This paper proposes an empirical model of a discrete choice demand with a nonparametric income effect specification. The model allows for the flexible estimation of demand curvature, which has significant implications for pricing and policy analysis in oligopolistic markets. We adopt a sieve...

In the context of high-frequency financial data it is often assumed that sampling times are exogenous. This entails that financial asset prices, sampled on a grid of trade instants, are independent from the sampling times. We derive statistical tests capable of determining whether or not, and to...

This paper proposes a novel testing procedure for selecting a sparse set of covariates that explains a large dimensional panel. Our selection method provides correct false detection control while having higher power than existing approaches. We develop the inferential theory for large panels...

We decompose the Pearson correlation coefficient into two components. We recommend the first component for detecting linear relationships and the second for recognizing patterns of two parallel lines, providing robust versions to outliers. The significance of the Pearson coefficient without...

The Taliban's takeover of Afghanistan in August 2021 is associated with a rapid collapse of the Afghan economy. In lieu of official data, attempts to measure the scale of the collapse have relied on rapid surveys of the population. We validate these qualitative measures by using nightlights as a...

In the current accelerated process of global warming, forest conservation is becoming more difficult to address in developing countries, where woodlands are often fueling the illegal economy. In Colombia, the issue of narcodeforestation is of great concern, because of the ramification of...

The model-free e-backtesting method was recently introduced by Wang et al. (2022) to monitor the performance of risk measure forecasts. In order to provide more practical illustration and insights, this paper demonstrates detailed simulation and data analysis results on backtesting the...

In this paper, we develop a novel high-dimensional time-varying coefficient estimation method, based on high-dimensional Ito diffusion processes. To account for high-dimensional time-varying coefficients, we first estimate local (or instantaneous) coefficients using a time-localized Dantzig...

This paper derives a new decomposition of stock returns using price extremes and proposes a conditional autoregressive shape (CARS) model with beta density to predict the direction of stock returns. The CARS model is continuously valued, which makes it different from binary classification...

Covariate benchmarking is an important part of sensitivity analysis about omitted variable bias and can be used to bound the strength of the unobserved confounder using information and judgments about observed covariates. It is common to carry out formal covariate benchmarking under the...