekeeping announcement Stephen Nickell (MPC and LSE) –British Academy Keynes Lecture in Economics –"Practical Issues in UK Monetary Policy " –Wednesday 2nd November –Arts Centre Conference Room at 5.30pm –http://www2.warwick.ac.uk/fac/soc/economics/f orums/deptsems/keynes_lecture/http://www2.warwick.ac.uk/fac/soc/economics/f orums/deptsems/keynes_lecture/

3 Stat-Transfer Use STAT- TRANSFER to convert data. Click on Stat-transfer is “point and click”. Just tell it the file name and format and the format you want it in. Click “transfer”.

4 Stat Transfer options Useful options for creating a manageable dataset from a large one: –Keep or drop variables –Change variable format E.g. float to integer –Select observations E.g. “where (income + benefits)/famsize < 4500” Can be used for reading a large STATA dataset and writing a smaller one Avoids doing this in STATA itself

5 Practicising You can import some of Stata’s own demo files using the.sysuse command –E.g..sysuse auto Many datasets are available at specific websites –E.g. STATA’s own site has all the demo data used in the manual examplesown You can use the.webuse command to load the files directly into stata without copying locally.webuse auto /* gets the data from STATA’s own site */ Or.webuse set economics/pg/modules/rm/notes/auto.dta

6 More help You can search the whole of STATA’s online help using.search xxx Michigan’s web-based guide to STATA (for SA)web-based guide UCLA resources to help you learn and use STATA:UCLA resources –including movies and “web-books” Consult other user-written guides and tutorials –Chevalier1, Chevalier2; Princeton; Illinois; GruhnChevalier1Chevalier2PrincetonIllinoisGruhn ESDS’s “Stata for LFS”Stata for LFS Stata’s own resources for learning STATA –Stata website, journal, library, archiveStata websitejournallibraryarchive –http://www.stata.com/links/resources1.htmlhttp://www.stata.com/links/resources1.html

7 Web resources STATA is web-aware –E.g.. update /* updates from */www.stata.com Statalist is an listserv discussion groupStatalist The Stata Journal is a refereed journalThe Stata Journal –Replaces the old Stata Technical Bulletin (STB):STB SSC Boston College STATA ArchiveSTATA Archive –Extensive library of programs by Stata users –Files can be downloaded in Stata using. ssc Eg.ssc install outreg Installs the outreg ado file that makes tables pretty

8 Always (whatever the software) Use lowercase Open a log file Label your data Use the do file editor Organise your files –Separate directories for separate projects –Archive (zip) data, do and results files when your finished

9 Customising STATA profile.do runs automatically when STATA starts Edit it to include commands you want to invoke every time.set mem 200m.log using justincase.log, replace Define preferences for STATA’s look and feel –Click on Prefs in menu Colours, graph scheme, etc. Save window positioning

10 Regression models - I Linear regression and related models when the outcome variable is continuous –OLS, 2SLS, 3SLS, IV, quantile reg, Box-Cox … Binary outcome data –the outcome variable is 0 or 1(or y/n) probit, logit, nested logit...; Multiple outcome data –the outcome variable is 1, 2,..., conditional logit, ordered probit

11 Regression models - II Count data –the outcome variable is 0, 1, 2,..., occurrences Poisson regression, negative binomial Choice models –multinomial choice –A, B or C Multinomial logit, Random utility model, unordered probit, nested logit,...etc Selection models –Truncated, censored Tobit, Heckman selection models; linear regression or probit with selection

12 Regression models - III STATA supports several special data types. Once type is defined special commands work Time series –Estimate ARIMA, and ARCH models –Estimators for autocorrelation and heteroscedasticity –Estimate MA and other smoothers –Tests for auto, het, unit roots - h, d, LM, Q, ADF, P-P ….. –TS graphs sysuse tsline2, clear tsset day tsline calories, ttick(28nov dec2002, tpos(in)) ttext( Nov2002 “Thanks" dec2002 “Xmas"",orient(vert))

13 …gives

14 Special data types: survey Non-randomness induces OLS to be inefficient STATA can handle non-random survey data –see the “syv***” commands –Example (stratified sample of medical cases):. webuse nhanes2f, clear. svyset psuid [pweight=finalwgt], strata(stratid). svy: reg zinc age age2 weight female black orace rural. reg zinc age age2 weight female black orace rural

15

16 Special data types: duration Survival time data –See the “st***” commands.stset failtime /*sets the var that defines duration*/ Estimates a wide variety of models to explain duration –E.g. Weibull “hazard” model -

17 Weibull example …. twoway (function y =.5*x^(-.5), range(0 5) yvarlab("a=.5") ) ( function y = 1.5*x^(.5), range(0 5) yvarlab("a=1.5") ) ( function y = 1*x^(0), range(0 5) yvarlab("a=1") ) ( function y = 2*x, range(0 2) yvarlab("a=2") ), saving(weib1, replace) title("Weibull hazard: lambda=1, alpha varying") ytitle(hazard) xtitle(t) ST regression supports Weibull, Cox PH and other options. streg load bearings, distribution(weibull) After streg you can plot bthe estimated hazard with. stcurve, cumhaz STATA allows functions to be plotted by specifying the function:

18 gives…..

19 Special data types: Panel data STATA can handle “panel” data easily –see the “xt***” commands Common commands are.xtdes Describe pattern of xt data.xtsum Summarize xt data.xttab Tabulate xt data.xtline Line plots with xt data.xtreg Fixed and random effects

20 Panel data An xt dataset looks like this: pid yr_visit fev age sex height smokes xt*** commands need to know the variables that identify person and “wave”:. iis pid. tis yr_visit Or use the tsset command. tsset pid yr_visit, yearly

21 Panel regression Once STATA has been told how to read the data it can perform regressions quite quickly:. xtreg y x, fe. xtreg y x, re

22 Further advice See Stephen Jenkins’ excellent course on duration modelling in STATAStephen Jenkins’ excellent course See Steve Pudney’s excellent course on panel data modelling in STATASteve Pudney’s excellent course –Beware the dataset is 30mb+


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