000106591 001__ 106591
000106591 005__ 20230519145348.0
000106591 0247_ $$2doi$$a10.1080/02664763.2020.1796933
000106591 0248_ $$2sideral$$a119611
000106591 037__ $$aART-2021-119611
000106591 041__ $$aeng
000106591 100__ $$0(orcid)0000-0002-1539-8483$$aLópez-Laborda, Julio$$uUniversidad de Zaragoza
000106591 245__ $$aEstimating Engel curves: a new way to improve the SILC-HBS matching process using GLM methods
000106591 260__ $$c2021
000106591 5060_ $$aAccess copy available to the general public$$fUnrestricted
000106591 5203_ $$aMicrodata are required to evaluate the distributive impact of the taxation system as a whole (direct and indirect taxes) on individuals or households. However, in European Union countries this information is usually distributed into two separate surveys: the Household Budget Surveys (HBS), including total household expenditure and its composition, and EU Statistics on Income and Living Conditions (EU-SILC), including detailed information about households'' income and direct (but not indirect) taxes paid. We present a parametric statistical matching procedure to merge both surveys. For the first stage of matching, we propose estimating total household expenditure in HBS (Engel curves) using a GLM estimator, instead of the traditionally used OLS method. It is a better alternative, insofar as it can deal with the heteroskedasticity problem of the OLS estimates, while making it unnecessary to retransform the regressors estimated in logarithms. To evaluate these advantages of the GLM estimator, we conducted a computational Monte Carlo simulation. In addition, when an error term is added to the deterministic imputation of expenditure in the EU-SILC, we propose replacing the usual Normal distribution of the error with a Chi-square type, which allows a better approximation to the original expenditures variance in the HBS. An empirical analysis is provided using Spanish surveys for years 2012–2016. In addition, we extend the empirical analysis to the rest of the European Union countries, using the surveys provided by Eurostat (EU-SILC, 2011; HBS, 2010).
000106591 536__ $$9info:eu-repo/grantAgreement/ES/DGA-ERDF/Public Economics Research Group$$9info:eu-repo/grantAgreement/ES/MINECO/ECO2016-76506-C4-3-R$$9info:eu-repo/grantAgreement/ES/MINECO/ECO2017-87862-P
000106591 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000106591 590__ $$a1.416$$b2021
000106591 591__ $$aSTATISTICS & PROBABILITY$$b73 / 125 = 0.584$$c2021$$dQ3$$eT2
000106591 594__ $$a2.5$$b2021
000106591 592__ $$a0.507$$b2021
000106591 593__ $$aStatistics, Probability and Uncertainty$$c2021$$dQ3
000106591 593__ $$aStatistics and Probability$$c2021$$dQ3
000106591 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000106591 700__ $$aMarín-González, Carmen
000106591 700__ $$aOnrubia-Fernández, Jorge
000106591 7102_ $$14014$$2225$$aUniversidad de Zaragoza$$bDpto. Economía Aplicada$$cÁrea Economía Aplicada
000106591 773__ $$g48, 16 (2021), 3233-3250$$pJ. appl. stat.$$tJOURNAL OF APPLIED STATISTICS$$x0266-4763
000106591 8564_ $$s399005$$uhttps://zaguan.unizar.es/record/106591/files/texto_completo.pdf$$yPostprint
000106591 8564_ $$s1981432$$uhttps://zaguan.unizar.es/record/106591/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000106591 909CO $$ooai:zaguan.unizar.es:106591$$particulos$$pdriver
000106591 951__ $$a2023-05-18-13:23:15
000106591 980__ $$aARTICLE