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Full-Text Articles in Econometrics
A General Test For Functional Inequalities, Jia Li, Zhipeng Liao, Wenyu Zhou
A General Test For Functional Inequalities, Jia Li, Zhipeng Liao, Wenyu Zhou
Research Collection School Of Economics
This paper develops a nonparametric test for general functional inequalities that include conditional moment inequalities as a special case. It is shown that the test controls size uniformly over a large class of distributions for observed data, importantly allowing for general forms of time series dependence. New results on uniform growing dimensional Gaussian coupling for general mixingale processes are developed for this purpose, which readily accommodate most applications in economics and finance. The proposed method is applied in a portfolio evaluation context to test for “all-weather” portfolios with uniformly superior conditional Sharpe ratio functions.
Uniform Nonparametric Inference For Spatially Dependent Panel Data: The Xtnpsreg Command, Jia Li, Zhipeng Liao, Wenyu Zhou
Uniform Nonparametric Inference For Spatially Dependent Panel Data: The Xtnpsreg Command, Jia Li, Zhipeng Liao, Wenyu Zhou
Research Collection School Of Economics
In this article, we introduce a command, xtnpsreg, that implements a uniform nonparametric inference procedure for possibly unbalanced panel datasets with general forms of spatio-temporal dependence. We demonstrate how to apply this command in several use cases, including (i) the nonparametric estimation of conditional mean function and its marginal response; (ii) the construction of uniform confidence bands for these nonparametric functional parameters; (iii) specification tests for parametric model restrictions; and (iv) the estimation and uniform inference for functional coefficients in semi-nonparametric models.
Learning Before Testing: A Selective Nonparametric Test For Conditional Moment Restrictions, Jia Li, Zhipeng Liao, Wenyu Zhou
Learning Before Testing: A Selective Nonparametric Test For Conditional Moment Restrictions, Jia Li, Zhipeng Liao, Wenyu Zhou
Research Collection School Of Economics
This paper develops a new test for conditional moment restrictions via nonparametric series regression, with approximating series terms selected by Lasso. Machine-learning the main features of the unknown conditional expectation function beforehand enables the test to seek power in a targeted fashion. The data-driven selection, however, also tends to distort the test’s size nontrivially, because it restricts the (growing-dimensional) score vector in the series regression on a random polytope, and hence, effectively alters the score’s asymptotic normality. A novel critical value is proposed to account for this truncation effect. We establish the size and local power properties of the proposed …