Slajd 1

January 17, 2018 | Author: Anonymous | Category: Math, Statistics And Probability, Statistics
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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

View more...

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