Search Results for author:
Found 5 papers, 0 papers with code
Bias correction and uniform inference for the quantile density function
For the kernel estimator of the quantile density function (the derivative of the quantile function), I show how to perform the boundary bias correction, establish the rate of strong uniform consistency of the bias-corrected estimator, and construct the confidence bands that are asymptotically exact uniformly over the entire domain $[0, 1]$.
Efficient counterfactual estimation in semiparametric discrete choice models: a note on Chiong, Hsieh, and Shum (2017)
I suggest an enhancement of the procedure of Chiong, Hsieh, and Shum (2017) for calculating bounds on counterfactual demand in semiparametric discrete choice models.
Nonparametric inference on counterfactuals in first-price auctions
In a classical model of the first-price sealed-bid auction with independent private values, we develop nonparametric estimation and inference procedures for a class of policy-relevant metrics, such as total expected surplus and expected revenue under counterfactual reserve prices.
Bias correction for quantile regression estimators
We derive a higher-order stochastic expansion of these estimators using empirical process theory.
A Uniform Bound on the Operator Norm of Sub-Gaussian Random Matrices and Its Applications
For an $N \times T$ random matrix $X(\beta)$ with weakly dependent uniformly sub-Gaussian entries $x_{it}(\beta)$ that may depend on a possibly infinite-dimensional parameter $\beta\in \mathbf{B}$, we obtain a uniform bound on its operator norm of the form $\mathbb{E} \sup_{\beta \in \mathbf{B}} ||X(\beta)|| \leq CK \left(\sqrt{\max(N, T)} + \gamma_2(\mathbf{B}, d_\mathbf{B})\right)$, where $C$ is an absolute constant, $K$ controls the tail behavior of (the increments of) $x_{it}(\cdot)$, and $\gamma_2(\mathbf{B}, d_\mathbf{B})$ is Talagrand's functional, a measure of multi-scale complexity of the metric space $(\mathbf{B}, d_\mathbf{B})$.
