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Imputation of missing expenditure information in standard household income surveys. (2015). Pacifico, Daniele ; baldini, massimo ; Termini, Federica .
In: Center for the Analysis of Public Policies (CAPP).
RePEc:mod:cappmo:0116.

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  1. The Value Added Tax Simulation Model: VATSIM-DF (II). (2021). Manzo, Marco ; Imperioli, Lucia ; Cirillo, Cristina.
    In: Working Papers.
    RePEc:ahg:wpaper:wp2021-12.

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  2. Shifting taxes from labour to consumption: the efficiency-equity trade-off. (2019). Savegnago, Marco ; Curci, Nicola.
    In: Temi di discussione (Economic working papers).
    RePEc:bdi:wptemi:td_1244_19.

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  3. Is the Gasoline Tax Regressive in the Twenty-First Century? Taking Wealth into Account. (2017). Teixido, Jordi ; Verde, Stefano F.
    In: Ecological Economics.
    RePEc:eee:ecolec:v:138:y:2017:i:c:p:109-125.

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  4. Estimating consumption in the HFCS: Experimental results on the first wave of the HFCS. (2017). Lamarche, Pierre.
    In: Statistics Paper Series.
    RePEc:ecb:ecbsps:201722.

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  5. Longitudinal statistical matching: transferring consumption expenditure from HBS to SILC panel survey. (2016). Betti, Gianni ; Ucar, Baris .
    In: Department of Economics University of Siena.
    RePEc:usi:wpaper:739.

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References

References cited by this document

  1. Abadie, Alberto, and Guido W. Imbens. "Large sample properties of matching estimators for average treatment effects." Econometrica 74.1 (2006): 235-267.

  2. Baldini, Massimo, Giarda Elena and Arianna Olivieri. “A tax-benefit microsimulation model for Italy: A partial evaluation of fiscal consolidation in the period 2011-14”, Prometeia, Nota di Lavoro n. 2015-01, www.prometeia.com (2015). Ballin, Marco, et al. "Statistical Matching of Two Surveys with a Common Subset." Tec.
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  3. Becker, Sascha, and Andrea Ichino. "Estimation of average treatment effects based on propensity scores." The stata journal 2.4 (2002): 358-377.

  4. Caliendo, Marco, and Sabine Kopeinig. "Some practical guidance for the implementation of propensity score matching." Journal of economic surveys 22.1 (2008): 31-72.

  5. Decoster, Andrè, et al. “Comparative analysis of different techniques to impute expenditures into an income dataset.” AIM-AP deliverable (2007).
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  6. Decoster, André, et al. "Microsimulation of indirect taxes." International journal of microsimulation 4.2 (2011): 41-56.

  7. Diamond, Alexis, and Jasjeet S. Sekhon. "Genetic matching for estimating causal effects: A general multivariate matching method for achieving balance in observational studies." Review of Economics and Statistics 95.3 (2013): 932-945.

  8. Donatiello, Gabriella et al. “Statistical Matching of Income and Consumption Expenditures.” Report from 9th International Academic Conference, Istanbul (2014) Eurostat, “Statistical matching of EU-SILC and the Household Budget Survey to compare poverty estimates using income. expenditures and material deprivation.” Methodologies and Working Papers (2013) Guo, Shenyang, and Mark W. Fraser. “Propensity score analysis: Statistical methods and applications.” Vol. 12. Sage Publications, (2009).
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  9. Imbens, Guido W. "Nonparametric estimation of average treatment effects under exogeneity: A review." Review of Economics and statistics 86.1 (2004): 4-29.

  10. Pisano, Elena, and Simone Tedeschi. “Micro Data Fusion of Italian Expenditures and Incomes Surveys.” Working Paper No. 164 (2014). Department of Public Economics -University of Rome, La Sapienza,.

  11. Rässler, Susanne. "Data fusion: identification problems, validity, and multiple imputation." Austrian Journal of Statistics 33.1-2 (2004): 153-171.
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  12. Rubin, Donald B. "Bias reduction using Mahalanobis-metric matching."Biometrics (1980): 293-298.
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  13. Sekhon, Jasjeet S. "Multivariate and propensity score matching software with automated balance optimization: the matching package for R." Journal of Statistical Software, 42.7 (2011): 1-52.

  14. Stiglitz, Joseph, “Report on the Measurement of Economic Performance and Social Progress.” Commission on the Measurement of Economic Performance and Social Progress, Paris (2009) Zhao, Zhong. "Using matching to estimate treatment effects: data requirements, matching metrics, and Monte Carlo evidence." The review of economics and statistics.
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