Identifying demand shocks from production data

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Standard productivity estimates contain a mixture of cost efficiency and demand conditions. I propose a method to identify the distribution of the demand shock using production data. Identification does not depend on functional form restrictions. It is also robust to dynamic demand considerations and flexible labor. In the parametric case, the ratio of intermediate inputs to the wage bill (input ratio) contains information about the magnitude of the demand shock. The method is tested using data from Spain that contains information on prices and demand conditions. Finally, we generate Monte Carlo simulations to evaluate the method’s performance and sensitivity. Supplementary materials for this article are available online.

Original languageEnglish
Pages (from-to)93-106
Number of pages14
JournalJournal of Business and Economic Statistics
Volume38
Issue number1
DOIs
Publication statusPublished - 2020

Fingerprint

Shock
demand
Cost Efficiency
Wages
bill
Productivity
wage
Spain
Monte Carlo Simulation
productivity
Demand
Demand shocks
labor
Restriction
efficiency
simulation
Evaluate
costs
Estimate
performance

Cite this

@article{e34ff0db284e4a6e97411e58edf90f38,
title = "Identifying demand shocks from production data",
abstract = "Standard productivity estimates contain a mixture of cost efficiency and demand conditions. I propose a method to identify the distribution of the demand shock using production data. Identification does not depend on functional form restrictions. It is also robust to dynamic demand considerations and flexible labor. In the parametric case, the ratio of intermediate inputs to the wage bill (input ratio) contains information about the magnitude of the demand shock. The method is tested using data from Spain that contains information on prices and demand conditions. Finally, we generate Monte Carlo simulations to evaluate the method’s performance and sensitivity. Supplementary materials for this article are available online.",
author = "Santos, {Carlos Daniel}",
year = "2020",
doi = "10.1080/07350015.2018.1458622",
language = "English",
volume = "38",
pages = "93--106",
journal = "Journal of Business and Economic Statistics",
issn = "0735-0015",
publisher = "Taylor & Francis",
number = "1",

}

Identifying demand shocks from production data. / Santos, Carlos Daniel.

In: Journal of Business and Economic Statistics, Vol. 38, No. 1, 2020, p. 93-106.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Identifying demand shocks from production data

AU - Santos, Carlos Daniel

PY - 2020

Y1 - 2020

N2 - Standard productivity estimates contain a mixture of cost efficiency and demand conditions. I propose a method to identify the distribution of the demand shock using production data. Identification does not depend on functional form restrictions. It is also robust to dynamic demand considerations and flexible labor. In the parametric case, the ratio of intermediate inputs to the wage bill (input ratio) contains information about the magnitude of the demand shock. The method is tested using data from Spain that contains information on prices and demand conditions. Finally, we generate Monte Carlo simulations to evaluate the method’s performance and sensitivity. Supplementary materials for this article are available online.

AB - Standard productivity estimates contain a mixture of cost efficiency and demand conditions. I propose a method to identify the distribution of the demand shock using production data. Identification does not depend on functional form restrictions. It is also robust to dynamic demand considerations and flexible labor. In the parametric case, the ratio of intermediate inputs to the wage bill (input ratio) contains information about the magnitude of the demand shock. The method is tested using data from Spain that contains information on prices and demand conditions. Finally, we generate Monte Carlo simulations to evaluate the method’s performance and sensitivity. Supplementary materials for this article are available online.

UR - http://www.scopus.com/inward/record.url?scp=85049579886&partnerID=8YFLogxK

U2 - 10.1080/07350015.2018.1458622

DO - 10.1080/07350015.2018.1458622

M3 - Article

VL - 38

SP - 93

EP - 106

JO - Journal of Business and Economic Statistics

JF - Journal of Business and Economic Statistics

SN - 0735-0015

IS - 1

ER -