Footnote:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments June 23, 2023 erstellt
Description:
This paper considers a multi-dimensional panel data model with multilevel factors when the numbers of cross-sections and time observations are large. We develop a multilevel iterative principal component (MIPC) method for estimation by iteratively updating between the slope coefficients and factors, given one another. Under a finite number of blocks, our approach is able to produce consistent estimates of the slope coefficients, factors, and loadings. We also propose a model selection criteria based on the eigenvalue ratios to determine the numbers of factors. Given consistent factor estimates from each block, we apply the generalised canonical correlation (GCC) estimation to separately identifying the global and local factors. We show the consistency of our estimates and establish the asymptotic normality of the bias-corrected estimator for the slope coefficients. The Monte Carlo simulation demonstrates good finite sample performance of MIPC compared to IPC in the presence of multilevel factor structure. In an empirical application, our model is applied to an analysis of the energy consumption and economic growth nexus using a cross-country panel data categorised by regions