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  4. On the robustness of location estimators in models of firm growth under heavy-tailedness
 
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On the robustness of location estimators in models of firm growth under heavy-tailedness
File(s)
IbragimovGrowthFinal.pdf (167.92 KB)
Accepted version
Author(s)
Ibragimov, R
Type
Journal Article
Abstract
Focusing on the model of demand-driven innovation and spatial competition over time in Jovanovic and Rob (1987), we study the effects of the robustness of estimators employed by firms to make inferences about their markets on the firms’ growth patterns. We show that if consumers’ signals in the model are moderately heavy-tailed and the firms use the sample mean of the signals to estimate the ideal product, then the firms’ output levels exhibit positive persistence. In such a setting, large firms have an advantage over their smaller counterparts. These properties are reversed for signals with extremely heavy-tailed distributions. In such a case, the model implies anti-persistence in output levels, together with a surprising pattern of oscillations in firm sizes, with smaller firms being likely to become larger ones next period, and vice versa. We further show that the implications of the model under moderate heavy-tailedness continue to hold under the only assumption of symmetry of consumers’ signals if the firms use a more robust estimator of the ideal product, the sample median.
Date Issued
2014-07-01
Date Acceptance
2014-03-01
Citation
Journal of Econometrics, 2014, 181 (1), pp.25-33
URI
http://hdl.handle.net/10044/1/67734
DOI
https://www.dx.doi.org/10.1016/j.jeconom.2014.02.005
ISSN
0304-4076
Publisher
Elsevier
Start Page
25
End Page
33
Journal / Book Title
Journal of Econometrics
Volume
181
Issue
1
Copyright Statement
© 2014 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/
Sponsor
National Science Foundation
Grant Number
SES-0820124
Subjects
Social Sciences
Science & Technology
Physical Sciences
Economics
Mathematics, Interdisciplinary Applications
Social Sciences, Mathematical Methods
Business & Economics
Mathematics
Mathematical Methods In Social Sciences
Robustness
Location estimators
Heavy-tailed distributions
Demand-driven innovation
Spatial competition
Firm growth
Signals
Investment
Information
Sample mean
Sample median
Majorization
PORTFOLIO DIVERSIFICATION
DESCRIPTIVE STATISTICS
NONPARAMETRIC MODELS
SIZE DISTRIBUTION
POWER LAWS
ZIPFS LAW
DISTRIBUTIONS
MARKETS
FLUCTUATIONS
INNOVATION
1403 Econometrics
Econometrics
Publication Status
Published
Date Publish Online
2014-03-01
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