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Teaching Microeconometrics using at Warsaw School of Economics

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

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Teaching Microeconometrics using at Warsaw School of Economics

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  1. Teaching Microeconometricsusing at Warsaw School of Economics Marcin Owczarczuk Monika Książek

  2. Agenda • What is microeconometrics • Microeconometrics – the lecture • How do we teach: • Ordinal outcome models • Count outcome models • Limited outcome models

  3. Microeconometrics • Microdata • Individuals • Households • Companies • Microeconometrics = econometrics for microdata • Fields of application: • Marketing • Finance • Social science

  4. 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

  5. Ordinal outcome models

  6. 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)

  7. OLOGIT, OPROBIT, GOLOGIT Significance testing: • Single variable • Variable set • Whole model

  8. Parallel regressions assumption testing • Wolfe & Gould • LR ologitvsgologit • Brant Assumption holds  standard model is OK

  9. Model quality assessment • Model fit • Predictive capacities predict prob1, outcome(1)

  10. Parameters interpretation • Compensating effect • Marginal effect • Odds ratio

  11. Count outcome models

  12. 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)

  13. Poisson regression

  14. Negative binomial regression(allows for overdispersion).... No overdispersion Poisson model is OK

  15. Zero inflated (Poisson) model (Poisson model) (Binary logit model: P(Y=0)) ZIP fits better than standard Poisson model

  16. Limited outcome models

  17. 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

  18. Tobit regression Target_d – amount given in last mailing (many zeros)

  19. Truncated regression Target_d – amount given in last mailing (no zero observations)

  20. 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

  21. Sample selection, two step Inverse Mills ratio

  22. Coming soon September 2010

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