计量经济分析 = Econometric analysis

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

The structure

Начала политической экономии и податного обложения = The principles of political economy and taxation

﻿LEAVING aside the courageous efforts of people around Monthly Review and Science and Society there has, by and large, been hardly any Marxist tradition among American intellectuals. However, during the last few years the situation has considerably changed and there is now a growing interest in Marxism, particularly among the younger social scientists of America. The book under review falls within this trend.

Value and Capital

A method is developed to permit joint estimation of risk preference structure, degree of risk aversion, and production technology. The method is implemented using the Expo-Power utility function, which imposes no restrictions on risk preference structure. The empirical application uses data from a sample of Kansas wheat farmers Evidence rejects the null hypothesis of risk neutrality and suggests that Kansas farmers exhibit decreasing absolute risk aversion and increasing relative risk aversion. Results also show that combined estimation of production function parameters with the utility function parameter is more efficient than is separate estimation of each.

Joint Estimation of Risk Preference Structure and Technology Using Expo-Power Utility

The purpose of this paper is to examine the structure of agricultural production for six Texas field crops (cotton, grain sorghum, wheat, corn, rice, and hay) which account for nearly 95% of recent field crop value. Specific objectives are to: (a) test the structure of technology to determine (i) if output and input utilization choices for individual products are independent of production decisions about other outputs and (ii) if products and/or inputs can be consistently aggregated, (b) estimate a coherent set of interrelated supply functions for the six commodities and input demand functions for fertilizer and hired labor. Because the empirical focus of this paper is specific to one state, the results will be of limited value for interpreting specific production relationships elsewhere. However, the problem is of common interest in production agriculture, and the techniques used demonstrate an important application of dual modeling.

Supply, Demand, and Technology in a Multiproduct Industry: Texas Field Crops

Allocatable fixed inputs, such as land, are a potentially important source of jointness in agriculture. As with other causes of jointness, they necessitate multiple-product systems for modeling product supply and input demand. In other important ways, however, their analytical implications are very different from other causes of jointness. Model specification differs. Demand functions for the quantities of each input used in the production of individual commodities can be derived if a primal approach is used, but such allocation equations cannot in general be identified from a dual specification. Available allocation data are not even useful in such dual estimations.

Allocatable Fixed Inputs and Jointness in Agricultural Production: Implications for Economic Modeling

A dual model is used to examine the dynamic structure of the U.S. dairy industry. Properties implied by the theory of the competitive firm and independent adjustment of two quasi-fixed inputs, labor and herd size, are tested and not rejected. Instantaneous adjustment, however, is soundly rejected for each quasi-fixed input. Input adjustment to optimal levels is estimated to take about two years for labor and ten for cows. Quality adjustments of the labor and cow series do not fully embody the technological change that has occurred in this industry over the study period.

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Dynamic Adjustment in the U.S. Dairy Industry

Concern for the potentially harmful side effects of agricultural chemical inputs, especially pesticides, highlights the need to accurately determine the economic levels of their use. We consider three model specification issues: interaction of direct production inputs with damage control inputs in damage abatement, justification for a priori exclusion of production inputs from the abatement function, and the motivations and consequences of alternative stochastic specifications. Empirical analysis using farm-level data shows that misspecification of the stochastic element in the production function can overestimate the marginal physical productivity of pesticides and grossly underestimate the responsiveness of demand to increases in pesticide prices.

C. Richard Shumway

Papers

Joint Estimation of Risk Preference Structure and Technology Using Expo-Power Utility

Supply, Demand, and Technology in a Multiproduct Industry: Texas Field Crops

Allocatable Fixed Inputs and Jointness in Agricultural Production: Implications for Economic Modeling

Dynamic Adjustment in the U.S. Dairy Industry

The Economics and Econometrics of Damage Control