dLagM v1.0.19


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Time Series Regression Models with Distributed Lag Models

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) <doi:10.1007/978-3-642-20059-5> for more information.



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You can install the latest version from CRAN.

install.packages('dLagM', dependencies = TRUE)


This package is free and open source software, licensed under GPL-3.

Functions in dLagM

Name Description
finiteDLMauto Find the optimal lag length for finite DLMs
forecast Compute forecasts for distributed lag models
ardlBoundOrders Find optimal orders (lag structure) for ARDL bounds test
dlm Implement finite distributed lag model
koyckDlm Implement distributed lag models with Koyck transformation
dLagM-package Implementation of Time Series Regression Models with Distributed Lag Models
polyDlm Implement finite polynomial distributed lag model
ardlDlm Implement finite autoregressive distributed lag model
GoF Compute goodness-of-fit measures for DLMs
wheat World wheat production, CO2 emissions, and temperature anomalies data
ardlBound Implement ARDL bounds test
sdPercentiles Test the significance of signal from rolling correlation analysis
rolCorPlot PLot the rolling correlations
sortScore Sort AIC, BIC, MASE, MAPE, sMAPE, MRAE, GMRAE, or MBRAE scores
warming Global warming and vehicle production data
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Type Package
Date 2019-10-23
License GPL-3
RoxygenNote 6.1.1
NeedsCompilation no
Repository CRAN
Packaged 2019-10-23 12:09:27 UTC; haydardemirhan
Date/Publication 2019-10-23 14:30:11 UTC

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