Teaching Microeconometricsusing

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 income2 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

• Brant • Wolfe & Gould

• 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

• 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)

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ensi

ty

0 2 4 6V344

Poisson regression

Negative binomial regression(allows for overdispersion)....

No overdispersion Poisson model is OK

Zero inflated (Poisson) model

ZIP fits better than standard Poisson model

(Binary logit model: P(Y=0))

(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

Srednia_odleglosc – average distance (in days) between gifts;

sredni_datek – average amountselekcja =1 if more than 6 gifts were given

Positive correlation – who gives more, gives less frequently

Significant correlation

Sample selection, two step

Inverse Mills ratio

Coming soon

Coming soonSeptember 2010

September 2010