Journal of Economic Dynamics & Control最新文献

New firms are often based on ideas developed within incumbent firms. We study spinoff activities in a growth model with entry and product quality innovation. Spinoffs increase aggregate productivity through product variety expansion and, if created voluntarily by incumbents, boost their return to equity. However, they erode incumbents' market share and, when stemming from conflicts between incumbents and employees, raise incumbents' internal cost of capital. The analysis reveals that a priori investment protection has an ambiguous impact on spinoff activities, depending on whether it focuses on incumbents' product quality investments or the creation of voluntary spinoffs. The calibrated model indicates that a broad investment protection reform reduces the spinoff rate but boosts incumbents' product improvement, raising income growth and welfare. The trade-offs are consistent with firm-level evidence from Italy.

We propose a test for time-varying impulse responses in heteroskedastic structural vector autoregressions that can be used when the shocks are identified by external proxy variables as a group but not necessarily individually. The test is robust to the identification scheme for identifying the shocks individually and can be used even if the shocks are not identified individually. The asymptotic analysis is supported by small sample simulations which show good properties of the test. An investigation of the impact of productivity shocks in a small macroeconomic model for the U.S. illustrates the importance of the issue for empirical work.

We propose a modeling approach to disentangle how idiosyncratic and aggregate shocks shape the impact of credit default swaps (CDSs) on CDS firms' financial decisions. Our relatively parsimonious model highlights a novel mechanism contributing to CDS procyclicality. We show that CDSs postpone debt renegotiation and risk-taking investment. CDS firms have higher leverage ratios than non-CDS firms. CDS firms' leverage and credit spreads are counter-cyclical. CDS firms' debt overhang is less significant than non-CDS firms. CDSs can increase or decrease CDS firms' value, depending on macroeconomic conditions, idiosyncratic risk, and borrowers' bargaining power. Empirical studies verify some model predictions.

We study the impact on the workings of the wheat commodity market of increasing weather variability, one of the direct consequences of climate change. After finding strong evidence of an increase in the variance of weather and harvest for wheat in the US, we develop a structural time series model of the commodity market to investigate the sources and consequences of this increased variability. Exploiting this model, we devise a novel empirical procedure to analyze the impact on price and the potential adjustments of the speculative demand for inventories, as predicted by the rational storage theory. We find that speculation in the physical market for wheat at annual frequency adapted to the greater uncertainty about harvest stabilizing the market price.

Simulation models, in particular agent-based models, are gaining popularity in economics and the social sciences. The considerable flexibility they offer, as well as their capacity to reproduce a variety of empirically observed behaviours of complex systems, give them broad appeal, and the increasing availability of cheap computing power has made their use feasible. Yet a widespread adoption in real-world modelling and decision-making scenarios has been hindered by the difficulty of performing parameter estimation for such models. In general, simulation models lack a tractable likelihood function, which precludes a straightforward application of standard statistical inference techniques. A number of recent works have sought to address this problem through the application of likelihood-free inference techniques, in which parameter estimates are determined by performing some form of comparison between the observed data and simulation output. However, these approaches are (a) founded on restrictive assumptions, and/or (b) typically require many hundreds of thousands of simulations. These qualities make them unsuitable for large-scale simulations in economics and the social sciences, and can cast doubt on the validity of these inference methods in such scenarios. In this paper, we investigate the efficacy of two classes of simulation-efficient black-box approximate Bayesian inference methods that have recently drawn significant attention within the probabilistic machine learning community: neural posterior estimation and neural density ratio estimation. We present a number of benchmarking experiments in which we demonstrate that neural network-based black-box methods provide state of the art parameter inference for economic simulation models, and crucially are compatible with generic multivariate or even non-Euclidean time-series data. In addition, we suggest appropriate assessment criteria for use in future benchmarking of approximate Bayesian inference procedures for simulation models in economics and the social sciences.

This paper examines aggregate and distributional implications of automation in an epidemic. Using industry-level and firm-level data from the Chinese manufacturing sector, we document that the COVID-19 pandemic has led to a significant surge in the installation of industrial robots, and during the health crisis firms with more aggressive robot adoption experienced less severe revenue losses and a more pronounced increase in the wage gap between high-skilled and low-skilled workers. We then develop a tractable SIS-based macroeconomic model to explain these observations. The model economy has two steady states, and an outbreak can trigger a regime-switching transition from a disease-free steady state to an epidemic steady state. Accelerated robot adoption in the transition, stemming from labor shortfall and wage inflation, alleviates the loss in output but affects low-skilled labor disproportionately. These results are robust in an extended setting, where workers have an option to work remotely.

Building on recent work in monetary theory and finance, we develop a framework where money serves a double liquidity role, namely, it serves as a medium of exchange in goods markets as well as asset markets. We argue that studying such a framework is not only more empirically relevant, but also gives rise to new, important economic insights regarding the effects of inflation on welfare and asset prices. The main result of the paper is that, contrary to conventional wisdom, in our model welfare can be increasing in inflation due to a new channel whereby higher inflation promotes beneficial trade in the secondary asset market.

We study the Ramsey optimal fiscal and monetary policy in an economy where banks face collateral constraints. Inflation reduces the net worth of banks and tightens their collateral constraint by revaluing their nominal assets and liabilities. The optimal policy balances tax distortions with the costs of inflation on banks, thereby deviating from perfect tax smoothing. Our quantitative analysis reveals that inflation plays a much smaller role in financing fiscal needs in the optimal policy compared to existing literature. When considering price stickiness and long-term government debt, optimal inflation is modest and persistent, and the role of inflation in fiscal financing increases with the maturity of government debt.

This study investigates the functioning of modern payment systems through the lens of banks' maturity mismatch practices, and it examines the effects of banks' refusal to roll over short-term interbank liabilities on financial stability. Within an agent-based stock-flow consistent framework, banks can engage in two segments of the interbank market that differ in maturity, overnight and term. We compare two interbank matching scenarios to assess how bank-specific maturity targets, dependent on the dictates of the Net Stable Funding Ratio, impact the dynamics of the interbank market and the effectiveness of conventional monetary policies. The findings reveal that maturity misalignment between deficit and surplus banks compromises the interbank market's efficiency and increases reliance on the central bank's standing facilities. Monetary policy interest-rate steering practices also become less effective. The study also uncovers a dual stability-based configuration in the banking sector, resembling the segmented European interbank structure. This paper suggests that heterogeneous maturity mismatches between surplus and deficit banks may result in asymmetric funding frictions that might precede credit- and sovereign-risk explanations of interbank tensions. Also, a combined examination of macroprudential tools and rollover-based interbank dynamics can enhance our understanding of how regulatory changes impact the stability of heterogeneous banking sectors.

The dynamic portfolio construction problem requires dynamic modeling of the joint distribution of multivariate stock returns. To achieve this, we propose a dynamic generative factor model which uses random variable transformation as an implicit way of distribution modeling and relies on the Attention-GRU network for dynamic learning and forecasting. The proposed model captures the dynamic dependence among multivariate stock returns, especially focusing on the tail-side properties. We also propose a two-step iterative algorithm to train the model and then predict the time-varying model parameters, including the time-invariant tail parameters. At each investment date, we can easily simulate new samples from the learned generative model, and we further perform CVaR portfolio optimization with the simulated samples to form a dynamic portfolio strategy. The numerical experiment on stock data shows that our model leads to wiser investments that promise higher reward-risk ratios and present lower tail risks.