Using text from 200 million pages of 13,000 US local newspapers and machine learning methods, we construct a 170-year-long measure of economic sentiment at the country and state levels, that expands existing measures in both the time series (by more than a century) and the cross-section. Our...

After the Covid-shock in March 2020, stock prices declined abruptly, reflecting both the deterioration of investors' expectations of economic activity as well as the surge in aggregate risk aversion. In the following months however, whereas economic activity remained sluggish, equity markets...

Is shareholder interest in corporate social responsibility driven by pecuniary motives (abnormal rates of return) or non-pecuniary ones (willingness to sacrifice returns to address various firm externalities)? To answer this question, we categorize the literature into seven tests: (1) costs of...

We measure investors' short- and long-term stock-return expectations using both options and survey data. These expectations at different horizons reveal what investors think their own short-term expectations will be in the future, or forward return expectations. While contemporaneous short-term...

We theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance--in terms of SDF Sharpe ratio and test asset pricing errors--is improving in model parameterization (or "complexity"). Our empirical findings verify the...

We investigate the event-based geopolitical shocks from the Russian invasion of Ukraine on selected agricultural and energy commodities using daily event-based structural vector autoregression (SVAR). We find that the geopolitical shock affects the markets of wheat (3%) and European natural gas...

Recent research has proposed the state space (88) framework for decomposition of GNP and other economic time series into trend and cycle components, using the Kalman filter. This paper reviews the empirical evidence and suggests that the resulting decomposition may be spurious, just as...

We establish that the recursive, state-space methods of Kalman filtering and smoothing can be used to implement the Doan, Litterman, and Sims (1983) approach to econometric forecast and policy evaluation. Compared with the methods outlined in Doan, Litterman, and Sims, the Kalman algorithms are...

The accuracy of particle filters for nonlinear state-space models crucially depends on the proposal distribution that mutates time t-1 particle values into time t values. In the widely-used bootstrap particle filter, this distribution is generated by the state-transition equation. While...

Money is debt that circulates with no questions asked. Stablecoins are a new form of private money that circulate with many questions asked. We show how stablecoins can maintain a constant price even though they face run risk and pay no interest. Stablecoin holders are indirectly compensated for...