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Full-Text Articles in Social and Behavioral Sciences
Conditional Superior Predictive Ability, Jia Li, Zhipeng Liao, Rogier Quaedvlieg
Conditional Superior Predictive Ability, Jia Li, Zhipeng Liao, Rogier Quaedvlieg
Research Collection School Of Economics
This article proposes a test for the conditional superior predictive ability (CSPA) of a family of forecasting methods with respect to a benchmark. The test is functional in nature: under the null hypothesis, the benchmark’s conditional expected loss is no more than those of the competitors, uniformly across all conditioning states. By inverting the CSPA tests for a set of benchmarks, we obtain confidence sets for the uniformly most superior method. The econometric inference pertains to testing conditional moment inequalities for time series data with general serial dependence, and we justify its asymptotic validity using a uniform non-parametric inference method …
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 …
Forecast Combinations In Machine Learning, Yue Qiu, Tian Xie, Jun Yu
Forecast Combinations In Machine Learning, Yue Qiu, Tian Xie, Jun Yu
Research Collection School Of Economics
This paper introduces novel methods to combine forecasts made by machine learning techniques. Machine learning methods have found many successful applications in predicting the response variable. However, they ignore model uncertainty when the relationship between the response variable and the predictors is nonlinear. To further improve the forecasting performance, we propose a general framework to combine multiple forecasts from machine learning techniques. Simulation studies show that the proposed machine-learning-based forecast combinations work well. In empirical applications to forecast key macroeconomic and financial variables, we find that the proposed methods can produce more accurate forecasts than individual machine learning techniques and …