Teaching Microeconometrics
using
at Warsaw School of Economics
Marcin Owczarczuk
Monika Książek
Agenda
• What is microeconometrics
• Microeconometrics – the lecture
• How do we teach:
• Ordinal outcome models
• Count outcome models
• Limited outcome models
Microeconometrics
• Microdata
• Individuals
• Households
• Companies
• Microeconometrics
= econometrics for microdata
• Fields of application:
• Marketing
• Finance
• Social science
Microeconometrics – the lecture
•
•
•
•
•
•
15 lectures (2h each)
Theory + applications
Applications on publicly avaiable datasets
Calculations in STATA
Maximum likelihood
Binary, multinomial, ordinal, count, limited
dependent variables
• Cross-sectional data only
Ordinal outcome models
Data
• European Social Survey, vawe 3, Poland
• Ordinal dependent variable (ocdoch):
Which of the descriptions on this card comes closest to
how you feel about your household’s income nowadays?
1
Living comfortably on present income
2
Coping on present income
3
Finding it difficult on present income
4
Finding it very difficult on present income
• Independent variables:
• Continous AGE (wiek)
• Binary CHILDREN (dzieci)
• Nominal (3 categories) PROFESSION
(zawód: kierownicy, pracownicy)
OLOGIT, OPROBIT, GOLOGIT
Significance testing:
• Single variable
• Variable set
• Whole model
Parallel regressions assumption testing
• Wolfe & Gould
• Brant
• LR ologit vs gologit
Assumption holds 
standard model is OK
Model quality assessment
• Model fit
• Predictive capacities
predict prob1, outcome(1)
Parameters interpretation
• Compensating effect
• Marginal effect
• Odds ratio
Count outcome models
Data
.4
.2
0
Density
.6
.8
• CBOS survey: Living conditions of Polish people
– problems and strategy
• Dependent variable: number of small children
(up to 6 year old) in a young family (20-35 year old)
0
2
4
V344
6
Poisson regression
Negative binomial regression
(allows for overdispersion)....
No overdispersion Poisson model is OK
Zero inflated (Poisson) model
(Poisson model)
(Binary logit model: P(Y=0))
ZIP fits better than standard Poisson model
Limited outcome models
Data
• PVA (US not-for-profit organisation) which
rises funds by direct mailings
• Donors differ in amounts and frequencies
of gifts
• Explanatory variables
• history of previous mailings
• characteristics of the donor’s neighbourhood
Tobit regression
Target_d – amount given in last mailing (many zeros)
Truncated regression
Target_d – amount given in last mailing
(no zero observations)
Sample selection, maximum likelihood
Positive correlation –
who gives more,
gives less frequently
Significant
correlation
Srednia_odleglosc – average distance (in days) between gifts;
sredni_datek – average amount
selekcja =1 if more than 6 gifts were given
Sample selection, two step
Inverse Mills ratio