Teaching Microeconometrics using at Warsaw School of Economics

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Teaching Microeconometricsusing at Warsaw
School of Economics

• Marcin Owczarczuk Monika Ksiazek

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Agenda

• What is microeconometrics
• Microeconometrics the lecture
• How do we teach
• Ordinal outcome models
• Count outcome models
• Limited outcome models

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Microeconometrics

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

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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

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Ordinal outcome models
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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 households 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)

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OLOGIT, OPROBIT, GOLOGIT

• Significance testing
• Single variable
• Variable set
• Whole model

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Parallel regressions assumption testing

• Wolfe Gould
• LR ologit vs gologit

• Brant

Assumption holds ? standard model is OK
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Model quality assessment

• Model fit
• Predictive capacities

predict prob1, outcome(1)
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Parameters interpretation

• Compensating effect
• Marginal effect
• Odds ratio

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Count outcome models
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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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Poisson regression
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Negative binomial regression(allows for
overdispersion)....
No overdispersion ?Poisson model is OK
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Zero inflated (Poisson) model
(Poisson model)
(Binary logit model P(Y0))
ZIP fits better than standard Poisson model
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Limited outcome models
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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 donors neighbourhood

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Tobit regression
Target_d amount given in last mailing (many
zeros)
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Truncated regression
Target_d amount given in last mailing (no
zero observations)
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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
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Sample selection, two step
Inverse Mills ratio
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Coming soon September 2010