Advertisement
1 / 19

Estimation taking account of sample selection with Stata


Estimation taking account of sample selection with Stata . Cheti Nicoletti ISER, University of Essex 2009. Estimation commands : truncreg , tobit, heckman, heckprobit , treatreg, ivreg Other useful commands: ivprobit, ivtobit Useful option in the estimation commands : pweights.

Presentation posted in : General

Download Presentation

Estimation taking account of sample selection with Stata

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other sites. SlideServe reserves the right to change this policy at anytime.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.











- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -




Presentation Transcript


Estimation taking account of sample selection with stata l.jpg

Estimation taking account of sample selection with Stata

Cheti Nicoletti

ISER, University of Essex

2009


Slide2 l.jpg

  • Estimation commands:

    truncreg, tobit,

    heckman,heckprobit,

    treatreg, ivreg

  • Other useful commands:

    ivprobit, ivtobit

  • Useful option in the estimation commands:

    pweights


Truncreg l.jpg

truncreg

  • The truncreg command is useful to estimate regression models with a truncated sample

  • Ex: Health insurance claims observed only when amount claimed is higher than a fixed threshold.

    truncreg y x1 x1 x2 … xk , ll(c)


Tobit l.jpg

tobit

  • The tobit command is useful to estimate regression models with a censored dependent variable (deterministic censure)

  • 3 Different types of models:

    • Tobit with fixed censoring value (tobit)

    • Censored regression with varying censoring value (cnreg)

    • Regression with interval data (intreg)


Tobit5 l.jpg

tobit

  • Tobit first type (consumption of a good)

    tobit y x1 x2 … xk , ll(0)

    tobit y x1 x2 … xk , ul(c)


Cnreg l.jpg

cnreg

  • Tobit first type

    Ex. minimum wage with different levels in different years

  • cnreg y x1 x2 … xk censored(d)


Intreg l.jpg

intreg

  • Interval data regression (Ex:Bracket information on income for people refusing to give the exact value)

  • Whet yi* is not declared we observe the range to which yi* belong

    (0, 5000], (5000,15000], (15000,30000], (30000,+∞] say (ai, bi]


Estimating the regression with interval data in stata l.jpg

Estimating the regression with interval data in Stata

The command intreg needs two variables to define the dependent variable, say y1 and y2

intreg y1 y2 x1 x2 … xk


Heckman l.jpg

heckman

  • The heckman command is used to estimate Generalized Tobit or Tobit of the 2nd type using ML estimation (default option) or the two-step estimation (option [twostep])

    heckman yx1 x2 … xk, select(z1 z2 … zs)

    heckman yx1 x2 … xk, select(d = z1 z2 … zs)

    heckman yx1 x2 … xk, select(z1 z2 … zs) twostep


Heckprobit l.jpg

heckprobit

  • The heckman command is used to estimate a probit model with selection (option twostep does not exist because inconsistent)

    heckprobit px1 x2 … xk, select(z1 z2 … zs)


Impact of an endogenous dummy homogenous treatment effect l.jpg

Impact of an endogenous dummy Homogenous treatment effect

y1= earnings for trained people

y0= earnings for non-trained people

d dummy indicating participation to the training program

y=y1 d+y0 (1-d)

y=x+  d+

d*=z +u where d=l(d*>0)

We have a selection problem because of the correlation

between u and . This implies that d is not independent of .


Treatreg l.jpg

treatreg

  • The treatreg command is used to evaluate the effect of a endogenous binary variables (treatment, program, …) on a continuous variable of interest (see previous slide).

    treatreg yx1 x2 … xk , treat(d=z1 z2 … zs)

  • Ex: Sample of graduated students with and without a master degree

  • y=log earnings, d=1 if master degree, 0 otherwise

  • x = age, age square, d, sex, type first degree

  • z = mother’s level of education, father’s level of education, sex, type first degree


How to use weights in stata l.jpg

How to use weights in Stata

  • Most Stata commands can deal with weighted data. Stata allows four kinds of weights:

    • fweights, or frequency weights, are weights that indicate the number of duplicated observations.

    • pweights, or sampling weights, are weights that denote the inverse of the probability that the observation is included due to the sampling design and or nonresponse.

    • aweights, or analytic weights, are weights that are inversely proportional to the variance of an observation; i.e., the variance of the j-th observation is assumed to be sigma^2/w_j, where w_j are the weights.

    • iweights, or importance weights, are weights that indicate the "importance" of the observation in some vague sense.


Option pweights l.jpg

Option pweights

  • Usually sample surveys provide weights to take account of sampling design and nonresponse.

  • Let p be individual weight

  • Then we can run a regression with weighted observations

    regress y x1 x2 … xk [pweight=p]

  • Let us assume to have a sample with a sample selection problem (due to observables), then we can use propensity score weighting

  • A possible “simplified” way to estimate your own weights is described in the following:

    probit d z1 z2 … zs

    predict prop

    gen invprop=1/prop

    reg y x1 x2 … xk [pweight=invprop]


For complex survey design it is better to use l.jpg

For complex survey design it is better to use

  • svyset [pweight=p]

  • svy: regress y x1 x2 … xk

  • svyset have options for cluster sampling designs or other complex design

  • Declare survey design for dataset

  • svyset [pweight=p], strata(stratid)


Ivreg l.jpg

ivreg

  • The ivreg command is used to estimate regression model by using instrumental variables for potential endogenous explanatory variables.

  • Evaluation of the impact of years of schooling on earnings

    y=x+  d*+

    Problem: d* and  are correlated

    Solution 1: IV estimation ( IV=z: parental interest in the child education, bad financial shock of the family when the child is age 11-16, presence of older siblings, Blundell et al 2003)

    ivreg y x1 x1 x2 … xk (d*=z1 z2 … zs)


Stata program for evaluation l.jpg

STATA program for evaluation

Abadie A., Drukker D., Herr J.L., Imbens G.W. (2001), Implementing Matching Estimators for Average Treatment Effects in Stata, The Stata Journal, 1, 1-18 http://ksghome.harvard.edu/~.aabadie.academic.ksg/software.html

Becker S.O., Ichino A. (2002), Estimation of average treatment effects based on propensity scores. The Stata Journal, 2, 358-377http://www.lrz-muenchen.de/~sobecker/pscore.html

Sianesi B. (2001), Implementing Propensity Score Matching Estimators with STATA, UK Stata Users Group, VII Meeting London, http://ideas.repec.org/c/boc/bocode/s432001.html


Text book references l.jpg

Text Book References:

  • Amemiya T. (1985), Advanced Econometrics, Basil Blackwell, Oxford.

  • Gourieroux C. (2000),  Econometrics of Qualitative Dependent Variables, Cambridge University Press, Cambridge.

  • Greene W.H. (2000), Econometric Analysis, Third edition, Prentice-hall, London.

  • Maddala G. S. (1983), Limited-Dependent and Qualitative Variables in Econometrics, Cambridge University Press, Cambridge.

  • Wooldridge J.M. (2002), Econometric Analysis of Cross-Section and Panel Data, MIT press

  • Lee M. (2005) Micro-Econometrics for policy, program and treatment effects. Advanced Text in Econometrics. Oxford University Press, Oxford


Survey articles l.jpg

Survey Articles:

  • Angrist J. (2001), Estimation of Limited-Dependent Variable Models with Binary Endogenous Regressors: Simple Strategies for Empirical Practice,” Journal of Business and Economic Statistics, 19, 2-28.

  • Angrist J.D., Krueger A.B. (1999), Empirical strategies in labor economics, published as working paper Princeton University, 401, and in O. Ashenfelter and D. Card, eds., Handbook of Labor Economics, Volume 3A, Amsterda,, 1277-1366.

  • Blundell R., Costa-Dias M. (2002), Alternative approaches to evaluation in empirical microeconomics', published as IFS, Cemmap working paper, 10, and in Portuguese Economic Journal, Vol.1, 91-115, 2002.

  • Blundell R., Powell J.L. (2001), Endogeneity in nonparametric and semiparametric regression models, IFS, Cemmap working paper, CWP09/01, Chapter 8 in Advances in Economics and Econometrics , M. Dewatripont, Hansen, L. and S. J. Turnsovsky (eds.), Cambridge University Press, ESM 36, pp 312-357,2003.

  • Heckman J.J., Ichimura H., Smith J.A., Todd P. (1998), Characterization of Selection Bias Using Experimental Data, Econometrica, 66, 1017-1098.

  • Heckman J.J., LaLonde R.J., Smith J.A. (2000), The economics and econometrics of active labor market programs, in O. Ashenfelter and D. Card, (eds.), Handbook of Labor Economics, vol. 3, North Holland, Amsterdam.

  • Moffitt R. (2004), An introduction to the symposium of matching econometrics, Review of Economics and Statistics, vol. 1, a collection of articles on matching by various authors.

  • Vella F. (1998), Estimating models with sample selection bias: a survey', The Journal of Human Resources, vol. 3, 127-169.