CLICK HERE to download a
zipped file with all the data files,
programs and output listed below.
DATA: We thank the authors of the papers listed in the table below for making their data available for empirical illustrations.
STATA: Stata was used
for most of the book and the programs reproduce virtually all of
Some Stata programs require user-written addons:
fmm for finite mixture models (chapter 4, 6, 11)
countfit for predicted probabilities (chapter 3, 5, 7)
mtreatreg for multiple treatment models (Chapter 10).
rcal, simex, qvf and cme for measurement error (Chapter 13).
R: R was used for the
nonparametric and semiparametric analysis of chapter 11.
A fairly detailed R program is also given for chapter 3 (basic Poisson and negative binomial regression).
Short R programs to read in data and estimate by Poisson are given for the other chapters.
These usually do not give exactly the same numerical results as the Stata code. e.g. robust standard errors are calculated differently.
The R programs use packages
foreign to read in a Stata dataset
sandwich for robust sandwich standard errors
boot for bootstrap standard errors
MASS for negative binomial (NB2) regression
gamlss for negative binomial (NB1 and NB2) regression
pscl for predicted probabilities from Poisson and NB2 regression
flexmix for finite mixtures of Poisson
np for nonparametric and semiparametric estimation
|See chapters 3 and 6
||Model Specification and
||Basic Count Regression
|A.C. Cameron and P.K. Trivedi (1986),
"Econometric Models Based on
Count Data: Comparisons and Applications of Some Estimators and Tests,"
Journal of Applied Econometrics, 1, 29-54.
||Generalized Count Regression
|See chapter 9
||Model Evaluation and Testing
|Sanjiv Jaggia and Satish Thosar (1993),
"Multiple Bids as a Consequence of Target Management
Resistance," Review of Quantitative Finance and Accounting,
||Introduction: 4 different counts||racd06p0.do
|See chapter 6.3, 6.4, 6.5 for
||See 6.3, 6.4, 6.5|
||Illustration: Health Services
|P. Deb and P.K. Trivedi (1997), "Demand for
Medical Care by the Elderly: A Finite Mixture Approach,"
Journal of Applied Econometrics, 12, 313-326.
||Illustration: Recreational Trips
|C. Sellar, J.R. Stoll and J.P. Chavas (1985),
"Validation of Empirical Measures of Welfare Change: A
Comparison of nonmarket Techniques," Land Economics, 61,
||No dataset - the data are
||Time Series Data
|J. Kennan, "The Duration of Contract strikes
in U.S. Manufacturing," Journal of Econometrics, 1985, 28,
R.C. Jung, R. Liesenfeld and J.-F. Richard (2011), "Dynamic Factor Models for Multivariate Count Data: An Application to Stock-Market Trading Activity," Journal of Business and Economic Statistics, 29, 73-85.
||See chapter 6.3
|B.H. Hall, Z. Griliches and J.A. Hausman
(1986), "Patents and R&D: Is There a Lag?",
International Economic Review, 27, 265-283.
||Nonrandom Samples and
|P. Deb and P.K. Trivedi (2006),
"Specification and simulated likelihood estimation of a
nonnormal treatment-outcome model with selection:
application to health care utilization," Econometrics
Journal, 9, 307-331.
||Flexible Methods For Counts
|See chapter 9.
|No dataset - the data are generated.
|No dataset - the data are generated.|
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