Maximum Likelihood Estimation of Input Demand Models with Fixed Costs of Adjustment

Francesca Di Iorio and Stefano Fachin

Statistical Methods & Applications, 2006, vol. 15, issue 1, No 9, 129-137

Abstract: Abstract Traditional models of input demand rely upon convex and symmetric adjustment costs. However, the fortune of this highly restrictive approach is due more to analytical convenience than to empirical relevance. In this note we examine the model under more realistic hypothesis of fixed costs, show that it can be cast in the form of a Double Censored Random Effect Tobit Model, derive its likelihood function, and finally evaluate the performance of the ML estimators through a Monte Carlo experiment. The performances, although strongly dependent on the degree of censoring, appear to be promising.

Keywords: Adjustment Cost; Data Generate Process; Monte Carlo Experiment; Input Demand; Analytical Convenience (search for similar items in EconPapers)
Date: 2006
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10260-006-0014-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stmapp:v:15:y:2006:i:1:d:10.1007_s10260-006-0014-8

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10260/PS2

DOI: 10.1007/s10260-006-0014-8

Access Statistics for this article

Statistical Methods & Applications is currently edited by Tommaso Proietti

More articles in Statistical Methods & Applications from Springer, Società Italiana di Statistica
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().