U.S. Macroeconomic Policy Evaluation in an Open Economy Context Using Wavelet Decomposed Optimal Control Methods

Patrick M. Crowley, David Hudgins
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引用次数: 3

Abstract

It is widely recognized that the policy objectives of fiscal and monetary policymakers usually have different time horizons, and this feature may not be captured by traditional econometric techniques. In this paper, we first decompose U.S macroeconomic data using a time-frequency domain technique, namely discrete wavelet analysis. We then model the behavior of the U.S. economy over each wavelet frequency range and use our estimated parameters to construct a tracking model. To illustrate the usefulness of this approach, we simulate jointly optimal fiscal and monetary policy with different short-term targets: an inflation target, a money growth target, an interest rate target, and a real exchange rate target. The results determine the reaction in fiscal and monetary policy that is required to achieve an inflation target in a low inflation environment, and when both fiscal and monetary policy are concerned with meeting certain economic growth objectives. The combination of wavelet decomposition in an optimal control framework can also provide a new approach to macroeconomic forecasting.
基于小波分解最优控制方法的开放经济背景下美国宏观经济政策评价
人们普遍认识到,财政和货币政策制定者的政策目标通常具有不同的时间范围,而传统的计量经济学技术可能无法捕捉到这一特征。在本文中,我们首先使用时频域技术分解美国宏观经济数据,即离散小波分析。然后,我们在每个小波频率范围内对美国经济的行为进行建模,并使用我们估计的参数来构建跟踪模型。为了说明这种方法的有效性,我们用不同的短期目标来模拟联合最优财政和货币政策:通胀目标、货币增长目标、利率目标和实际汇率目标。结果决定了在低通胀环境下,当财政和货币政策都与实现某些经济增长目标有关时,实现通胀目标所需的财政和货币政策的反应。在最优控制框架中结合小波分解也为宏观经济预测提供了一种新的方法。
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