Research

Working Papers

We develop uniform inference for high-dimensional threshold regression parameters and valid inference for the threshold parameter in this paper. We first establish oracle inequalities for prediction errors and ℓ1 estimation errors for the Lasso estimator of the slope parameters and the threshold parameter, allowing for heteroskedastic non-subgaussian error terms and non-subgaussian covariates. Next, we derive the asymptotic distribution of tests involving an increasing number of slope parameters by debiasing (or desparsifying) the scaled Lasso estimator. The asymptotic distribution of tests without the threshold effect is identical to that with a fixed effect. Moreover, we perform valid inference for the threshold parameter using subsampling method. Finally, we conduct simulation studies to demonstrate the performance of our method in finite samples.

Supplementary Material

This paper investigates the extent of market integration and exchange rate pass-through, as well as market factors that may be associated with deviations from perfect market integration and pass-through. To address the shortcomings of existing models of spatial market integration, we employ procedures outlined in Yan(2023) for inference and model selection, utilizing the desparsified LASSO method for high-dimensional threshold regression. Our results support the integration of global corn markets, especially when accounting for the existence of thresholds. We identify significant relationships among several variables representing domestic and world economic conditions.

Working in progress