Spodaj je zbirka dodatnega gradiva na temo analize kavzalnosti v ekonometriji, povezana predvsem s Cerullijevim poglavjem, ki ga bomo obravnavali. Vsebuje tudi nekaj druge literature, ki se tukaj običajno navaja.
Spodnja zbirka vključuje kratko uvodno bibliografijo in sledeče članke (dobite jih s klikom na naslov članka)
Poglavje "Selection on Observables":
Abadie, A., & Imbens, G. W. (2006). Large sample properties of matching estimators for average treatment effects. Econometrica, 74(1), 235–267.
Abadie, A., & Imbens, G. W. (2008). On the failure of the bootstrap for matching estimators. Econometrica, 76(6), 1537–1557.
Abadie, A., & Imbens, G. W. (2009). Matching on the estimated propensity score. Harvard University and National Bureau of Economic Research.
Abadie, A., & Imbens, G. W. (2011). Bias-corrected matching estimators for average treatment effects. Journal of Business & Economic Statistics, 29, 1–11.
Abadie, A., Drukker, D., Herr, H., & Imbens, G. (2001). Implementing matching estimators for average treatment effects in Stata. The Stata Journal, 4, 290–311.
Becker, S. O., & Caliendo, M. (2007). Sensitivity analysis for average treatment effects. The Stata Journal, 7(1), 71–83.
Becker, S., & Ichino, A. (2002). Estimation of average treatment effects based on propensity scores. The Stata Journal, 2, 358–377.
Brunell, T. L., & DiNardo, J. E. (2004). A propensity score reweighting approach to estimating the partisan effects of full turnout in American presidential elections. Political Analysis, 12, 28–45.
Busso, M., DiNardo, J., & McCrary, J. (2009). New evidence on the finite sample properties of propensity score matching and reweighting estimators. Dept. Of Economics, UC Berkeley.
Caliendo, M., & Kopeinig, S. (2005). Some practical guidance for the implementation of propensity score matching. IZA Discussion Paper No. 1588.
Cattaneo, M. D. (2010). Efficient semiparametric estimation of multi–valued treatment effects under ignorability. Journal of Econometrics, 155, 138–154.
Dehejia, R., & Wahba, S. (1999). Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs. Journal of the American Statistical Association, 94, 1053–1062.
Dehejia, R., & Wahba, S. (2002). Propensity score–matching methods for nonexperimental causal studies. The Review of Economics and Statistics, 84, 151–161.
DiPrete, T., & Gangl, M. (2004). Assessing bias in the estimation of causal effects: Rosenbaum bounds on matching estimators and instrumental variables estimation with imperfect instruments. Wissenschaftszentrum Berlin für Sozialforschung (WZB).
Fan, J. (1993). Local linear regression smoothers and their minimax efficiencies. Annals of Statistics, 21, 196–216.
Hahn, J. (1998). On the role of the propensity score in efficient semiparametric estimation of average treatment effects. Econometrica, 66(2), 315–332.
Heckman, J. J., Ichimura, H., & Todd, P. E. (1997). Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme. Review of Economic Studies, 64(4), 605–54.
Heckman, J. J., Ichimura, H., & Todd, P. (1998). Matching as an econometric evaluation estimator. Review of Economic Studies, 65(2), 261–94.
Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient estimation of average treatment effects using the estimated propensity score. Econometrica, 71(4), 1161–1189.
Horvitz, D. G., & Thompson, D. J. (1952). A generalization of sampling without replacement from a finite universe source. Journal of the American Statistical Association, 47, 663–685.
Iacus, S. M., King, G., & Porro, G. (2009). CEM: Coarsened exact matching. The Stata Journal, 9, 524–546.
Iacus, S. M., King, G., & Porro, G. (2012). Causal inference without balance checking: Coarsened exact matching. Oxford University Press on behalf of the Society for Political Methodology.
Imbens, G. W. (2004). Nonparametric estimation of average treatment effects under exogeneity: A review. Review of Economics and Statistics, 86(1), 4–29.
LaLonde, R. (1986). Evaluating the econometric evaluations of training programs with experimental data. American Economic Review, 76, 604–620.
Lechner, M. (2000). A note on the common support problem in applied evaluation studies. Swiss Institute for International Economics and Applied Economic Research (SIAW) University of St. Gallen.
Li, Q., Racine, J. S., & Wooldridge, J. M. (2004). Efficient estimation of average treatment effects with mixed categorical and continuous data. Private Enterprise Research Center, Texas A&M University.
Lunceford, J. K., & Davidian, M. (2004). Stratification and weighting via the propensity score in estimation of causal treatment effects: A comparative study. Statistics in Medicine, 15, 2937–2960.
Nannicini, T. (2007). Simulation–based sensitivity analysis for matching estimators. The Stata Journal, 7, 3.
Newey, W. K. (1997). Convergence rates and asymptotic normality for series estimators. MIT, 95-13, Jan1995.
Robins, J. M., Hernan, M. A., & Brumback, B. A. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11, 550–560.
Robins, J. M., & Rotnitzky, A. (1995). Semiparametric efficiency in multivariate regression models with missing data. Journal of the American Statistical Association, 90, 122–129.
Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41–55.
Rosenbaum, P. R., & Rubin, D. B. (1984). Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association, 79(387), 147–156.
Seifert, B., & Gasser, T. (1999/2000). Data adaptive ridging in local polynomial regression. Journal of Computational and Graphical Statistics, 9, 338–360.
Smith, J. A., & Todd, P. E. (2003). Does matching overcome LaLonde’s critique of nonexperimental estimators? Centre for Human Capital and Productivity. CHCP Working Papers.
Stuart, E. A. (2010). Matching methods for causal inference: A review and a look forward. Statistical Science, 25(1), 1–21.
Wooldridge, J. M. (2007). Inverse probability weighted estimation for general missing data problems. Journal of Econometrics, 141, 1281–1301.
Zhao, Z. (2004). Using matching to estimate treatment effects: data requirements, matching metrics, and Monte Carlo evidence. Review of Economics and Statistics, 86, 91–107.
Poglavje "Selection on Unobservables":
Abadie, A. (2005). Semiparametric difference-in-differences estimators. Review of Economic Studies, 72, 1–19.
Abadie, A., Angrist, J. D., & Imbens, G. (2002). Instrumental variables estimates of the effect of subsidized training on the quantiles of trainee earnings. Econometrica, 70, 91–117.
Angrist, J. D. (1991). Instrumental variables estimation of average treatment effects in econometrics and epidemiology (Working Paper No. 115). National Bureau of Economic Research.
Angrist, J. D., & Imbens, G. W. (1995). Two-stage least squares estimation of average causal effects in models with variable treatment intensity. Journal of the American Statistical Association, 90, 431–442.
Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996). Identification of causal effects using instrumental variables. Journal of the American Statistical Association, 91, 444–455.
Angrist, J. D., & Krueger, A. B. (1991). Does compulsory school attendance affect schooling and earnings? Quarterly Journal of Economics, 106, 979–1014.
Angrist, J. D., & Krueger, A. B. (2001). Instrumental variables and the search for identification: From supply and demand to natural experiments. Journal of Economic Perspectives, 15, 69–85.
Autor, D. H. (2003). Outsourcing at will: The contribution of unjust dismissal doctrine to the growth of employment outsourcing. Journal of Labor Economics, 21, 1–42.
Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How much should we trust differences-indifferences estimates? Quarterly Journal of Economics, 119, 249–275.
Blundell, R., & Costa Dias, M. (2000). Evaluation methods for non-experimental data. Fiscal Studies, 21, 427–468.
Bound, J., Jaeger, D. A., & Baker, R. M. (1995). Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak. Journal of the American Statistical Association, 90, 443–450.
Burnett, N. J. (1997). Gender economics courses in liberal arts colleges. Journal of Economic Education, 28, 369–376.
Card, D. (1992). Using regional variation in wages to measure the effects of the federal minimum wage. Industrial & Labor Relations Review, 46, 22–37.
Card, D., & Krueger, A. B. (1994). Minimum wages and employment: A case study of the fastfood industry in New Jersey and Pennsylvania. American Economic Review, 84, 772–793.
Card, D., & Krueger, A. B. (2000). Minimum wages and employment: A case study of the fastfood industry in New Jersey and Pennsylvania: Reply. American Economic Review, 90, 1397–1420.
Cerulli, G. (2012). An assessment of the econometric methods for program evaluation and a proposal to extend the difference-in-differences estimator to dynamic treatment. In S. A. Mendez & A. M. Vega (Eds.), Econometrics: New research. New York: Nova. Chapter 1.
Donald, S. G., & Lang, K. (2007). Inference with difference-in-differences and other panel data. Review of Economics and Statistics, 89, 221–233.
Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424–438.
Hahn, J., & Hausman, J. (2005). Estimation with valid and invalid instruments. Annales d’Economie et de Statistique, 79–80, 25–57.
Heckman, J. J. (1978). Dummy endogenous variables in a simultaneous equation system. Econometrica, 46, 931–959.
Heckman, J. J. (1979). Sample selection bias as a specification error. Econometrica, 47, 153–161.
Heckman, J. J. (1997). Instrumental variables: A study of implicit behavioral assumptions used in making program evaluations. Journal of Human Resources, 32, 441.
Heckman, J. J., Ichimura, H., Smith, J., & Todd, P. (1998). Characterizing selection bias using experimental data (Working paper N. w6699). Cambridge: National Bureau of Economic Research.
Heckman, J. J., & Vytlacil, E. (1998). Instrumental variables methods for the correlated random coefficient model: Estimating the average rate of return to schooling when the return is correlated with schooling. Journal of Human Resources, 33, 974.
Heckman, J. J., & Vytlacil, E. J. (2001). Instrumental variables, selection models, and tight bounds on the average treatment effect. In P. D. M. Lechner & D. F. Pfeiffer (Eds.), Econometric evaluation of labour market policies (ZEW economic studies, pp. 1–15). Heidelberg: Physica.
Imbens, G. W., & Angrist, J. D. (1994). Identification and estimation of local average treatment effects. Econometrica, 62, 467–475.
Lach, S. (2002). Do R&D subsidies stimulate or displace private R&D? Evidence from Israel. Journal of Industrial Economics, 50, 369–390.
Lee, M. (2005). Micro-econometrics for policy, program and treatment effects (OUP catalogue). Oxford: Oxford University Press.
Meyer, B. D., Viscusi, W. K., & Durbin, D. L. (1995). Workers’ compensation and injury duration: Evidence from a natural experiment. American Economic Review, 85, 322–340.
Murray, M. P. (2006). Avoiding invalid instruments and coping with weak instruments. Journal of Economic Perspectives, 20, 111–132.
Nelson, C. R., & Startz, R. (1990a). Some further results on the exact small sample properties of the instrumental variable estimator. Econometrica, 58, 967–976.
Nelson, C. R., & Startz, R. (1990b). The distribution of the instrumental variables estimator and its t-ratio when the instrument is a poor one. Journal of Business, 63, S125–S140.
Nicoletti, C., & Peracchi, F. (2001). Two-step estimation of binary response models with sample selection. Fac. Econ. Tor Vergata Univ. Rome.
Phillips, P. C. B. (1983). Exact small sample theory in the simultaneous equations model. In Z. Griliches & M. D. Intriligator (Eds.), Handbook of econometrics (1st ed., Vol. 1, Chap. 8, pp. 449–516). Amsterdam: Elsevier.
Rivers, D., & Vuong, Q. H. (1988). Limited information estimators and exogeneity tests for simultaneous probit models. Journal of Econometrics, 39, 347–366.
Sargan, J. D. (1958). The estimation of economic relationships using instrumental variables. Econometrica, 26, 393–415.
Smith, J. A., & Todd, P. E. (2005). Does matching overcome LaLonde’s critique of nonexperimental estimators? Journal of Econometrics, 125, 305–353.
Stock, J. H., & Yogo, M. (2002). Testing for weak instruments in linear IV regression (NBER Technical Working Paper No. 0284). National Bureau of Economic Research, Inc.
Poglavje "Local Average Treatment Effect and Regression Discontinuity Design"
Abadie, A. (2003). Semiparametric instrumental variable estimation of treatment response models. Journal of Econometrics, 113, 231–263.
Abadie, A., Angrist, J. D., & Imbens, G. W. (2002). Instrumental variables estimates of the effect of subsidized training on the quantiles of trainee earnings. Econometrica, 70, 91–117.
Angrist, J. D. (1990). Lifetime earnings and the Vietnam era draft lottery: Evidence from social security administrative records. American Economic Review, 80, 313–336.
Angrist, J. D., & Evans, W. N. (1998). Children and their parents’ labor supply: Evidence from exogenous variation in family size. American Economic Review, 88, 450–477.
Angrist, J. D., & Imbens, G. W. (1995). Two-stage least squares estimation of average causal effects in models with variable treatment intensity. Journal of the American Statistical Association, 90, 431–442.
Angrist, J. D., & Lavy, V. (1999). Using Maimonides’ rule to estimate the effect of class size on scholastic achievement. Quarterly Journal of Economics, 114, 533–575.
Bloom, H. S. (1984). Accounting for no-shows in experimental evaluation designs. Evaluation Review, 8, 225–246.
Cook, T. D. (2008). “Waiting for life to arrive”: A history of the regression-discontinuity design in psychology, statistics and economics. Journal of Econometrics, 142, 636–654. doi:10.1016/j.jeconom.2007.05.002.
Fan, J., & Gijbels, I. (1996). Local polynomial modelling and its applications: Monographs on statistics and applied probability (Vol. 66). Boca Raton, FL: CRC Press.
Hahn, J., Todd, P., & Van der Klaauw, W. (2001). Identification and estimation of treatment effects with a regression-discontinuity design. Econometrica, 69, 201–209.
Hardle, W. (1991). Applied nonparametric regression. Cambridge: Cambridge University Press.
Hardle, W., & Marron, J. S. (1985). Optimal bandwidth selection in nonparametric regression function estimation. Annals of Statistics, 13, 1465–1481.
Imbens, G. W., & Angrist, J. D. (1994). Identification and estimation of local average treatment effects. Econometrica, 62, 467–475.
Imbens, G. W., & Kalyanaraman, K. (2012). Optimal bandwidth choice for the regression discontinuity estimator. Review of Economic Studies, 79, 933–959.
Imbens, G. W., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. Journal of Econometrics, 142, 615–635.
Lee, D. S., & Lemieux, T. (2009). Regression discontinuity designs in economics (NBER Working Paper No. 14723). National Bureau of Economic Research, Inc.
Lee, D. S., Moretti, E., & Butler, M. J. (2004). Do voters affect or elect policies? Evidence from the U. S. House. Quarterly Journal of Economics, 119, 807–859.
Ludwig, J., & Miller, D. L. (2007). Does head start improve children’s life chances? Evidence from a regression discontinuity design. Quarterly Journal of Economics, 122, 159–208.
McCrary, J. (2008). Manipulation of the running variable in the regression discontinuity design: A density test. Journal of Econometrics, 142, 698–714.
Nichols, A. (2007). Causal inference with observational data: Regression discontinuity and related methods in Stata (North American Stata Users’ Group Meetings 2007 No. 2). Stata Users Group.
Pagan, A., & Ullah, A. (1999). Nonparametric econometrics. Cambridge: Cambridge University Press.
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Mediation analysis:
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Dodajamo še nekaj temeljnih monografij, zaradi omejitev s prostorom seveda le najbolj osnovne (ponovno jih dobite s klikom na ime knjige):
J. D. Angrist and J. - S. Pischke. 2008. Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press.
P. Spirtes, C. Glymour and R. Scheines. 2001. Causation, Prediction, and Search. Adaptive Computation and Machine Learning series. The MIT Press.
A. P. Dawid. 2007. Fundamentals of Statistical Causality. RSS/EPSRC Graduate Training Programme, University of Sheffield.
A. C. Cameron and P. K. Trivedi. 2005. Microeconometrics: Methods and Applications. Cambridge University Press.
J. M. Wooldridge. 2005. Econometric Analysis of Cross Section and Panel Data. The MIT Press.
G. W. Imbens and J. M. Wooldridge. 2009. Recent developments in the econometrics of program evaluation. Journal of Economic Literature 47, 1: 5-86.
J. D. Angrist and J. - S. Pischke. 2008. Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press.
P. Spirtes, C. Glymour and R. Scheines. 2001. Causation, Prediction, and Search. Adaptive Computation and Machine Learning series. The MIT Press.
A. P. Dawid. 2007. Fundamentals of Statistical Causality. RSS/EPSRC Graduate Training Programme, University of Sheffield.
A. C. Cameron and P. K. Trivedi. 2005. Microeconometrics: Methods and Applications. Cambridge University Press.
J. M. Wooldridge. 2005. Econometric Analysis of Cross Section and Panel Data. The MIT Press.
G. W. Imbens and J. M. Wooldridge. 2009. Recent developments in the econometrics of program evaluation. Journal of Economic Literature 47, 1: 5-86.