Recent rises in macroeconomic volatility have prompted the introduction of quantile vector autoregression (QVAR) models to forecast macroeconomic risk. This paper provides an extensive evaluation of the predictive performance of QVAR models in a pseudo-out-of-sample experiment spanning 112...

There are numerous measures of place-based quality of life, accompanied by ongoing debate over which factors should be included. Past studies have predominately relied on aspatial, subjectively weighted combinations of different place-based characteristics. Little attention has been given to the...

Evaluation metrics are essential in selecting the most appropriate predictive model. Conventional metrics such as R-squared, root mean squared error (RMSE), and mean absolute percentage error (MAPE) rely on arithmetic averages, making them sensitive to outliers, undefined values, and scale...

We propose a new approach to estimating the random coefficient logit demand model for differentiated products when the vector of market-product-level shocks is sparse. Assuming sparsity, we establish nonparametric identification of the distribution of random coefficients and demand shocks under...

We consider structural vector autoregressions that are identified through stochastic volatility. Our analysis focuses on whether a particular structural shock can be identified through heteroskedasticity without imposing any sign or exclusion restrictions. Three contributions emerge from our...

In this paper, we describe an attempt made to develop an efficient disclosure control algorithm for microdata in a statistical portal used for releasing detailed statistical information at various levels of spatial aggregation. The proposed algorithm is based on perturbative methods, such as...