Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-25T18:31:24.413Z Has data issue: false hasContentIssue false

Non-Parametric and Semi-Parametric Techniques for Modeling and Simulating Correlated, Non-Normal Price and Yield Distributions: Applications to Risk Analysis in Kansas Agriculture

Published online by Cambridge University Press:  28 April 2015

Allen M. Featherstone
Affiliation:
Department of Agricultural Economics, Kansas State University, Manhattan, Kansas
Terry L. Kastens
Affiliation:
Department of Agricultural Economics, Kansas State University, Manhattan, Kansas

Abstract

Parametric, non-parametric, and semi-parametric approaches are commonly used for modeling correlated distributions. Semi-parametric and non-parametric approaches are used to examine the risk situation for Kansas agriculture. Results from the model indicate that 2000 will be another difficult year for Kansas farmers, although crop income will increase slightly from 1999. However, unless another supplemental infusion of government payments occurs, crop income is expected to be the lowest since 1992.

Type
Invited Paper Sessions
Copyright
Copyright © Southern Agricultural Economics Association 2000

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Berends, P., Featherstone, A.M., and Kastens, T.L.. “Technology Restrictions on Cost Functions: An Application of Genetic Algorithms.” abstract in Journal of Agricultural and Applied Economics 31(1999):401.Google Scholar
Eakin, B.K., McMillen, D.P., and Bruno, M.J.. “Constructing Confidence Intervals using the Bootstrap: An Application to a Multi-Product Cost Function.” Review of Economics and Statistics 72(1990):339–44.CrossRefGoogle Scholar
Efron, B.Better Bootstrap Confidence Intervals.” Journal of the American Statistical Association 82(1987):171–85.CrossRefGoogle Scholar
Farm Services Agency, Kansas Office, Enrolled Farm and Producer Report Summaries for 1993, 1994, and 1995.Google Scholar
Featherstone, A.M., Moss, C.B., Baker, T.G., and Preckel, P.V.. “The Theoretical Effects of Farm Policies on Optimal Leverage and the Probability of Equity Losses.” American Journal of Agricultural Economics 70(1988):572–79.CrossRefGoogle Scholar
Featherstone, A.M., Moghnieh, G.A., and Goodwin, B.K.. “Farm-Level Nonparametric Analysis of Cost-Minimization and Profit-Maximization Behavior.” Agricultural Economics 13(November 1995):111–20.Google Scholar
Featherstone, A.M., Mintert, J., and Kastens, T.L.. “Kansas Agricultural Outlook.” Kansas Business Review 22(Winter 1998-99):1016.Google Scholar
Featherstone, A.M., Preckel, P.V., and Baker, T.G.. “Modeling Farm Financial Decisions in a Dynamic and Stochastic Environment.” Agricultural Finance Review 50(1990):8099.Google Scholar
Griffiths, W.E., Hill, R.C., and Judge, G.G.. Learning and Practicing Econometrics. New York: John Wiley & Sons, 1993.Google Scholar
Hannan, E.J.Regression for Time Series.” in Rosenblatt, M., (ed.), Time Series Analysis, John Wiley, New York, New York, 1963.Google Scholar
Hogg, R.V. and Craig, A.T. Introduction to Mathematical Statistics, Macmillan Publishing Company, Inc. New York, New York, 1978.Google Scholar
Kansas Department of Agriculture, USDA. Kansas Farm Facts. Several issues.Google Scholar
Kansas Department of Agriculture, USDA, Crops. Several issues.Google Scholar
Kansas Department of Agriculture, USDA, Agricultural Prices. Several issues.Google Scholar
Kastens, T.L. and Featherstone, A.M.. “The Federal Agricultural Improvement and Reform Act of 1996: A Kansas Perspective.” Review of Agricultural Economics 19(Fall-Winter 1997):326–49.CrossRefGoogle Scholar
Kastens, T.L. and Featherstone, A.M.. “Impact of Additional Federal Farm Subsidies on Kansas Tax Revenue.” Presentation to the Consensus Revenue Estimating Outlook Meeting, Topeka, Kansas, October 25, 1999.Google Scholar
Miller, A.C. and Rice, T.R.. “Discrete Approximations of Probability Distributions.” Management Science 29(1983):352362.CrossRefGoogle Scholar
Newey, W.K.Semiparametric Efficiency Bounds.” Journal of Applied Econometrics 5(1990):99135.CrossRefGoogle Scholar
Preckel, P.V. and DeVuyst, E.. “Efficient Handling of Probability Information for Decision Analysis Under Risk.” American Journal of Agricultural Economics 74,3(Aug. 1992):655662.CrossRefGoogle Scholar
Ramirez, O.A.Multivariate Transformations to Parametrically Model and Simulate Joint Price-Yield Distributions under Non-Normality, Autocorrelation and Heteroscedasticity: A Tool for Assessing Risk in Agriculture,” Journal of Agricultural and Applied Economics, Forthcoming 2000.Google Scholar
Richardson, J.W., Klose, S.L., and Gray, A.W. “An Applied Procedure for Estimating and Simulating Multivariate Empirical (MVE) Probability Distributions in Farm Level Risk Assessment and Policy Analysis,” Journal of Agricultural and Applied Economics, Forthcoming 2000.CrossRefGoogle Scholar
Tauer, L.Do New York Dairy Farmers Maximize Profits or Minimize Costs?American Journal of Agricultural Economics 77(1995):421–29.CrossRefGoogle Scholar
The Rainbow Book, Summary of the FAPRI Baseline. Food and Agricultural Policy Research Institute. Columbia, Missouri. December 1996.Google Scholar
U.S. Department of Agriculture. 1996 Farm Bill Press Release. April 26, 1996.Google Scholar
U.S. Department of Agriculture, Agricultural Prices. Several issues.Google Scholar
U.S. Department of Agriculture. Crop Production. National Agricultural Statistics. Several issues.Google Scholar
U.S. Department of Agriculture. Economic Indicators of the Farm Sector, State Financial Summary. Rural Economy Division, Economic Research Service. Several issues.Google Scholar
U.S. Department of Agriculture. Historical State Farm Income Indicators by State. Economic Research Service and National Agricultural Statistics Services. Computer database at Cornell University.Google Scholar
U.S. Department of Commerce. 1992 Census of Agriculture, Kansas State and County Data. Economics and Statistics Administration, Bureau of the Census.Google Scholar
Varian, H.R.Microeconomic Analysis. W.W Norton & Company. 1978.Google Scholar