政策效果評估之計量應用：Last Revised: [2016.12.29]

1. Overall Surveys:

   • Roberts, M. R. and Whited, T. M. (2012), "Endogeneity in empirical corporate finance.'' In G. M. Constantinides, M. Harris, and R. M. Stulz, eds. Handbook of the Economics of Finance, Vol. 2. (I love this paper.)

2. Difference-in-Differences Approach:

   • Beck, T., Levine, R., and Levkov, A. (2010), "Big bad banks? The winners and losers from bank deregulation in the United States.'' Journal of Finance, 65(5), 1637-1667. [code/data] [Time-varying effects]

   • Bertrand, M., Duflo, E. and Mullainathan, S. (2004), "How much should we trust differences-in-differences estimates?'' Quarterly Journal of Economics, 119(1), 249-275. [Standard errors: cluster id]

   • Conley, T. G. and Taber, C. R. (2011), "Inference with Difference in Differences with a Small Number of Policy Changes." Review of Economics and Statistics, 93(1), 113–125. [Small # of Treated Groups]

   • Levine, R., Levkov, A., and Rubinstein, Y. (2014), "Bank Deregulation and Racial Inequality in America.'' Journal of Finance, 65(5), 1637-1667. [Time-Varying Effects]

   • [diff: DD with Matching, Quantile DD]

3. Regression Discontinuity Design: [Stata Softwares]

   • Surveys:

     • Imbens, G. W. and Lemieux, T. (2008), "Regression discontinuity designs: A guide to practice.'' Journal of Econometrics, 142(2), 615-635.

   • Lee, D. S. and Lemieux, T. (2010), "Regression Discontinuity Designs in Economics.'' Journal of Economic Literature, 48(2), 281-355.

   • Lee, D. S. (2008), "Randomized Experiments from Non-Random Selection in U.S. House Elections.'' Journal of Econometrics, 142(2), 675-697.

   • Lindo, J. M., Sanders, N. J. and Oreopoulos, P. (2010), "Ability, gender, and performance standards: Evidence from academic probation.'' American Economic Journal: Applied Economics, 2(2): 95-117.

4. Synthetic Control Methods: [Stata]

   1. Abadie, A., Diamond, A. and Hainmueller, J. (2010), "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program.'' Journal of the American Statistical Association, 105(490), 493-505.

   2. Abadie, A., Diamond, A. and Hainmueller, J. (2011), "Synth: An R Package for Synthetic Control Methods in Comparative Case Studies." Journal of Statistical Software,  42(13).

   3. Abadie, A. and Gardeazabal, J. (2003), "The Economic Costs of Conflict: A Case Study of the Basque Country.'' American Economic Review, 93(1), 113-132.

5. Matching Approaches: [Software for Implementing Matching Methods and Propensity Scores]

   • Caliendo, M. and Kopeinig, S. (2008), "Some Practical Guidance for the Implementation of Propensity Score Matching.'' Journal of Economic Surveys, 22, 31-72.

   • cem (coarsened exact matching, CEM, available in Stata and R):

     • CEM is a monotonic imbalance-reducing matching method, which means that the balance between the treated and the control groups is chosen by ex ante user choice rather than being discovered through the usual laborious process of checking after the fact, tweaking the method, and repeatedly re-estimating. After preprocessing data with CEM, the analyst may then use a simple difference in means or whatever statistical model he or she would have applied without matching.

   • ebalance (entropy balancing, EB, available in Stata and R):

     • EB is a data preprocessing procedure that allows users to reweight a dataset. The preprocessing is based on a maximum entropy reweighting scheme that assigns weights to each data unit such that the covariate distributions in the reweighted data satisfy a set of moment conditions set by the researcher. This can be useful to balance covariate distributions in observational studies with a binary treatment where the control group data can be reweighted to match the covariate moments in the treatment group. Entropy balancing can also be used to reweight a survey sample to known characteristics from a target population. The weights that result from entropy balancing can be passed to any standard model to subsequently analyse the reweighted data.

   • psmathc2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing (psgraph), and covariate imbalance testing (pstest).

   • CBPS (covariate balancing propensity score, CBPS, available in R):

     • The propensity score is estimated such that it maximizes the resulting covariate balance as well as the prediction of treatment assignment. The method, therefore, avoids an iteration between model fitting and balance checking. Currently, the package can only deal with binary treatment in the cross section setting but in the future it will be extended to other settings including non-binary treatments and causal inference with longitudinal data.

   • MatchIt: (Nonparametric Preprocessing for Parametric Causal Inference, available in R)

     • MatchIt implements a wide range of sophisticated matching methods, making it possible to greatly reduce the dependence of causal inferences on hard-to-justify, but commonly made, statistical modeling assumptions. The software also easily fits into existing research practices since, after preprocessing data with MatchIt, researchers can use whatever parametric model they would have used without MatchIt, but produce inferences with substantially more robustness and less sensitivity to modeling assumptions. MatchIt is an R program, and also works seamlessly with Zelig.

6. Instrumental Variables: [Macro Panels: Brückner, Markus]

7. Panel Data:

   • (Dynamic) PD with Sample Selection, Endogeneity:

     • Semykina, A. and Wooldridge, J. M. (2010), "Estimating panel data models in the presence of endogeneity and selection." Journal of Econometrics, 157(2), 375-380.

     • Semykina, A. and Wooldridge, J. M. (2013), "Estimation of dynamic panel data models with sample selection." Journal of Applied Econometrics, 28(1), 47-61.

     • Semykina, A. and Wooldridge, J. M. (2017), "Binary response panel data models with sample selection and self-selection." Forthcoming in Journal of Applied Econometrics.

   • Distinguish Long-Run and Short-Run Effects:

     • Pesaran, M. H. and Smith, R. P. (1995), "Estimating Long-Run Relationships from Dynamic Heterogeneous Panels.'' Journal of Econometrics, 68, 79-113. (MG)

     • Pesaran, M. H., Shin, Y. and Smith, R. P. (1999), "Pooled Mean Group Estimation of Dynamic Heterogeneous Panels.'' Journal of the American Statistical Association, 94, 621-634. (PMG)

     • Blackburne, E. F. and Frank, M. W. (2007), "Estimation of Nonstationary Heterogeneous Panels." Stata Journal, 7(2), 197-208. (Stata: MG, PMG)

     • Eberhardt, M. (2012), "Estimating Panel Time-Series Models with Heterogeneous Slopes." Stata Journal, 12(1), 61-71. (Cross-Sectional Dependence) (Stata: MG, CCEMG, AMG)

   • Quantile (Dynamic) Panel Data (with Endogeneity):