Seasonal adjustment procedures attempt to estimate the sample realizations of an unobservable economic time series in the presence of both seasonal factors and irregular factors. In this paper we consider a factor which has not been considered explicitly in previous treatments of seasonal...

Here's why. (1) The HP filter produces series with spurious dynamic relations that have no basis in the underlying data-generating process. (2) Filtered values at the end of the sample are very different from those in the middle, and are also characterized by spurious dynamics. (3) A statistical...

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...

In the 2018 comprehensive update of the national income and product accounts, the Bureau of Economic Analysis released not seasonally adjusted data, and modified its seasonal adjustment procedures. I find some indication of residual seasonality in the seasonally adjusted data as published before...

We study the usefulness of root tests as diagnostic tools for selecting forecasting models. Difference stationary and trend stationary models of economic and financial time series often imply very different predictions, so deciding which model to use is tremendously important for applied...

Traffic fatalities are the leading cause of mortality in the United States despite being preventable. While several policies have been introduced to improve traffic safety and their effects have been well documented, the role of transitory health shocks or situational factors at explaining...

We develop a tractable framework to study the optimal design of stress scenarios. A principal wants to manage the unknown risk exposures of a set of agents. She asks the agents to report their losses under hypothetical scenarios before mandating actions to mitigate the exposures. We show how to...

Macroeconomic news announcements are elaborate and multi-dimensional. We consider a framework in which jumps in asset prices around macroeconomic news and monetary policy announcements reflect both the response to observed surprises in headline numbers and latent factors, reflecting other...

This paper examines the correlated random coefficient model. It extends the analysis of Swamy (1971, 1974), who pioneered the uncorrelated random coefficient model in economics. We develop the properties of the correlated random coefficient model and derive a new representation of the variance...

We propose a simple nonparametric mixtures estimator for recovering the joint distribution of parameter heterogeneity in economic models, such as the random coefficients logit. The estimator is based on linear regression subject to linear inequality constraints, and is robust, easy to program...