Journal of Economic Dynamics & Control最新文献

Women account for a small share of all business owners and a small share of the market in India's manufacturing sector. To account for these patterns, I estimate the extent of gender-specific distortions to operating a business using firm-level data. Feeding these estimates that differ across gender into a standard framework of heterogeneous producers replicates key features of the firm size distribution, on aggregate and across gender. While women face high entry barriers into entrepreneurship, they have modest impacts on female market shares when there are sharp differences in distortions across gender along the intensive margin of entrepreneurship. Policies that promote female entrepreneurship are effective, yet have only modest impacts on aggregate productivity. These findings are not unique to India, and apply across a broader set of countries.

We propose a pseudospectral method to solve heterogeneous-agent models in continuous time. The solution is approximated as a sum of smooth global basis functions, in our case polynomials represented by their values at Chebyshev nodes. We illustrate the method by applying it to a Krusell-Smith model. It solves the differential equations characterizing the steady-state efficiently and precisely, despite using only very few nodes. System dynamics are then automatically differentiated to simulate a linearized model. The full solution takes a third of a second and only uses standard software. A benchmark against finite differences shows that pseudospectral methods achieve far greater precision for a given number of nodes and for a given runtime. We conclude by discussing the methods' applicability, which is promising for smooth multi-dimensional models.

We show how the classical Lagrangian approach to solving constrained optimization problems from standard calculus can be extended to solve continuous time stochastic optimal control problems. Connections to mainstream approaches such as the Hamilton-Jacobi-Bellman equation and the stochastic maximum principle are drawn. Our approach is linked to the stochastic maximum principle, but more direct and tied to the classical Lagrangian principle, avoiding the use of backward stochastic differential equations in its formulation. Using infinite dimensional functional analysis, we formalize and extend the approach first outlined in Chow (1992) within a rigorous mathematical setting using infinite dimensional functional analysis. We provide examples that demonstrate the usefulness and effectiveness of our approach in practice. Further, we demonstrate the potential for numerical applications facilitating some of our key equations in combination with Monte Carlo backward simulation and linear regression, therefore illustrating a completely different and new avenue for the numerical application of Chow's methods.

This paper explores a novel mechanism through which transitions to a low-carbon economy can proceed smoothly without excessive disinvestment in carbon-intensive capital. The mechanism is analyzed in a Lucas-Uzawa green growth model with carbon-temperature dynamics. Due to the externalities associated with climate damages and learning by doing, insufficient resources are allocated towards investment in clean capital in the business-as-usual market economy. Without green subsidies to stimulate clean capital investment, pricing emissions to internalize the social cost of carbon causes disinvestment in carbon-intensive capital and increases the costs of low-carbon transitions. Pricing emissions and subsidizing clean investment yield a higher return on clean capital and boost clean capital accumulation. This curbs disinvestment in carbon-intensive capital and limits carbon emissions. This highlights the positive role of clean capital for smoothing low-carbon transitions.

The real option value theory posits that non-convex adjustment costs pertaining to a firm's input are central to comprehending the consequences of increased uncertainty. This paper leverages the diversity observed at both sectoral and country levels in the degree of irreversibility associated with hiring and firing, a critical factor generating what is commonly referred to as “wait-and-see” behavior in times of heightened uncertainty. Our empirical findings reveal two key insights. First, in alignment with the concept of second-moment shocks, uncertainty shocks predominantly influence the labor market through the extensive margin rather than the intensive margin. Second, the effects of uncertainty shocks exhibit pronounced heterogeneity across countries and industries, and the adverse employment effects (extensive margin) are amplified in a country with strict employment protection or in an industry characterized by a higher natural layoff rate, consistent with the real option theory.

Artificial Neural Networks (ANNs) are powerful tools that can solve dynamic programming problems arising in economics. In this context, estimating ANN parameters involves minimizing a loss function based on the model's stochastic functional equations. In general, the expectations appearing in the loss function admit no closed-form solution, so numerical approximation techniques must be used. In this paper, I analyze a bias-corrected Monte Carlo operator (bc-MC) that approximates expectations by Monte Carlo. I show that the bc-MC operator is a generalization of the all-in-one expectation operator, already proposed in the literature. I demonstrate that, under some conditions on the primitives of the economic model, the bc-MC operator is the unbiased estimator of the loss function with the minimum variance. I propose a method to optimally set the hyperparameters defining the bc-MC operator, and illustrate the findings numerically with well-known economic models. I also demonstrate that the bc-MC operator can scale to high-dimensional models. With just approximately a minute of computing time, I find a global solution to an economic model with a kink in the decision function and more than 100 dimensions.

We propose a statistical identification procedure for recursive structural vector autoregressive (VAR) models that present a nonlinear dependence (at least) at the contemporaneous level. By applying and adapting results from the literature on causal discovery with continuous additive noise models, we show that, under certain conditions, a large class of structural VAR models is identifiable. We spell out these specific conditions and propose a scheme for the estimation of structural impulse response functions in a nonlinear setting. We assess the performance of this scheme in a simulation experiment. Finally, we apply it in a study on the effects of the macroeconomic shocks that propagate through the economy, allowing for asymmetry between responses from positive and negative impulses.

What are the cross-border spillovers from major economies' quantitative easing policies to their trading partners? We provide evidence by concentrating on spillovers from the US to Canada during the ZLB period when QE policies were actively used. We identify QE shocks in the US and estimate their impact on a large number of Canadian macroeconomic and financial variables. Then we analyze transmission channels of foreign QE shocks to the domestic economy. Our results suggest that US QE shocks are expansionary for Canada despite a currency appreciation. This is because they spill over to domestic borrowing costs, lowering long-term rates as well as financial premiums, and increasing asset prices. We find evidence for both portfolio balance and risk channels.

The quantity theory of money hypothesizes that the price level is determined through the equilibration of money supply and demand. Predicated on this causal structure, a single-equation error correction model decomposes from a larger vector autoregressive system so as to make available bounds tests for a levels relationship that are robust to the univariate integration properties of the variables. This model is estimated using three monetary aggregates and two money demand specifications, for U.S. and U.K. annual data over the past century and quarterly post-WWII data. The classic homogeneity propositions of the quantity theory are testable, and are found to be most compatible with U.S. annual M2 using log-log money demand with structural change permitted. Nevertheless, the resulting welfare costs are similar to those yielded by the U.K. annual data, being less than one percent of GDP at interest rates experienced during the past century.

The socioeconomic impact of pollution naturally comes with uncertainty due to, e.g., current new technological developments in emissions' abatement or demographic changes. On top of that, the trend of the future costs of the environmental damage is unknown: Will global warming dominate or technological advancements prevail? The truth is that we do not know which scenario will be realised and the scientific debate is still open. This paper captures those two layers of uncertainty by developing a real-options-like model in which a decision maker aims at adopting a once-and-for-all costly reduction in the current emissions rate, when the stochastic dynamics of the socioeconomic costs of pollution are subject to Brownian shocks and the drift is an unobservable random variable. By keeping track of the actual evolution of the costs, the decision maker is able to learn the unknown drift and to form a posterior dynamic belief of its true value. The resulting decision maker's timing problem boils down to a truly two-dimensional optimal stopping problem which we address via probabilistic free-boundary methods and a state-space transformation. We completely characterise the solution by showing that the optimal timing for implementing the emissions reduction policy is the first time that the learning process has become “decisive” enough; that is, when it exceeds a time-dependent percentage. This is given in terms of an endogenously determined threshold function, which solves uniquely a nonlinear integral equation. We numerically illustrate our results, discuss the implications of the optimal policy and also perform comparative statics to understand the role of the relevant model's parameters in the optimal policy.