Altmetric

On the robustness of location estimators in models of firm growth under heavy-tailedness

File Description SizeFormat 
IbragimovGrowthFinal.pdfAccepted version167.92 kBAdobe PDFView/Open
Title: On the robustness of location estimators in models of firm growth under heavy-tailedness
Authors: Ibragimov, R
Item 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.
Issue Date: 1-Jul-2014
Date of Acceptance: 1-Mar-2014
URI: http://hdl.handle.net/10044/1/67734
DOI: https://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/Funder: National Science Foundation
Funder's Grant Number: SES-0820124
Keywords: 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
Online Publication Date: 2014-03-01
Appears in Collections:Imperial College Business School



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Creative Commonsx