We nowcast world trade using machine learning, distinguishing between tree-based methods (random forest, gradient boosting) and their regression-based counterparts (macroeconomic random forest, gradient linear boosting). While much less used in the literature, the latter are found to outperform...

We find evidence suggesting that surveys of professional forecasters are biased by strategic incentives. First, we find that individual forecasts overreact to idiosyncratic information but underreact to common information. Second, we show that this bias is not present in forecasts data that is...

We propose a new tool to filter non-linear dynamic models that does not require the researcher to specify the model fully and can be implemented without solving the model. If two conditions are satisfied, we can use a flexible statistical model and a known measurement equation to back out the...

This paper measures option-implied skewness for individual firms and the overall stock market between 1980 and 2021, giving real-time measures of conditional micro and macro skewness. There are three key results: 1. Micro skewness is significantly procyclical, while macro skewness is acyclical;...

We present evidence that the high estimated MPCs from the leading household studies result in implausible macroeconomic counterfactuals. Using the 2008 tax rebate as a case study, we calibrate a standard medium-scale New Keynesian model with the estimated micro MPCs to construct counterfactual...

Macroeconomics has increasingly adopted tools from the applied micro "credibility revolution" to estimate micro parameters that can inform macro questions. In this paper, we argue that researchers should take advantage of this confluence of micro and macro to take the credibility revolution one...

We propose a general simulation-based procedure for estimating quality of approximate policies in heterogeneous-agent equilibrium models, which allows to verify that such approximate solutions describe a near-rational equilibrium. Our procedure endows agents with superior knowledge of the future...

There have been important methodological changes in the Consumer Price Index (CPI) over time. These distort comparisons of inflation from different periods, which have become more prevalent as inflation has risen to 40-year highs. To better contextualize the current run-up in inflation, this...

We introduce a novel approach to solving dynamic programming problems, such as those in many economic models, on a quantum annealer, a specialized device that performs combinatorial optimization. Quantum annealers attempt to solve an NP-hard problem by starting in a quantum superposition of all...

Professional house price forecast data are consistent with a rational model where agents must learn about the parameters of the house price growth process and the underlying state of the housing market. Slow learning about the long-run mean can generate forecast bias, a response of forecasts to...