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On Measuring the Effects of Fiscal Policy in Recessions

机译:衡量经济衰退中财政政策的效果

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We do not have a good measure of the effects of fiscal policy in a recession because the methods that we use to estimate the effects of fiscal policy-both those using the observed outcomes following different policies in aggregate data and those studying counter)actuals in fitted model economies-almost entirely ignore the state of the economy and estimate "the" government multiplier, which is presumably a weighted average of the one we care about-the multiplier in a recession-and one we care less about-the multiplier in an expansion. Notable exceptions to this general claim suggest this difference is potentially large. Our lack of knowledge stems significantly from the focus on linear dynamics: vector autoregressions and linearized (or close-to-linear) dynamic stochastic general equilibrium (DSGE) models. Our lack of knowledge also reflects a lack of data: deep recessions are few and nonlinearities hard to measure. The lack of statistical power in the estimation of nonlinear models using aggregate data can be addressed by exploiting estimates of partial-equilibrium responses in disaggregated data. Microeconomic estimates of the partial-equilibrium causal effects of a policy can discipline the causal channels inherent in any DSGE model of the general equilibrium effects of policy. Microeconomic studies can also provide measures of the dependence of the effects of a policy on the states of different agents, which is a key component of the dependence of the general-equilibrium effects of fiscal policy on the state of the economy.
机译:我们无法很好地衡量经济衰退期间的财政政策效果,因为我们用来估算财政政策效果的方法-既使用汇总数据中遵循不同政策观察到的结果的方法,又包括研究反事实的方法。模型经济体-几乎完全忽略了经济状况,并估计“政府”乘数,它可能是我们关注的乘数(在衰退中乘数,而我们不在乎的乘数)是扩张中的乘数的加权平均值。该一般性声明的显着例外表明,这种差异可能很大。我们对知识的缺乏主要源于对线性动力学的关注:矢量自回归和线性化(或接近线性)的动态随机一般均衡(DSGE)模型。我们知识的匮乏也反映出数据的匮乏:严重的经济衰退很少,而非线性则难以衡量。可以通过利用分类数据中的部分平衡响应的估计值来解决使用集合数据的非线性模型估计中缺乏统计能力的问题。对政策的部分均衡因果效应的微观经济估算可以约束任何政策的一般均衡效应的DSGE模型中固有的因果通道。微观经济学研究还可以提供衡量政策对不同主体状态的依赖关系的度量,这是财政政策对经济状态对一般均衡效应的依赖的关键组成部分。

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