動態對偶理論：學習成為理性預期

Epstein（1981）與McLaren and Cooper（1980）建立的動態對偶理論已成為分析廠商生產行為與產業相關研究的主要工具；但在經由動態對偶理論推導出對應的最適行為模式時，卻必須對決策者的價格預期做一靜態的假設。文獻中雖有少數研究曾嘗試將動態對偶理論擴展至非靜態預期的架構，但基本上仍假設理性預期的形式，而未對廠商如何形成理性預期以及對偶關係賴以建立的條件如何修正進行討論。本文嘗試將最小平方的學習機能引入動態對偶理論，在廠商透過學習而達到理性預期均衡的架構下，重新評估價值函數的特性，並建立價值函數與生產函數之間的對偶關係。

關鍵字

動態對偶；學習機能；理性預期

並列摘要

This paper is an extension of the dynamic duality theory established by Epstein (1981) and McLaren and Cooper (1980). Applications of the duality theory to dynamic optimization problems provide an efficient means of constructing empirically implementable models. Nevertheless, implications drawn from the dynamic dual models are subject to Lucas’ criticism due to the commonly maintained assumption of static price expectations. Explicitly incorporating the least-squares learning mechanism popularized by Marcet and Sargent (1989a), this paper establishes the duality between the firm’s production technology and the value function under nonstatic price expecations. Complete characterization of the value function is derived to infer dynamic decision rules consistent with the rationale of learning.

並列關鍵字

Dynamic duality ； Least-squares learning ； Rational expectations

參考文獻

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動態對偶理論：學習成為理性預期

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