A consistent two-step estimation procedure is proposed for a system of equations with limited dependent variables. Monte Carlo simulation results suggest the procedure outperforms an existing two-step method. Copyright 1999, Oxford University Press.
Conservation behavior is influenced by the attitudes of farmers and by context va¡ like income and farm terrain. Important attitudes were selected by using the theory that fundamental value ranks or weights affect attitudes and that only certain values are important to the conservation decision. An extension of the tobit estimation approach, handling both censored observations of the dependent variable and measurement error for the nonlimit observations, was used. Conservation behavior models can be improved with a merger of concepts and approaches from social psychology and economics.Economic theory provides limited guidance in the selection of vafiables to explain the resource conservation actions of farmers (Norris and Batie). A strict profit maximization framework (e.g., McConnell) fails to encompass attitudinal variables. A utility maximization framework is less restrictive, but economic theory is of little help in identifying psychological processes that determine preferences.The purpose of this article is to advance economic modeling of farmers' conservation decisions by improving the ways in which values, beliefs, attitudes, and intentions are incorporated. A behavioral model is developed and tested with respect to soil management decisions of Florida farmers. Past Studies of Conservation BehaviorThe importance of preferences in determining farmers' conservation behavior is a theme in a number of economic and sociological studies. Ervin and Ervin included attitudinal and institutional variables in a theoretically sound model of conservation decisions, although their empirical results did not indicate that these variables were very important among their sample of Missouri farmers. Nowak and Korsching employed an ad hoc model to test the importance of institutional and attitudinal variables. Their sample of Iowa farmers indicated that risk attitudes, cost sharing, institutional contacts, erosion potential, and farm size have a significant bearing on conservation decisions, while income does not. Forster and Stem, and Napier and Forster have pointed out the potential for internal conflict between conservation-promoting attitudes and affiliations, and profit motives. Lee, Lee and Stewart, and Bilis have investigated land tenure influences with mixed results.
Randomness in crop yields can be decomposed into two broad modeling focuses: the estimation of the mean or central tendency of the distribution and the dispersion around that central tendency. We propose modeling the central tendency of the distribution with a stochastic trend model and allowing for nonnonnality within the stochastic trend through an inverse hyperbolic sine distribution. Results are consistent with this construction. First, residuals around the stochastic trend model are found to be non normal. Second, the inverse hyperbolic sine modification of the stochastic trend model corrects both skewness and kurtosis of corn yields.Key words: inverse hyperbolic sine, random trend, transformation of random variables.Distributions of commodity prices and yields are critical components of firm-level decision analysis. Farm planning and sectoral policy models incorporating risk typically require distributions of yields and prices consistent with the period of analysis. In multi-period analysis, interest centers not only on selecting the appropriate probability density function but also on determining its location over time. Deaton and Laroque recently have considered time-series models of commodity prices which attempt to account for price volatility. Here we consider time-series models of yields and attempt to account for their nonnormality.The fact that yields may not be normally distributed in the relevant production range has already been recognized. Day suggested that distributions of field crop yields in the Mississippi delta had negative skewness. Nelson and Preckel found com distributions to be negatively skewed given average fertilizer use, and Gallagher reported that soybean yields are negatively skewed. Given the possibility of nonnormality, several modelling approaches have been suggested. perbolic tangent function be transformed into a cumulative distribution function which, upon differentiation, would result in a probability density function capable of representing yields. FLIPSIM includes a procedure to simulate yields based on a discrete distribution function (Richardson and Nixon). Although estimated errors are retained for simulation, the FLIPS 1M approach does not link estimation of the distribution's central tendency to the empirical probability density function.The manner in which yields change over time affects estimation and inference (Swinton and King) and nonstationarity (Kaylen and Koroma). Outliers can exert considerable influence on regression parameters and nonnormally distributed residuals will distort standard inferential procedures. On the other hand, if yields are difference-stationary, regressions specifying time as a regressor can give spurious parameter estimates (Nelson and Kang).Our purpose is to incorporate the possibility of both nonstationary data and nonnormal errors into a single time-dependent model of yield variation. A standard stochastic trend model is employed in conjunction with a transformation to mitigate effects of outliers and induce normality. Unlike previo...
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