2026-10-14T00:14:42Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33054697 2026-09-22T15:53:18Z category_7 category_12 category_21 category_272 category_39 category_45 category_811 portal_87 item_type_3 month_year_09_2026

Asymptotic Distributions of Nonlinear Least Squares Estimators for Panel-MIDAS Models Min Lin (17652) Han Lin (393647) Medicine Cell Biology Biotechnology Environmental Sciences not elsewhere classified Ecology Sociology Mathematical Sciences not elsewhere classified Autocorrelation Heteroscedasticity Mixed data sampling Panel data model Supremum-type Wald test <p>The Mixed Data Sampling (MIDAS) approach provides a flexible framework for incorporating mixed-frequency data into econometric models. In this article, we introduce the MIDAS framework into a panel data model and derive the asymptotic properties of the NLS estimator under a joint asymptotic framework where both <i>N</i> and <i>T</i> tend to infinity, allowing for heteroscedasticity and arbitrary correlation within each individual. To facilitate valid inference, we construct robust heteroscedasticity- and autocorrelation-consistent (HAC) covariance estimators. We also analyze the asymptotic bias and variance of the LS estimator based on pre-specified aggregation weights. In addition, we propose a Wald test for the null hypothesis of equal weights and a supremum-type Wald test to address the unidentified nuisance parameter problem. The theoretical results are evaluated via an extensive Monte Carlo simulation study and an empirical application.</p> 2026-09-22T15:53:18Z Dataset Dataset 10.6084/m9.figshare.33054697.v2 https://figshare.com/articles/dataset/Asymptotic_Distributions_of_Nonlinear_Least_Squares_Estimators_for_Panel-MIDAS_Models/33054697 CC BY 4.0