程序代写代做代考 GMM ECON6300/7320/8300 Advanced Microeconometrics Instrumental variables

ECON6300/7320/8300 Advanced Microeconometrics Instrumental variables
Christiern Rose 1University of Queensland
Practical 4 March 2019
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Introduction
􏰉 This class will review:
􏰉 Instrumental variables
􏰉 2SLS and GMM estimation
􏰉 Tests for endogeneity of regressors
􏰉 Tests for weak instruments
􏰉 Tests of overidentifying restrictions (instrument validity)
􏰉 We begin with a demonstration from Microeconometrics using STATA Chapter 6 looking at the effect of employer/union sponsored health insurance on drug expenditures
􏰉 We move on to a practical looking at the the returns to schooling
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Demonstration (1)
􏰉 We analyse data on health, retirement and private insurance
􏰉 The data is from the Medical Expenditure Panel Survey
􏰉 We explore the effect of employer/union sponsored health
insurance (hi_empunion) on (log) drug expenditure
(ldrugexp)
􏰉 We treat insurance as endogenous as it is a choice
variable. Those who expect high future medical expenses are more likely to take out insurance.
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Demonstration (2)
􏰉 We analyse data on health, retirement and private insurance
􏰉 We consider as instruments:
􏰉 The proportion of total income that comes from social
security (ssiratio)
􏰉 An indicator for low income status (lowincome)
􏰉 The size of the individual’s firm’s labour force (firmsz)
􏰉 Whether the firm operates in more than one location (multlc)
􏰉 As exogenous regressors we include number of chronic conditions (totchr), age, female, black/hispanic indicator (blhisp) and log income (linc)
􏰉 The data are mus06data.dta and the do file is mus06p1iv.do
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Practical (1)
􏰉 We use data from Kling (2001) : mus06klingdata.dta are wage data collected in 1976
􏰉 We want to estimate the impact of years of schooling (grade76) on wages (wage76)
􏰉 Other covariates in the wage equation could be black, south76, smsa76, reg2-reg9, smsa66, age76 agesq76. (Use desc)
􏰉 Years of schooling (grade76) is endogenous! Those with higher ability tend to have more schooling, and ability ought also to determine wages.
􏰉 Possible instruments could be proximity to a 4 year college (col4) or family education levels (e.g. daded momed)
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Practical (2)
􏰉 Load the data
􏰉 Describe, summarise and choose a baseline instrument(s)
􏰉 Estimate the 2SLS model and optimal two-step GMM. Assume heteroskedastic errors. Interpret your results
􏰉 Compare your results with the OLS estimator.
􏰉 Are your results sensitive to the choice of instrument(s)?
􏰉 Is there evidence of endogeneity of the regressors in your wage equation?
􏰉 Are your instruments weak?
􏰉 Are your instruments valid?
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