Microsimulating farm business performance
Microsimulating farm business performance
Microsimulation of business performance based on sample survey data is a relatively underdeveloped field, but its application in government economic policy formulation is potentially great since it can be used to measure the distributional effects of change rather than just average change. Techniques which account for the dynamic response of businesses to macro level price expectations have recently been developed (Kokic et al., 1993). These allow individual level business performance to be forecast from sample survey data. In this paper we outline a general methodology for combining these forecasting techniques with Monte Carlo simulation in order to produce a microsimulation of business performance that accurately captures the true distributional characteristics of the underling survey data. Applying this methodology to Australian farm survey data, we show that these methods may be used to forecast the distribution of farm business production and performance within arbitrary subdomains of the surveyed population conditional on a given set of expected commodity price outcomes. The microsimulations reflect both the uncertainty due to climatic variation from one year to the next, which in the Australian context depends largely on geographic location, as well as the uncertainty of commodity prices.
expectile regression, kernel smoothing, M-quantile regression, performance measurement, supply modelling
259-275
Kokic, P.
eb738cae-8a6d-45a5-beca-7e51781521e1
Chambers, R.
a9a457b3-2dc5-4ff0-9ed3-dbb3a6901ed7
Beare, S.
90ea8f60-9540-4b3c-b0ee-90effde50c3f
December 2000
Kokic, P.
eb738cae-8a6d-45a5-beca-7e51781521e1
Chambers, R.
a9a457b3-2dc5-4ff0-9ed3-dbb3a6901ed7
Beare, S.
90ea8f60-9540-4b3c-b0ee-90effde50c3f
Kokic, P., Chambers, R. and Beare, S.
(2000)
Microsimulating farm business performance.
International Statistical Review, 68 (3), .
Abstract
Microsimulation of business performance based on sample survey data is a relatively underdeveloped field, but its application in government economic policy formulation is potentially great since it can be used to measure the distributional effects of change rather than just average change. Techniques which account for the dynamic response of businesses to macro level price expectations have recently been developed (Kokic et al., 1993). These allow individual level business performance to be forecast from sample survey data. In this paper we outline a general methodology for combining these forecasting techniques with Monte Carlo simulation in order to produce a microsimulation of business performance that accurately captures the true distributional characteristics of the underling survey data. Applying this methodology to Australian farm survey data, we show that these methods may be used to forecast the distribution of farm business production and performance within arbitrary subdomains of the surveyed population conditional on a given set of expected commodity price outcomes. The microsimulations reflect both the uncertainty due to climatic variation from one year to the next, which in the Australian context depends largely on geographic location, as well as the uncertainty of commodity prices.
This record has no associated files available for download.
More information
Published date: December 2000
Keywords:
expectile regression, kernel smoothing, M-quantile regression, performance measurement, supply modelling
Identifiers
Local EPrints ID: 34171
URI: http://eprints.soton.ac.uk/id/eprint/34171
ISSN: 0306-7734
PURE UUID: 78234a21-96ed-4570-82e3-eae29ab36af6
Catalogue record
Date deposited: 21 Aug 2008
Last modified: 22 Jul 2022 20:42
Export record
Contributors
Author:
P. Kokic
Author:
R. Chambers
Author:
S. Beare
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics