A. Colin Cameron and Pravin K. Trivedi
MICROECONOMETRICS: Methods and Applications
Cambridge University Press, New York
May 2005

PROGRAMS AND OUTPUT AND DATA FOR ENTIRE BOOK

[CHAPTERS 1-25 COMPLETE. CHAPTERS 26-27 TO BE COMPLETED IN JUNE]

For a zipped file with all programs and data and output click here

For some explanation of programs click here

For details on the data sets click here

PROGRAMS: I. INTRODUCTION (chapters 1-3)

No programs.

PROGRAMS: II. CORE METHODS (chapters 4-10)

Section | Pages | Example | Program and Output | Data [* means generated]
4.5.3 | 84-5 | Robust Standard Errors for OLS, WLS and GLS | mma04p1wls.do mma04p1wls.txt | * mma04p1wls.asc
4.6.4 | 88-90 | Quantile and Median Regression | mma04p2qreg.do mma04p2qreg.txt | qreg0902.dta or qreg0902.asc
4.8.8 | 102-3 | Instrumental Variables Regression | mma04p3iv.do mma04p3iv.txt | * mma04p3iv.asc
4.9.6 | 110-2 | IV Application with Weak Instruments | mma04p4ivweak.do mma04p4ivweak.txt | DATA66.dat and DATA66.dct
5.9.2-3 | 159-63 | Exponential: MLE using ml command | mma05p1mle.do mma05p1mle.txt | * mma05data.asc
5.9.2-3 | 159-63 | Exponential: NLS using nl command | mma05p2nls.do mma05p2nls.txt | * mma05data.asc
5.9.2-3 | 159-63 | Exponential: NLS using ml command | mma05p3nlsbyml.do mma05p3nlsbyml.txt | * mma05data.asc
5.9.4 | 159-63 | Exponential: Computation of marginal effects | mma05p4margeffects.do mma05p4margeffects.txt | * mma05data.asc
6.5.4 | 198-9 | Nonlinear 2SLS: Using Limdep | mma06p1nl2sls.lim mma06p1nl2sls.out | * mma06p1nl2sls.asc
6.5.4 | 198-9 | Part of preceding using Stata | mma06p2twostage.do mma06p2twostage.txt | * mma06p1nl2sls.asc
7.4 | 241-3 | Likelihood-based Hypothesis Testts | mma07p1mltests.do mma07p1mltests.txt | * mma07p1mltests.asc
7.6.3 | 248-9 | Asymptotic Power of Wald Test | mma07p2power.do mma07p2power.txt | No data
7.7.1-5 | 250-4 | Monte Carlo Simulation of Wald Test | mma07p3montecarlo.do mma07p3montecarlo.txt | Data for many simulations not saved
7.8 | 254-6 | Bootstrap example | mma07p4boot.do mma07p4boot.txt | * mma07p4boot.asc
8.2.9 | 269-71 | Conditional moment tests example | mma08p1cmtests.do mma08p1cmtests.txt | * mma08p1cmtests.asc
8.5.5 | 283-4 | Nonnested models test example | mma08p2nonnested.do mma08p2nonnested.txt | * mma08p2nonnested.asc
8.7.3 | 290-1 | Model diagnostics example | mma08p3diagnostics.do mma08p3diagnostics.txt | * mma08p3diagnostics.asc
9.2 | 295-7 | Nonparametric density estimation and regression: appplication | mma09p1np.do mma09p1np.txt |
9.4-9.5 | 307-19 | Nonparametric regression: more | mma09p2npmore.do mma09p2npmore.txt | * mma09p2npmore.asc
9.3.3 | 299-300 | Kernel functions plotted | mma09p3kernels.do mma09p3kernels.txt | * mma09p3kernels.asc
10.2.5 | 338-9 | Gradient method example (Newton Raphson) | mma10p1gradient.do mma10p1gradient.txt | No data

PROGRAMS: III. Computationally-Intensive Methods (chapters 11-13)

Section | Pages | Example | Program and Output | Data
11.3 | 366-8 | Bootstrap example | mma11p1boot.do mma11p1boot.txt | * mma11p1boot.asc
12.3.3 | 391-2 | Integral Computation Example | mma12p1integration.do mma12p1integration.txt | No data
12.4.5, 12.5.6 | 397-7, 403-4 | Maximum Simulated Likelihood and Maximum Simulated Score Example | mma12p2mslmsm.do mma12p2mslmsm.txt | * mma12p2mslmsm.asc
12.8.2 | 412-3 | Illustration of Methods to Draw Random Variates | mma12p3draws.do mma12p3draws.txt | No data
13.2.2 | 424 | Bayes Theorem Illustration for Normal Distribution and Prior | mma13p1bayesthm.do mma13p1bayesthm.txt | No data
13.6 | 452-4 | MCMC Example: Gibbs Sampler for SUR | mma13p2bayesgibbs.sas mma13p2bayesgibbs.lst mma13p2bayesgibbs.log | Program generated

PROGRAMS: IV. Models for Cross-Section Data (chapters 14-20)

Section | Pages | Example | Program and Output | Data
14.2 | 464-5 | Logit and Probit Application (fishing mode) | mma14p1binary.do mma14p1binary.txt | Nldata.asc
14.7.5 | 486 | Maximum score estimator for binary outcome | mma14p2maxscore.lim mma14p2maxscore.out | mma14p1binary.asc
15.2.1-3 | 491-5 | Multinomial Logit and Conditional Logit Application (fishing mode) | mma15p1mnl.do mma15p1mnl.txt | Nldata.asc
15.6.3 | 511 | Nested Logit (or GEV) estimation | mma15p2gev.do mma15p2gev.txt | Nldata.asc
15.2.2 | 493-4 | Limdep multinomial logit | mma15p3mnl.lim mma15p3mnl.out | Nldata.asc
15.2.1-3 | 491-5 | Limdep and addon Nlogit for conditional and nested logit | mma15p4gev.lim mma15p4gev.out | mma15p4gev.asc
16.2.1 | 530-1, 565 | Classic Tobit MLE and CLAD | mma16p1tobit.do mma16p1tobit.txt | mma16p1tobit.asc
16.3.4 | 540 | Inverse Mills ratio plotted | mma16p2mills.do mma16p2mills.txt | No data
16.6 | 553-5 | Selection Model Application (medical expenditures) | mma16p3selection.do mma16p3selection.txt | randdata.dta or mma16p3selection.asc
17.2 17.5.1 | 574-5 581-3 | Nonparametric estimation (KM for NA) for survival data (strike duration) | mma17p1km.do mma17p1km.txt | strkdur.dta or strkdur.asc
17.5.1 | 581-2 | Nonparametric estimation (KM and NA) for survival data (artificial) | mma17p2kmextra.do mma17p2kmextra.txt | Data in program
17.6.1 | 584-6 | Weibull distribution functions plotted | mma17p3weib.do mma17p3weib.txt | No data
17.11 | 603-8 | Duration regression models (unemployment duration) | mma17p4duration.do mma17p4duration.txt | ema1996.dta or ema1996.asc
18.8 | 632-6 | Duration regression with unobserved heterogeneity (unemployment duration) | mma18p1heterogeneity.do mma18p1heterogeneity.txt | ema1996.dta or ema1996.asc
19.5 | 658-3 | Competing risks model (unemployment duration) | mma19p1comprisks.do mma19p1comprisks.txt | ema1996.dta or ema1996.asc
20.2 20.7 | 671-4 690 | Count regression (doctor contacts) | mma20p1count.do mma20p1count.txt | randdata.dta mma20p1count.asc

PROGRAMS: V. Models for Panel Data (chapters 21-23)

Section | Pages | Example | Program and Output | Data
21.3.1-3 | 708-13 | Linear Panel Fixed and Random Effects Application (hours and wages) | mma21p1panfeandre.do mma21p1panfeandre.txt | MOM.dat
21.3.2 21.3.4 | 710 719 | Linear Panel Estimators manually obtained by OLS on transformed equation (hours and wages) | mma21p2panmanual.do mma21p2panmanual.txt | MOM.dat
21.3.4 | 713-5 | Linear Panel Residual Analysis (hours and wages) | mma21p3panresiduals.do mma21p3panresiduals.txt | MOM.dat
21.5.5 | 725 | Linear Panel pooled OLS and GLS estimation (hours and wages) | mma21p4pangls.do mma21p4pangls.txt | MOM.dat
22.3 | 754-6 | Linear Panel GMM Application (hours and wages) | mma22p1gmmpanel.do mma22p1gmmpanel.txt | MOMprecise.dat
23.3 | 792-5 | Nonlinear Panel Application (patents and R&D) | mma23p1pannonlin.do mma23p1pannonlin.txt | patr7079.asc

PROGRAMS: VI. Further Methods (chapters 24-27)

Section | Pages | Example | Program and Output | Data
24.7 | 848-53 | Clustered Linear Regression (household medical expenditure clustered on commune) | mma24p1olscluster.do mma24p1olscluster.txt | vietnam_ex1.dta or vietnam_ex1.asc
|  | Clustered Poisson Regression (individual pharmacy visits clustered on commune) | mma24p2poiscluster.do mma24p2poiscluster.txt | vietnam_ex2.dta or vietnam_ex2.asc
25.8.1-4 | 889-93 | Treatment Evaluation: Simple calculations (training on earnings) | mma25p1treatment.do mma25p1treatment.txt | nswpsid.da1 or nswpsid.dta
25.8.5 | 893-6 | Treatment Evaluation: Propensity score matching (training on earnings): | mma25p2matching.do mma25p2matching.txt | nswpsid.da1 or nswpsid.dta
25.8 | 889-96 | Treatment Evaluation: Additional analysis not in book using additional data sets (NSW experimental controls and CPS controls) | mma25p3extra.do mma25p3extra.txt | nswre74_treated.dta and nswre74_control.dta or nswre74_all.asc propensity_cps.dta or propensity_cps.asc
26.5 | 919-20 | Measurement Error Bias Example | To come | Generated data
27.8 | 935-9 | Missing Data MCMC Imputation Example | To come | Generated data

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