C61 - Optimization Techniques; Programming Models; Dynamic Analysis
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Quantum Monte Carlo for Economics: Stress Testing and Macroeconomic Deep Learning
Using the quantum Monte Carlo algorithm, we study whether quantum computing can improve the run time of economic applications and challenges in doing so. We apply the algorithm to two models: a stress testing bank model and a DSGE model solved with deep learning. We also present innovations in the algorithm and benchmark it to classical Monte Carlo. -
Technology Adoption in Input-Output Networks
We study how input-output networks affect the speed of technology adoption. In particular, we model the decision to adopt the programming language Python 3 by software packages. Python 3 provides advanced features but is not backward compatible with Python 2, which implies it comes with adoption costs. -
Firm-level Investment Under Imperfect Capital Markets in Ukraine
This paper develops and estimates a model of firm-level fixed capital investment when firms face borrowing constraints. -
Should Central Banks Worry About Nonlinearities of their Large-Scale Macroeconomic Models?
How wrong could policymakers be when using linearized solutions to their macroeconomic models instead of nonlinear global solutions? -
Housing and Tax-Deferred Retirement Accounts
Assets in tax-deferred retirement accounts (TDA) and housing are two major components of household portfolios. In this paper, we develop a life-cycle model to examine the interaction between households’ use of TDA and their housing decisions. -
Cash Management and Payment Choices: A Simulation Model with International Comparisons
Despite various payment innovations, today, cash is still heavily used to pay for low-value purchases. This paper develops a simulation model to test whether standard implications of the theory on cash management and payment choices can explain the use of payment instruments by transaction size. -
Optimization in a Simulation Setting: Use of Function Approximation in Debt Strategy Analysis
The stochastic simulation model suggested by Bolder (2003) for the analysis of the federal government's debt-management strategy provides a wide variety of useful information. It does not, however, assist in determining an optimal debt-management strategy for the government in its current form. -
Conditioning Information and Variance Bounds on Pricing Kernels with Higher-Order Moments: Theory and Evidence
The author develops a strategy for utilizing higher moments and conditioning information efficiently, and hence improves on the variance bounds computed by Hansen and Jagannathan (1991, the HJ bound) and Gallant, Hansen, and Tauchen (1990, the GHT bound). -
The Stochastic Discount Factor: Extending the Volatility Bound and a New Approach to Portfolio Selection with Higher-Order Moments
The authors extend the well-known Hansen and Jagannathan (HJ) volatility bound. HJ characterize the lower bound on the volatility of any admissible stochastic discount factor (SDF) that prices correctly a set of primitive asset returns.