Densifications of the Distribution of Treatment Effect with Duration Outcomes

David Koch; Steve Ofili; Abel Cudjoe

David Koch Economic Development Department, Government of the Republic of South Africa
Steve Ofili University of Benin - Department of Economics & Statistics
Abel Cudjoe University of Ghana - Institute of Statistical, Social and Economic Research

Keywords: Competing Risk, Nonparametric, Confidence Bounds.

Abstract

The most critical factor in econometric estimations is parameter identification. Identification in econometric models formalizes prior assumptions and the data to information about a parameter of interest. However, there are two important features characterize duration data. The first one is that the data may be censored, and the second feature of duration data is that exogenous determinants of the event times characterizing the data may change during the event spell. The two characteristics lead some famous identification problems for the duration models. Following the recent literature in partial identification, we show the conditions when the duration models could be identified and provide several suggestions for the confidence bounds of partial identifications.

Downloads

Download data is not yet available.

References

Abbring, J. and G. Berg (2005). “Social experiments and instrumental variables with duration outcomes.” Free University Amsterdam, and Tinbergen Institute, Working Paper.

Berg, G. (1997). “Association measures for durations in bivariate hazard rate models.” Journal of Econometrics, vol. 79, pp. 221-245.

Berg, G. and B. Drepper (2016). “Inference for shared-frailty survival models with feft- truncated data.” Econometric Reviews, vol. 35, pp. 1075-1098.

Cunha, F. and J. Heckman (2008). “A new framework for the analysis of inequality.” Macroeconomic Dynamics, vol. 12, pp. 315-354.

Elbers, C. and G. Ridder (1982). “True and spurious duration dependence: the identifiability of the proportional hazard model.” Review of Economic Studies, vol. 49, pp. 655-682.

Fan, Y. and S. Park (2012). “Confidence sets for the quantile of treatment effects in randomized experiments.” Journal of Econometrics, vol. 167, pp. 330-340.

Fan, Y. and J. Wu (2010). “Partial identification of the distribution of treatment effects in switching regime models and its confidence sets.” Review of Economic Studies, vol. 77, pp. 1002-1041.

Guo, Z. (2017). “Comparison of error correction models and first-difference models in CCAR deposits modeling,” Global Journal of Management and Business Research, 2017, vol. 17, pp. 13-31;

Shintani, M. and Z. Guo (2016). “Improving the finite sample performance of autoregression estimators in dynamic factor models: A bootstrap approach”, Econometric Reviews.

Ichimura, H. (1993). “Semiparametric least squares and weighted SLS estimation of single-index models.” Journal of Econometrics, vol. 58, pp. 71-120.

Ham, J. and R. LaLonde (1996). “The effect of sample selection and initial conditions in duration models: evidence from experimental data on training.” Econometrica, vol. 64, pp. 175-205.

Heckman, J. (1990). “Varieties of selection bias.” American Economic Review: Papers and Proceedings, vol. 80, pp. 313-318.

Heckman, J., J.Smith and C. Taber (1998). “Accounting for dropouts in evaluations of social programs.” Review of Economics and Statistics, vol. 80, pp. 1-14.

Honoré, B. and J. Heckman (1989). “The identifiability of the competing risks model.” Review of Economic Studies, vol. 76, pp. 325-330.

Jenkins, S. (1995). “Easy estimation methods for discrete-time duration models.” Oxford Bulletin of Economics and Statistics, vol. 57, pp. 129-136.

Lancaster, T. (1990). The Econometric Analysis of Transition Data. Econometric Society Monographs.

Manski, C. (1988). “Identification of binary response models.” Journal of the American Statistical Association, vol. 83, pp. 729-738.

Powell, J., J. Stock and T. Stoker (1989). “Semiparametric estimation of index coefficients.” Econometrica, vol. 57, pp. 1403-1430.

Reza, S. and P. Rilstone (2016). “Semiparametric efficiency bounds and efficient estimation of discrete duration models with unspecified hazard rate.” Econometric Reviews, vol. 35, pp. 693-726.