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Research on a Baysian Non-Parametric Density Estimation of Production Function Using Micro Data

基于微观数据的生产函数贝叶斯非参数密度估计研究

基本信息

批准号：
08303003
负责人：
WAGO Hijime
金额：
$ 3.07万
依托单位：
Niigata University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
1996
资助国家：
日本
起止时间：
1996 至 1997
项目状态：
已结题

项目摘要

For many economists, it is common practice to use a parametric specification of production functions (PF), such as Cobb-Douglas or Translog production function, when they analyze the production technology. However, these parametric specifications are subject to their own functional biases. In this paper, to partially remedy these shortcomings, we propose Baysian non-parametric estimation method of a PF,as well as analyzing the functional biases of some of the specifications in the CES family.Employing this method, the input space was partitioned into cells, and the production level for each cell was estimated by Baysian method. The assessed production surface was a collection of piece-wise constant "steps" over these cells. In the estimation process, the relationships between the steps have been modeled as a prior information to the model, so that the smoothness of the function is maintained.In this research we used micro data (Census of Manufactures, MITI) in estimating the production … More function. This data set include 22 industries and about 50,000 records in each year.With the census data, surveyed for units of operation, we want to examine the input-output technical relationship (s) for manufacturing industries listed in the Japan Standard Industrial Classification code.We want to estimate the production surface (function ) from the micro data which will affects production technology more properly. Within the knowledge of the authors, this kinds of micro data for manufacturing production and its analysis are rare.We have employed the Baysian non-parametric density estimation (BNDE) method for the data analysis. It is important to note that we do not parameterize the estimating function such as Cobb-Douglas and Translog form.Using the program developed for estimating the density, we estimated the most efficient production surface. From this most efficient production surface, we can analyze the distribution of inefficient production units for each "class" of production, in which the classes are defined based upon the levels of inputs. Less

对于许多经济学家来说，在分析生产技术时，通常使用生产函数（PF）的参数规格，例如Cobb-Douglas或Translog生产函数。然而，这些参数规格受到其自身的功能偏差。针对这些不足，本文提出了一种PF的贝叶斯非参数估计方法，并分析了CES族中部分规格的功能偏差，将输入空间划分为单元，利用贝叶斯方法估计每个单元的生产水平。评估的生产表面是这些单元上的分段恒定“台阶”的集合。在估计过程中，各步骤之间的关系被建模为模型的先验信息，从而保持了函数的光滑性。本研究中，我们使用微观数据（MITI，Census of Manufactures）来估计产量 ...更多信息功能本数据集包含22个产业，每年约5万条记录。本研究通过调查生产单位的普查数据，对日本标准产业分类中的制造业的投入产出技术关系进行研究，从能够更准确地影响生产技术的微观数据中推算生产面（功能）。就作者所知，这类用于制造业生产的微观数据及其分析是罕见的，我们采用贝叶斯非参数密度估计（BNDE）方法进行数据分析。我们没有对Cobb-Douglas和Translog形式的估算函数进行参数化，而是利用开发的密度估算程序，估算了最有效的生产面。从这个最有效率的生产面，我们可以分析每个生产“类”的低效率生产单元的分布，其中类是根据投入水平定义的。少