Estimating state-contingent production frontiers
Chambers and Quiggin (2000) use state-contingent representations of risky production technologies to establish important theoretical results concerning producer behavior under uncertainty. Unfortunately, perceived problems in the estimation of state-contingent models have limited the usefulness of t...
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| Veröffentlicht in: | American journal of agricultural economics 2006-02, Vol.88 (1), p.249-266 |
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| container_title | American journal of agricultural economics |
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| creator | O'Donnell, C. J. Griffiths, W. E. |
| description | Chambers and Quiggin (2000) use state-contingent representations of risky production technologies to establish important theoretical results concerning producer behavior under uncertainty. Unfortunately, perceived problems in the estimation of state-contingent models have limited the usefulness of the approach in policy formulation. We show that fixed and random effects state-contingent production frontiers can be conveniently estimated in a finite mixtures framework. An empirical example is provided. Compared to conventional estimation approaches, we find that estimating production frontiers in a state-contingent framework produces significantly different estimates of elasticities, firm technical efficiencies, and other quantities of economic interest. |
| doi_str_mv | 10.1111/j.1467-8276.2006.00851.x |
| format | Article |
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J.</creatorcontrib><creatorcontrib>Griffiths, W. E.</creatorcontrib><title>Estimating state-contingent production frontiers</title><title>American journal of agricultural economics</title><addtitle>American Journal of Agricultural Economics</addtitle><addtitle>American Journal of Agricultural Economics</addtitle><description>Chambers and Quiggin (2000) use state-contingent representations of risky production technologies to establish important theoretical results concerning producer behavior under uncertainty. Unfortunately, perceived problems in the estimation of state-contingent models have limited the usefulness of the approach in policy formulation. We show that fixed and random effects state-contingent production frontiers can be conveniently estimated in a finite mixtures framework. An empirical example is provided. Compared to conventional estimation approaches, we find that estimating production frontiers in a state-contingent framework produces significantly different estimates of elasticities, firm technical efficiencies, and other quantities of economic interest.</description><subject>Agricultural economics</subject><subject>Bayesian analysis</subject><subject>Bayesian theory</subject><subject>C320</subject><subject>Coefficients</subject><subject>Commercial production</subject><subject>crop production</subject><subject>econometric models</subject><subject>Economic models</subject><subject>economic policy</subject><subject>elasticities</subject><subject>Estimating techniques</subject><subject>estimation</subject><subject>finite mixtures</subject><subject>Firm theory</subject><subject>Forecasts</subject><subject>Gibb's sampling</subject><subject>inefficiency</subject><subject>input output analysis</subject><subject>macroeconomics</subject><subject>Market</subject><subject>Mathematical independent variables</subject><subject>Oryza sativa</subject><subject>Parametric models</subject><subject>Production</subject><subject>production economics</subject><subject>Production efficiency</subject><subject>Production estimates</subject><subject>production technology</subject><subject>Productivity</subject><subject>Q100</subject><subject>Rice</subject><subject>Risk</subject><subject>risk assessment</subject><subject>State of nature</subject><subject>Stochastic models</subject><subject>Studies</subject><subject>Technology</subject><subject>Uncertainty</subject><issn>0002-9092</issn><issn>1467-8276</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><recordid>eNqNkFFrHCEUhSW00G2af1DIkoe-zfSqO-o8LmHbJCSEtA0sebmI44SZbsaNOnTz76uZsA-lodUHuX7nHPQQMqdQ0rQ-9yVdCFkoJkXJAEQJoCpa7g7IbA_ekBkAsKKGmr0j70Po0wi0VjMCqxC7Bx274X4eoo62MG7Ikx3ifOtdM5rYuWHe-nxtffhA3rZ6E-zRy3lIbr-sfpyeFZfXX89Pl5eFkYyvC8ttA62uBW8bo6UWtraaGUGlZUxpxphsLEjd0FY0umpVpZJAGSFMpWDB-SH5NOWmRzyONkR86IKxm40erBsDclGBYhT-KaR1XckFy4knfwh7N_ohfQITpUIJRZNITSLjXQjetrj1qR__hBQwF4495l4x94q5cHwuHHfJejVZf3Ub-_TfPlxeLFcXmWWUyTNYpzw-5blx-0pa8bdXfJxcfYjO731c1DLthIsJdyHa3R5r_xOF5LLCs_Ud3oibq0VFv2Eu7XjSt9qhvvddwNvvDCgHCqkvAfw3uYC6Xg</recordid><startdate>200602</startdate><enddate>200602</enddate><creator>O'Donnell, C. 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| source | Wiley Online Library Journals Frontfile Complete; Business Source Complete; Jstor Complete Legacy |
| subjects | Agricultural economics Bayesian analysis Bayesian theory C320 Coefficients Commercial production crop production econometric models Economic models economic policy elasticities Estimating techniques estimation finite mixtures Firm theory Forecasts Gibb's sampling inefficiency input output analysis macroeconomics Market Mathematical independent variables Oryza sativa Parametric models Production production economics Production efficiency Production estimates production technology Productivity Q100 Rice Risk risk assessment State of nature Stochastic models Studies Technology Uncertainty |
| title | Estimating state-contingent production frontiers |
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