BaumC. F.HurnS.. 2021. Environmental Econometrics Using Stata. College Station, TX: Stata Press.
2.
De VosI.EveraertG.RuyssenI.. 2015. Bootstrap-based bias correction and inference for dynamic panels with fixed effects. Stata Journal15: 986–1018. https://doi.org/10.1177/1536867X1501500404.
DitzenJ.2021. Estimating long-run effects and the exponent of cross-sectional dependence: An update to xtdcce2. Stata Journal21: 687–707. https://doi.org/10.1177/1536867X211045560.
5.
JordanS.PhilipsA. Q.. 2018. Cointegration testing and dynamic simulations of autoregressive distributed lag models. Stata Journal18: 902–923. https://doi.org/10.1177/1536867X1801800409.
6.
KripfganzS.2016. Quasi–maximum likelihood estimation of linear dynamic short-T panel-data models. Stata Journal16: 1013–1038. https://doi.org/10.1177/1536867X1601600411.
7.
KripfganzS.SarafidisV.. 2021. Instrumental-variable estimation of large-T paneldata models with common factors. Stata Journal21: 659–686. https://doi.org/10.1177/1536867X211045558.
8.
KripfganzS.SchneiderD. C.. Forthcoming. ardl: Estimating autoregressive distributed lag and equilibrium correction models. Stata Journal.
9.
PesaranM. H.ShinY.SmithR. J.. 2001. Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics16: 289–326. https://doi.org/10.1002/jae.616.
WilliamsR.AllisonP. D.Moral-BenitoE.. 2018. Linear dynamic panel-data estimation using maximum likelihood and structural equation modeling. Stata Journal18: 293–326. https://doi.org/10.1177/1536867X1801800201.