Advances in land-use and stated-choice modeling using neural networks and discrete-choice models

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dc.contributor.author Ramsey, Steven M.
dc.date.accessioned 2017-07-17T13:23:35Z
dc.date.available 2017-07-17T13:23:35Z
dc.date.issued 2017-08-01 en_US
dc.identifier.uri http://hdl.handle.net/2097/35802
dc.description.abstract Applied research in agricultural economics often involves a discrete process. Most commonly, these applications entail a conceptual framework, such as random utility, that describes a discrete-variable data-generating process. Assumptions in the conceptual framework then imply a particular empirical model. Common approaches include the binary logit and probit models and the multinomial logit when more than two outcomes are possible. Conceptual frameworks based on a discrete choice process have also been used even when the dependent variable of interest is continuous. In any case, the standard models may not be well suited to the problem at hand, as a result of either the assumptions they require or the assumptions they impose. The general theme of this dissertation is to adopt seldom-used empirical models to standard research areas in the field through applied studies. A common motivation in each paper is to lessen the exposure to specification concerns associated with more traditional models. The first paper is an attempt to provide insights into what --- if any --- weather patterns farmers respond to with respect to cropping decisions. The study region is a subset of 11 north-central Kansas counties. Empirically, this study adopts a dynamic multinomial logit with random effects approach, which may be the first use of this model with respect to farmer land-use decisions. Results suggest that field-level land-use decisions are significantly influenced by past weather, at least up to ten years. Results also suggest, however, that that short-term deviations from the longer trend can also influence land-use decisions. The second paper proposes multiple-output artificial neural networks (ANNs) as an alternative to more traditional approaches to estimating a system of acreage-share equations. To assess their viability as an alternative to traditional estimation, ANN results are compared to a linear-in-explanatory variables and parameters heteroskedastic and time-wise autoregressive seemingly unrelated regression model. Specifically, the two approaches are compared with respect to model fit and acre elasticities. Results suggest that the ANN is a viable alternative to a simple traditional model that is misspecified, as it produced plausible acre-response elasticities and outperformed the traditional model in terms of model fit. The third paper proposes ANNs as an alternative to the traditional logit model for contingent valuation analysis. With the correct network specifications, ANNs can be viewed as a traditional logistic regression where the index function has been replaced by a flexible functional form. The paper presents methods for obtaining marginal effect and willingness-to-pay (WTP) measures from ANNs, which has not been provided by the existing literature. To assess the viability of this approach, it is compared with the traditional logit and probit models as well an additional semi-nonparametric estimator with respect to model fit, marginal effects, and WTP estimates. Results suggest ANNs are viable alternative and may be preferable if misspecification of the index function is a concern. en_US
dc.description.sponsorship National Science Foundation en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject Land use en_US
dc.subject Discrete choice en_US
dc.subject Neural networks en_US
dc.subject Contingent valuation en_US
dc.title Advances in land-use and stated-choice modeling using neural networks and discrete-choice models en_US
dc.type Dissertation en_US
dc.description.degree Doctor of Philosophy en_US
dc.description.level Doctoral en_US
dc.description.department Department of Agricultural Economics en_US
dc.description.advisor Jason S. Bergtold en_US
dc.description.advisor Jessica L. Heier Stamm en_US
dc.date.published 2017 en_US
dc.date.graduationmonth August en_US


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