Fiscal Incentives and Firm Investment Dynamics: Structural Model Meets Quasi-random Experiments
Abstract
Canonical models of rm investment used in Macro and Productivity literature often rely ona rich set of adjustment frictions. Previous quantitative studies illustrate that the adjustment
frictions are crucial to account for some of the salient features of the micro-level data, such as
the investment's lumpiness, serial correlation, and elasticity to productivity. However, most of
these studies focus on matching the data in a stationary environment. They seldom use the model
estimates to predict the rm-level changes in a quasi-random experiment. Meanwhile, with the
increasing availability of rm-level investment and tax data, there has been an emerging literature
that now quantify rm response to scal policy changes utilizing quasi-random variations in tax
regimes. Our paper aims to bridge these two approaches and provide a unied estimation framework
that embeds the information from both sides.