In this paper we study the finite sample and asymptotic properties of various weighting estimators of the local average treatment effect (LATE), several of which are based on Abadie (2003)’s kappa theorem. Our framework presumes a binary endogenous explanatory variable (“treatment”) and a...

This paper proposes a linear categorical random coefficient model, in which the random coefficients follow parametric categorical distributions. The distributional parameters are identified based on a linear recurrence structure of moments of the random coefficients. A Generalized Method of...

We improve on the Instrumented Principal Component Analysis (IPCA) model developed in Kelly, Pruitt and Su (2019) by providing more efficient Generalized Least Square (GLS) estimators with a closed-form limiting distribution allowing for a more consistent (mis)pricing inference. The IPCA model...

We propose a data-driven methodology to shrink the covariance matrix using reinforcement learning (RL). Our approach does not impose any assumption about the stock returns and can be applied to any covariance matrix target/prior. We focus on the special case of the global minimum variance...

Credit Value Adjustment sensitivities represent the quantitative basis for the full spectrum of steering activities of CVA desks: hedging, control, explanation and forecasting. In realistic applications, typically involving large numbers of market risk-factors, calculation of CVA Greeks poses...

The aim of this paper is to study the symmetry properties of solutions of bi-harmonic differential equations of the type Δ𝟐+||-| 𝜶 𝒖 = 𝟎 and Δ𝟐 𝒖 + 𝒇(𝒖) = 𝟎 𝒊𝒏 𝛀 ⊂ 𝑹𝟐 We employ the method of moving planes, which is based on the Maximum principles in...

This paper develops an efficient estimation procedure for time-varying parameter autoregressive models with stochastic volatility. Necessary restrictions are imposed on the time-varying autoregressive parameters, thus stability conditions are satisfied. We show that a conditional Gaussian...

In this paper, we propose an intersection-union test for multivariate forecast accuracy based on the combination of a sequence of univariate tests. The testing framework evaluates a global null hypothesis of equal predictive ability using any number of univariate forecast accuracy tests under...

We develop novel forecasting methods for panel data with heterogeneous parameters and examine them together with existing approaches. We conduct a systematic comparison of their predictive accuracy in settings with different cross-sectional (N) and time (T) dimensions and varying degrees of...