TY - JOUR

T1 - Computing regression statistics from grouped data

AU - Schwiebert, Jörg

PY - 2015

Y1 - 2015

N2 - This paper considers regression techniques for grouped data. In particular, it is shown how regression statistics obtained from individual level data can be replicated by means of grouped data. Three common regression approaches are considered: ordinary least squares, instrumental variables and nonlinear least squares regression. Also provided is code to implement the grouped-data techniques in the econometric software package Stata. An empirical example illustrates that the grouped-data formulas indeed replicate the statistics obtained from the individual level data. It is also argued why grouped data are important for empirical research.

AB - This paper considers regression techniques for grouped data. In particular, it is shown how regression statistics obtained from individual level data can be replicated by means of grouped data. Three common regression approaches are considered: ordinary least squares, instrumental variables and nonlinear least squares regression. Also provided is code to implement the grouped-data techniques in the econometric software package Stata. An empirical example illustrates that the grouped-data formulas indeed replicate the statistics obtained from the individual level data. It is also argued why grouped data are important for empirical research.

KW - Economics

KW - Data confidentiality

KW - grouped data

KW - instrumental variables

KW - nonlinear least Squares

KW - ordinary least squares

UR - http://www.scopus.com/inward/record.url?scp=84936764957&partnerID=8YFLogxK

U2 - 10.3233/JEM-150416

DO - 10.3233/JEM-150416

M3 - Journal articles

AN - SCOPUS:84936764957

VL - 39

SP - 283

EP - 303

JO - Journal of Economic and Social Measurement

JF - Journal of Economic and Social Measurement

SN - 0747-9662

IS - 4

ER -