"Heterogeneous Autoregressions in Short T Panel Data Models", by M. Hashem Pesaran and Liying Yang, forthcoming in Journal of Applied Econometrics, July 2024, Cambridge Working Papers in Economics, CWPE2342

Abstract: This paper considers a first-order autoregressive panel data model with individual specific effects and heterogeneous autoregressive coefficients defined on the interval (-1, 1], thus allowing for some of the individual processes to have unit roots. It proposes estimators for the moments of the cross-sectional distribution of the autoregressive (AR) coefficients, assuming a random coefficient model for the autoregressive coefficients without imposing any restrictions on the fixed effects. It is shown the standard generalized method of moments estimators obtained under homogeneous slopes are biased. Small sample properties of the proposed estimators are investigated by Monte Carlo experiments and compared with a number of alternatives, both under homogeneous and heterogeneous slopes. It is found that a simple moment estimator of the mean of heterogeneous AR coefficients performs very well even for moderate sample sizes, but to reliably estimate the variance of AR coefficients much larger samples are required. It is also required that the true value of this variance is not too close to zero. The utility of the heterogeneous approach is illustrated in the case of earnings dynamics.
JEL Classifications: C22, C23, C46
Key Words: Heterogeneous dynamic panels, neglected heterogeneity bias, short T panels, earnings dynamics
Full Text: https://www.econ.cam.ac.uk/research-files/repec/cam/pdf/cwpe2342.pdf
Arxiv Link: https://arxiv.org/abs/2306.05299
Codes and Data: https://github.com/LiyingYang2023/HetroPanelAR

"High-Dimensional Forecasting with Known Knowns and Known Unknowns", by M. Hashem Pesaran and Ron P. Smith, forthcoming in The National Institute Economic Review, March 2024, Cambridge Working Papers in Economics, CWPE2406

Abstract: Forecasts play a central role in decision making under uncertainty. After a brief review of the general issues, this paper considers ways of using high-dimensional data in forecasting. We consider selecting variables from a known active set, known knowns, using Lasso and OCMT, and approximating unobserved latent factors, known unknowns, by various means. This combines both sparse and dense approaches to forecasting. We demonstrate the various issues involved in variable selection in a high-dimensional setting with an application to forecasting UK inflation at different horizons over the period 2020q1-2023q1. This application shows both the power of parsimonious models and the importance of allowing for global variables.
JEL Classifications: C53, C55, E37, E52
Key Words: Forecasting, high-dimensional data, Lasso, OCMT, latent factors, principal components
Full Text: https://www.econ.cam.ac.uk/research-files/repec/cam/pdf/cwpe2406.pdf
Replication Files: https://www.econ.cam.ac.uk/people-files/emeritus/mhp1/fp24/Replication_files_for_Pesaran_Smith_NIER_4_April_2024.zip
arXiv link: https://arxiv.org/abs/2401.14582

"Pooled Bewley Estimator of Long Run Relationships in Dynamic Heterogenous Panels", by Alexander Chudik, M. Hashem Pesaran, and Ron P. Smith, forthcoming in Econometrics and Statistics, October 2023

Abstract: Using a transformation of the autoregressive distributed lag model due to Bewley, a novel pooled Bewley (PB) estimator of long-run coefficients for dynamic panels with heterogeneous short-run dynamics is proposed. The PB estimator is directly comparable to the widely used Pooled Mean Group (PMG) estimator, and is shown to be consistent and asymptotically normal. Monte Carlo simulations show good small sample performance of PB compared to the existing estimators in the literature, namely PMG, panel dynamic OLS (PDOLS), and panel fully-modified OLS (FMOLS). Application of two bias-correction methods and a bootstrapping of critical values to conduct inference robust to cross-sectional dependence of errors are also considered. The utility of the PB estimator is illustrated in an empirical application to the aggregate consumption function.
JEL Classifications: C12, C13, C23, C33
Key Words: Heterogeneous dynamic panels; I(1) regressors; pooled mean group estimator (PMG), Autoregressive-Distributed Lag model (ARDL), Bewley transform, PDOLS, FMOLS, bias correction, robust inference, cross-sectional dependence.
Full Text: http://www.econ.cam.ac.uk/people-files/emeritus/mhp1/fp23/CPSPooledBewley30Oct2023.pdf