Teaching Microeconometrics using at Warsaw School of Economics Marcin OwczarczukMonika 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? 1Living comfortably on present income 2Coping on present income 3Finding it difficult on present income 4Finding 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)

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 amount selekcja =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 September 2010