Provides time series regression models with one predictor using finite distributed lag models, polynomial (Almon) distributed lag models, geometric distributed lag models with Koyck transformation, and autoregressive distributed lag models. It also consists of functions for computation of h-step ahead forecasts from these models. See Baltagi (2011)(10.1007/978-3-642-20059-5) for more information.
Package: | dLagM |
Type: | Package |
Version: | 1.0.19 |
Date: | 2019-10-24 |
License: | GPL-3 |
To implement time series regression with finite distributed lag models, use dlm
function.
To implement time series regression with polynomial distributed lag models, use polyDlm
function.
To implement time series regression with geometric distributed lag models with Koyck transformation, use koyckDlm
function.
To implement time series regression with autoregressive distributed lag models, use ardlDlm
function.
To implement ARDL Bounds test, use ardlBound
function.
To produce forecasts for any of the models, use forecast
function.
To summarise the results of a model fitting, use summary
function.
B.H. Baltagi. Econometrics, Fifth Ed. Springer, 2011.
R.C. Hill, W.E. Griffiths, G.G. Judge. Undergraduate Econometrics. Wiley, 2000.
J. Soren, A.Q. Philips. "pss: Perform bounds test for cointegration and perform dynamic simulations."
P.K. Narayan. The Saving and Investment Nexus for China: Evidence from Cointegration Tests. Applied Economics 37(17):1979-1990, 2005.
M.H. Pesaran, S. Yongcheol, R.J. Smith. Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics 16(3):289-326, 2001.
# NOT RUN {
# --- For examples, please refer to specific functions ---
# }
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