As historical data are typically unavailable for a start-up, risk assessment is always complex and challenging. Traditional methods are incapable of capturing all facets of this complexity; therefore, more sophisticated tools are necessary. Using an expert-elicited Bayesian networks (BNs)...

We develop a generally applicable full‐information inference method for heterogeneous agent models, combining aggregate time series data and repeated cross‐sections of micro data. To handle unobserved aggregate state variables that affect cross‐sectional distributions, we compute a...

We use a dynamic panel Tobit model with heteroskedasticity to generate forecasts for a large cross‐section of short time series of censored observations. Our fully Bayesian approach allows us to flexibly estimate the cross‐sectional distribution of heterogeneous coefficients and then...

Football (soccer) stocks are substantially subject to investor sentiment stemming from football fields. Evaluating sentiment functions help us understand how investors interpret field signals and attach value to those signals in stock markets. This study develops the Gaussian investor sentiment...

We study whether the response of the economy to structural shocks changes at the zero lower bound. Monte Carlo evidence suggests that VARs have a limited ability to detect changes in impulse response functions at the ZLB compared to the standard environment with positive interest rates. This...