Graduate Thesis Or Dissertation
 

A comparison of box-cox and additive exponential models to estimate the depreciation of farm machinery

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/d504rn83s

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  • Farm machinery continues to increase in its importance to the agricultural sector. Depreciation, the decline in value of a durable asset over time, represents one of the largest costs of agricultural production. The general objectives of this study were to update and expand the number of Remaining Value (RV) functions for farm machinery; estimate and compare the depreciation patterns for Box-Cox (B-C) and Additive- Exponential (A-E) functional forms; and compare the predictive abilities of the two models. Data used in estimation were obtained from 15 years of machinery auction sales. Based on the hedonic approach, the Box-Cox and Additive-Exponential models were formulated to include variables for age, usage per year, condition, manufacturer, auction type and macroeconomic variables. Models were tested for potential correlation and heteroscedasticity problems. A Mean Absolute Percentage Error (MAPE) method was used to compare the predictive abilities. Depreciation functions were estimated for 17 types of machinery in four major categories: tractors, harvesting equipments, planting and tillage equipment and other equipment. A series of comparisons were conducted to examine the difference in depreciation patterns within and between major categories. B-C and A-E functions were estimated and compared for each data set. The comparison results showed that B-C always generated higher Log-likelihood values, but was relatively weak in predictive ability. The predictive ability of the Exponential model was also examined and the MAPE results showed that the Exponential model generally exhibited an overall best predictive ability.
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