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本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。

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分类依据与纠错

AI 根据所提供的论文材料归类:摘要明确比较季度投资动态模型,并纳入预期调查与机械预测方案。

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关注这篇论文的作者
Dale W. Jorgenson
Jerald Hunter
M. Ishag Nadiri

季度投资行为的替代计量模型比较

研究问题
论文研究不同季度投资模型在同一组美国制造业行业数据上的拟合与误差相关表现,并与投资预期调查及机械预测方案比较。
方法与思路
作者将四种投资计量模型拟合于共同数据,以拟合优度和误差无自相关作为比较标准,同时考察预期调查与机械预测方法。
材料陈述的贡献
摘要报告模型与调查、机械方案的拟合差异,并强调投资过程的时间结构和决定因素设定同时影响表现,难据此区分机制。
阅读关注点
应核对样本时期、动态设定与自相关诊断,并区分样本内拟合和样本外预测证据,检查不同模型是否使用相同信息集。
阅读边界
仅据所给 OpenAlex 摘要,不能判断样本外预测能力或投资机制的因果解释;作者也明确指出时间结构与决定因素难以分别评价。

原文摘要

In this paper four alternative quarterly econometric models of investment behavior are fitted to a common set of data for individual manufacturing industries in the United States. Goodness of fit and absence of autocorrelation of errors are used as a basis for comparison of the performance of the alternative models. The econometric models are compared with each other and with alternative explanations of data on investment based on surveys of anticipated investment and on mechanical forecasting schemes. The four econometric models included in our study are those of Anderson [2], Eisner [15], Jorgenson and Stephenson [38], and Meyer and Glauber [46]. On the basis of our comparison, the ranking of the alternative models is as follows: (1) Jorgenson-Stephenson, (2) Eisner, (3) Meyer-Glauber, (4) Anderson. Anticipatory data give a better fit to data on investment expenditures than that provided by any of the econometric models. Mechanical forecasting schemes provide a fit that is superior to the Anderson and Meyer-Glauber models. These schemes are slightly inferior to the Eisner model and clearly inferior to the Jorgenson-Stephenson model. The alternative econometric models included in our comparison differ in specification of the time structure of the investment process and in the role ascribed to specific determinants of investment behavior. Both aspects of an econometric model affect its performance so that it is difficult to discriminate among alternative determinants of investment behavior on the basis of our results.

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