Lec_21_Prof._Arkonac's_Slides(Ch_12.4_-_Ch13.2)_Fall_10

# Lec_21_Prof_Arkonac - Instrumental Variable Regression III Experiments(Fall 2010 Lecture 21 Seyhan Erden Arkonac PhD Problem Set 8 is due on

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Instrumental Variable Regression III, Experiments (Fall 2010) Lecture 21 Seyhan Erden Arkonac, PhD Problem Set 8 is due on Tuesday November 30th! 1

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2 Application to the Demand for Cigarettes (SW Section 12.4) Why are we interested in knowing the elasticity of demand for cigarettes? Theory of optimal taxation: optimal tax is inverse to elasticity: smaller deadweight loss if quantity is affected less. Externalities of smoking – role for government intervention to discourage smoking second-hand smoke (non-monetary) monetary externalities
3 Panel data set Annual cigarette consumption, average prices paid by end consumer (including tax), personal income 48 continental US states, 1985-1995 Estimation strategy Having panel data allows us to control for unobserved state- level characteristics that enter the demand for cigarettes, as long as they don’t vary over time But we still need to use IV estimation methods to handle the simultaneous causality bias that arises from the interaction of supply and demand.

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4 Fixed-effects model of cigarette demand ln( cigarettes it Q ) = i + 1 ln( cigarettes it P ) + 2 ln( Income it ) + u it i = 1,…,48, t = 1985, 1986,…,1995 i reflects unobserved omitted factors that vary across states but not over time, e.g. attitude towards smoking Still, corr(ln( cigarettes it P ), u it ) is plausibly nonzero because of supply/demand interactions Estimation strategy: Use panel data regression methods to eliminate i Use TSLS to handle simultaneous causality bias Use T = 2 with 1985 – 1995 changes (“changes” method) – look at long-term response, not short-term dynamics (short- v. long-run elasticities)
5 The “changes” method (when T =2) One way to model long-term effects is to consider 10-year changes, between 1985 and 1995 Rewrite the regression in “changes” form: ln( 1995 cigarettes i Q ) – ln( 1985 cigarettes i Q ) = 1 [ln( 1995 cigarettes i P ) – ln( 1985 cigarettes i P )] + 2 [ln( Income i 1995 ) – ln( Income i 1985 )] + ( u i 1995 u i 1985 ) Create “10-year change” variables, for example: 10-year change in log price = ln( P i 1995 ) – ln( P i 1985 ) Then estimate the demand elasticity by TSLS using 10-year changes in the instrumental variables

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6 STATA: Cigarette demand First create “10-year change” variables 10-year change in log price = ln( P it ) – ln( P it –10 ) = ln( P it / P it –10 ) . gen dlpackpc = log(packpc/packpc[_n-10]); _n-10 is the 10-yr lagged value . gen dlavgprs = log(avgprs/avgprs[_n-10]); . gen dlperinc = log(perinc/perinc[_n-10]); . gen drtaxs = rtaxs-rtaxs[_n-10]; . gen drtax = rtax-rtax[_n-10]; . gen drtaxso = rtaxso-rtaxso[_n-10];
7 Use TSLS to estimate the demand elasticity by using the “10-year changes” specification Y W X Z . ivreg dlpackpc dlperinc ( dlavgprs = drtaxso ) , r; IV (2SLS) regression with robust standard errors Number of obs = 48 F( 2, 45) = 12.31 Prob > F = 0.0001 R-squared = 0.5499 Root MSE = .09092 ------------------------------------------------------------------------------

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## This note was uploaded on 11/10/2011 for the course ECON 3142 taught by Professor Arkonac during the Spring '11 term at Columbia.

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Lec_21_Prof_Arkonac - Instrumental Variable Regression III Experiments(Fall 2010 Lecture 21 Seyhan Erden Arkonac PhD Problem Set 8 is due on

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