Abstract
In this paper, we highlight some recent developments of a new route to evaluate macroeconomic policy effects, which are investigated under the framework with potential outcomes. First, this paper begins with a brief introduction of the basic model setup in modern econometric analysis of program evaluation. Secondly, primary attention goes to the focus on causal effect estimation of macroeconomic policy with single time series data together with some extensions to multiple time series data. Furthermore, we examine the connection of this new approach to traditional macroeconomic models for policy analysis and evaluation. Finally, we conclude by addressing some possible future research directions in statistics and econometrics.
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A Abadie, J D Angrist, G W Imbens. Instrumental variable estimates of the effect of subsidized training on the quantile of trainee earnings, Econometrica, 2002, 70(1): 91–117.
A Abadie, A Diamond, J Hainmueller. Synthetic control methods for comparative case studies: estimating the effect of California’s tobacco control program, J Amer Statist Assoc, 2010, 105 (2010): 493–505.
A Abadie, J Gardeazabal. The economic costs of conflict: a case study of the Basque country, Am Econ Rev, 2003, 93(1): 113–132.
A Abadie, G W Imbens. Large sample properties of matching estimators for average treatment effects, Econometrica, 2006, 74(1): 235–267.
A Abadie, G W Imbens. On the failure of the bootstrap for matching estimators, Econometrica, 2008, 76(6): 1537–1557.
A Abadie, G W Imbens. Bias-corrected matching estimators for average treatment effects, J Bus Econ Stat, 2011, 29(1): 1–11.
A Abadie, G W Imbens. Matching on the estimated propensity score, Econometrica, 2016, 84(2): 781–807.
J H Abbring, G J van den Berg. The nonparametric identification of treatment effects in duration models, Econometrica, 2003, 71(5): 1491–1517.
J D Angrist, G W Imbens. 2-stage least-squares estimation of average causal effects in models with variable treatment intensity, J Am Stat Assoc, 1995, 90(430): 431–442.
J D Angrist, Ó Jordà, G M Kuersteiner. Semiparametric estimates of monetary policy effects: string theory revisited, J Bus Econ Stat, 2018, 36(3): 371–387.
J D Angrist, G M Kuersteiner. Causal effects of monetary shocks: semiparametric conditional independence tests with a multinomial propensity score, Rev Econ Stat, 2011, 93(3): 725–747.
J D Angrist, V Lavy. Using maimonides’ rule to estimate the effect of class size on scholastic achievement, Q J Econ, 1999, 114(2): 533–575.
I Bojinov, N Shephard. Time series experiments and causal estimands: exact randomization tests and trading, J Am Stat Assoc, 2019, Forthcoming.
T L Brunell, J E DiNardo. A propensity score reweighting approach to estimating the Partisan effects of full turnout in American presidential elections, Polit Anal, 2004, 12(1): 28–45.
Z Cai, Y Fang, M Lin, S Tang. Testing conditional unconfoundedness using auxiliary variables, Working Paper, Department of Economics, University of Kansas, 2019.
M Caliendo, S Kopeinig. Some practical guidance for the implementation of propensity score matching, J Econ Surv, 2010, 22(1): 31–72.
G Cerulli. Econometric evaluation of socio-economic programs, Springer, 2015.
V Chernozhukov, C Hansen. An IV model of quantile treatment effects, Econometrica, 2005, 73(1): 245–261.
R H Dehejia, S Wahba. Causal effects in nonexperimental studies: reevaluating the evaluation of training programs, J Am Stat Assoc, 1999, 94(448): 1053–1062.
R H Dehejia, S Wahba. Propensity score-matching methods for nonexperimental causal studies, Rev Econ Stat, 2002, 84(1): 151–161.
Z C Du, L Zhang. Home-purchase restriction, property tax and housing price in China: A counterfactual analysis, J Econometrics, 2015, 188(2): 558–568.
H Fujiki, C Hsiao. Disentangling the effects of multiple treatments Measuring the net economic impact of the 1995 great Hanshin-Awaji earthquake, J Econometrics, 2015, 186(1): 66–73.
J Gardeazabal, A VegaBayo. An empirical comparison between the synthetic control method and HSIAO et al.’s panel data approach to program evaluation, J Appl Economet, 2017, 32(5): 983–1002.
A N Glynn, K M Quinn. An introduction to the augmented inverse propensity weighted estimator, Polit Anal, 2010, 18(1): 36–56.
J Hahn. On the role of the propensity score in efficient semiparametric estimation of average treatment effects, Econometrica, 1998, 66(2): 315–331.
J Hahn, P Todd, W V D Klaauw. Identification and estimation of treatment effects with a regression-discontinuity design, Econometrica, 2001, 69(1): 201–209.
J J Heckman, H Ichimura, P E Todd. Matching as an econometric evaluation estimator: evidence from evaluating a job training programme, Rev Econ Stud, 1997, 64(4): 605–654.
J J Heckman, H Ichimura, P E Todd. Matching as an econometric evaluation estimator, Rev Econ Stud, 1998, 65(2): 261–294.
K Hirano, G W Imbens. The propensity score with continuous treatments, in Applied bayesian modeling and causal inference from incomplete-data perspectives, edited by G Andrew and X L Meng, pp.73–84: Wiley-Blackwell, 2004.
K Hirano, G W Imbens, G Ridder. Efficient estimation of average treatment effects using the estimated propensity score, Econometrica, 2003, 71(4): 1161–1189.
P W Holland. Statistics and causal inference, J Am Stat Assoc, 1986, 81(396): 945–960.
D G Horvitz, D J Thompson. A Generalization of Sampling Without Replacement From a Finite Universe, J Am Stat Assoc, 1952, 47(260): 663–685.
C Hsiao, H S Ching, S K Wan. A panel data approach for program evaluation: measuring the benefits of political and economic integration of Hong Kong with Mainland China, J Appl Economet, 2012, 27(5): 705–740.
G W Imbens. The role of the propensity score in estimating dose-response functions, Biometrika, 2000, 87(3): 706–710.
G W Imbens. Nonparametric estimation of average treatment effects under exogeneity: a review, Rev Econ Stat, 2004, 86(1): 4–29.
G W Imbens, J M Wooldridge. Recent developments in the econometrics of program evaluation, J Econ Lit, 2009, 47(1): 5–86.
O Jorda, A M Taylor. The time for austerity: estimating the average treatment effect of fiscal policy, Econ J, 2016, 126(590): 219–255.
W V D Klaauw. Breaking the link between poverty and low student achievement: An evaluation of Title I, J Econometrics, 2008, 142(2): 731–756.
R Koenker, G Bassett. Regression quantiles, Econometrica, 1978, 46(1): 33–50.
G M Kuersteiner, D C Phillips, M Villamizar-Villegas. Effective sterilized foreign exchange intervention? Evidence from a rule-based policy, J Int Econ, 2018, 113: 118–138.
M J van der Lann, J M Robins. Unified methods for censored longitudinal data and causality, Springer, 2003.
D S Lee. Randomized experiments from non-random selection in U.S. house elections, J Econometrics, 2008, 142(2): 675–697.
D S Lee, D Card. Regression discontinuity inference with specification error, J Econometrics, 2006, 142(2): 655–674.
K T Li, D R Bell. Estimation of average treatment effects with panel data: Asymptotic theory and implementation, J Econometrics, 2017, 197(1): 65–75.
Z Liu, Z Cai, Y Fang. Policy evaluation of monetary policy and macro-prudential policy in China, Working paper, The Wang Yanan Institute for Studies in Economics, Xiamen University, 2019.
J J Lok. Statistical modeling of causal effects in continuous time, Ann Stat, 2008, 36(3): 1464–1507.
J Ludwig, D L Miller. Does head start improve children’s life chances? evidence from a regression discontinuity design, Q J Econ, 2007, 122(1): 159–208.
J K Lunceford, M Davidian. Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study, Stat Med, 2004, 23(19): 2937–2960.
M Ouyang, Y Peng. The treatment-effect estimation: a case study of the 2008 economic stimulus package of China, J Econometrics, 2015, 188(2): 545–557.
J M Robins. A new approach to causal inference in mortality studies with a sustained exposure periodapplication to control of the healthy worker survivor effect, Mathl Modelling, 1986, 7(9): 1393–1512.
J M Robins. Correcting for non-compliance in randomized trials using structural nested mean models, Commun Stat-Theor M, 1994, 23(8): 2379–2412.
J M Robins, S Greenland, F C Hu. Estimation of the causal effect of a time-varying exposure on the marginal mean of a repeated binary outcome, J Am Stat Assoc, 1999, 94(447): 687–700.
J M Robins, A Rotnitzky. Semiparametric efficiency in multivariate regression models with missing data, J Am Stat Assoc, 1995, 90(429): 122–129.
J M Robins, A Rotnitzky, L P Zhao. Analysis of semiparametric regression models for repeated outcomes in the presence of missing data, J Am Stat Assoc, 1995, 90(429): 106–121.
P R Rosenbaum, D B Rubin. The central role of the propensity score in observational studies for causal effects, Biometrika, 1983, 70(1): 41–55.
D B Rubin. Matching to remove bias in observational studies, Biometrics, 1973a, 29(1): 159–183.
D B Rubin. The use of matched sampling and regression adjustment to remove bias in observational studies, Biometrics, 1973b, 29(1): 185–203.
D B Rubin. Estimating causal effects of treatments in randomized and nonrandomized studies, J Educ Psychol, 1974, 66(5): 688–701.
D B Rubin. Assignment to Treatment Group on the Basis of a Covariate, J Educ Stat, 1977, 2(1): 1–26.
D B Rubin. Bayesian inference for causal effects: the role of randomization, Ann Stat, 1978, 6(1): 34–58.
D B Rubin. Using multivariate matched sampling and regression adjustment to control bias in observational studies, J Am Stat Assoc, 1979, 74(366): 318–328.
J Shao. Linear model selection by cross-validation, J Am Stat Assoc, 1993, 88(422), 486–495.
J Shao. Bootstrap model selection, J Am Stat Assoc, 1996, 91(434), 655–665.
D L Thistlethwaite, D T Campbell. Regression-discontinuity analysis: an alternative to the ex post facto experiment, J Educ Psychol, 1960, 51(6): 309–317.
S K Wan, Y Xie, C Hsiao. Panel data approach vs synthetic control method, Econ Lett, 2018, 164: 121–123.
J M Wooldridge. Inverse probability weighted estimation for general missing data problems, J Econometrics, 2007, 141(2): 1281–1301.
J M Wooldridge. Econometric analysis of cross section and panel data, MIT Press, 2010.
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Supported by the National Natural Science Foundation of China (71631004, Key Project), the National Science Fund for Distinguished Young Scholars (71625001), the Basic Scientific Center Project of National Science Foundation of China: Econometrics and Quantitative Policy Evaluation (71988101), the Science Foundation of Ministry of Education of China (19YJA910003) and China Scholarship Council Funded Project (201806315045).
The original version of this article was revised due to a retrospective Open Access order.
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Liu, Zq., Cai, Zw., Fang, Y. et al. Statistical Analysis and Evaluation of Macroeconomic Policies: A Selective Review. Appl. Math. J. Chin. Univ. 35, 57–83 (2020). https://doi.org/10.1007/s11766-020-3775-1
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DOI: https://doi.org/10.1007/s11766-020-3775-1