Inference on Time Series Nonparametric Conditional Moment Restrictions Using General Sieves

TL;DR

This paper develops a flexible inference method for time series data using nonlinear sieve quasi-likelihood ratios, applicable to complex models like reinforcement learning and nonparametric IV, with proven asymptotic properties.

Contribution

It introduces a novel GN-QLR inference framework for high-dimensional nonlinear functions under conditional moment restrictions, valid for dependent data and applicable to various models.

Findings

Asymptotic Chi-square distribution of the GN-QLR statistic regardless of regularity.

Method effectively handles weakly dependent beta-mixing time series data.

Monte Carlo results demonstrate good finite sample performance.

Abstract

General nonlinear sieve learnings are classes of nonlinear sieves that can approximate nonlinear functions of high dimensional variables much more flexibly than various linear sieves (or series). This paper considers general nonlinear sieve quasi-likelihood ratio (GN-QLR) based inference on expectation functionals of time series data, where the functionals of interest are based on some nonparametric function that satisfy conditional moment restrictions and are learned using multilayer neural networks. While the asymptotic normality of the estimated functionals depends on some unknown Riesz representer of the functional space, we show that the optimally weighted GN-QLR statistic is asymptotically Chi-square distributed, regardless whether the expectation functional is regular (root-$n$ estimable) or not. This holds when the data are weakly dependent beta-mixing condition. We apply our method to the off-policy evaluation in reinforcement learning, by formulating the Bellman equation into the conditional moment restriction framework, so that we can make inference about the state-specific value functional using the proposed GN-QLR method with time series data. In addition, estimating the averaged partial means and averaged partial derivatives of nonparametric instrumental variables and quantile IV models are also presented as leading examples. Finally, a Monte Carlo study shows the finite sample performance of the procedure