Johanna Saecker

We study heterogeneous firms’ greening investment decisions and the role of competition between early greening investors and non-investors (“laggards”) therein. Empirically we show that firms have a higher propensity to engage in greening investment if they are more productive, less financially constrained and expect positive effects from an economy-wide transformation to climate-neutrality (green transition) on their competitiveness. We incorporate these facts into a dynamic heterogeneous firm model. We show that competition from non-investors keeps aggregate prices and thus idiosyncratic profits low and prevents potential early greening investors from engaging in greening investment. Incorporating expectations about a future green economy with increased competitiveness for early greening investors increases greening investment already today. Furthermore, easing financing constraints by 50% increases the share of greening firms by roughly 20%-points in the early stages of a green transition.

This paper presents and compares Newton-based methods from the applied mathematics literature for solving the matrix quadratic that underlies the recursive solution of linear DSGE models. The methods are compared using nearly 100 different models from the Macroeconomic Model Data Base (MMB) and different parameterizations of the monetary policy rule in the medium-scale New Keynesian model of Smets and Wouters (2007) iteratively. We find that Newton-based methods compare favorably in solving DSGE models, providing higher accuracy as measured by the forward error of the solution at a comparable computation burden. The methods, however, suffer from their inability to guarantee convergence to a particular, e.g. unique stable, solution, but their iterative procedures lend themselves to refining solutions either from different methods or parameterizations.

This paper applies structure preserving doubling methods to solve the matrix quadratic underlying the recursive solution of linear DSGE models. We present and compare two Structure-Preserving Doubling Algorithms ( SDAs) to other competing methods – the QZ method, a Newton algorithm, and an iterative Bernoulli approach – as well as the related cyclic and logarithmic reduction algorithms. Our comparison is completed using nearly 100 different models from the Macroeconomic Model Data Base (MMB) and different parameterizations of the monetary policy rule in the medium scale New Keynesian model of Smets and Wouters (2007) iteratively. We find that both SDAs perform very favorably relative to QZ, with generally more accurate solutions computed in less time. While we collect theoretical convergence results that promise quadratic convergence rates to a unique stable solution, the algorithms may fail to converge when there is a breakdown due to singularity of the coefficient matrices in the recursion. One of the proposed algorithms can overcome this problem by an appropriate (re)initialization. This SDA also performs particular well in refining solutions of different methods or from nearby parameterizations.