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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.

This equilibrium model explains the trend in long-term yields and business-cycle movements in short-term yields and yield spreads. The less-frequent inverted yield curves (and less-frequent recessions) after the 1990s are due to recent secular stagnation and procyclical inflation expectations.

Over the last six months, most countries have continued to register strong economic growth.

We study how the distribution of information supply by the news media affects the macroeconomy. We find that media coverage focuses particularly on the largest firms, and that firms’ equity financing and investment increase after media coverage. But these equity and investment responses are largest among small, rarely covered firms. Our quantitative studies highlight that the aggregate effects of media coverage depend crucially on how that coverage is allocated.

We introduce behavioral learning equilibria (BLE) into DSGE models with boundedly rational agents using simple but optimal first order autoregressive forecasting rules. The Smets-Wouters DSGE model with BLE is estimated and fits well with inflation survey expectations. As a policy application, we show that learning requires a lower degree of interest rate smoothing.