Gibrat’s law and quantile regressions: An application to firm growth
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Gibrat’s law and quantile regressions : An application to firm growth. / Distante, Roberta; Petrella, Ivan; Santoro, Emiliano.
I: Economics Letters, Bind 164, 2018, s. 5-9.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Gibrat’s law and quantile regressions
T2 - An application to firm growth
AU - Distante, Roberta
AU - Petrella, Ivan
AU - Santoro, Emiliano
PY - 2018
Y1 - 2018
N2 - The nexus between firm growth, size and age in U.S. manufacturing is examined through the lens of quantile regression models. This methodology allows us to overcome serious shortcomings entailed by linear regression models employed by much of the existing literature, unveiling a number of important properties. Size pushes both low and high performing firms towards the median rate of growth, while age is never advantageous, and more so as firms are relatively small and grow faster. These findings support theoretical generalizations of Gibrat’s law that allow size to affect the variance of the growth process, but not its mean (Cordoba, 2008).
AB - The nexus between firm growth, size and age in U.S. manufacturing is examined through the lens of quantile regression models. This methodology allows us to overcome serious shortcomings entailed by linear regression models employed by much of the existing literature, unveiling a number of important properties. Size pushes both low and high performing firms towards the median rate of growth, while age is never advantageous, and more so as firms are relatively small and grow faster. These findings support theoretical generalizations of Gibrat’s law that allow size to affect the variance of the growth process, but not its mean (Cordoba, 2008).
KW - Faculty of Social Sciences
KW - Firm growth
KW - Size
KW - Age
KW - Conditional quantiles
U2 - 10.1016/j.econlet.2017.12.028
DO - 10.1016/j.econlet.2017.12.028
M3 - Journal article
VL - 164
SP - 5
EP - 9
JO - Economics Letters
JF - Economics Letters
SN - 0165-1765
ER -
ID: 222750740