koyckDlm: Implement distributed lag models with Koyck transformation

Description Usage Arguments Details Value Author(s) References Examples

View source: R/koyckDlm.R

Description

A function that applies distributed lag models with Koyck transformation with one predictor.

Usage

1
koyckDlm(x , y)

Arguments

x

A vector including the observations of predictor time series. This is not restricted to ts objects.

y

A vector including the observations of dependent time series. This is not restricted to ts objects.

Details

To deal with infinite DLMs, we can use the Koyck transformation. When we apply Koyck transformation, we get the following:

Y_{t} - φ Y_{t-1} = α (1-φ)+β X_{t} + (ε_{t}-φ ε_{t-1}).

When we solve this equation for Y_{t}, we obtain Koyck DLM as follows:

Y_{t} = δ_{1} + δ_{2} Y_{t-1} + δ_{3} X_{t} + ν_{t},

where δ_{1} = α (1-φ),δ_{2}=φ,δ_{3}=β and the random error after the transformation is ν_{t}=(ε_{t}-φ ε_{t-1}) (Judge and Griffiths, 2000).

Then, instrumental variables estimation is employed to fit the model.

The standard function summary() prints model summary for the model of interest.

Value

model

An object of class ivreg. See the details of ivreg function.

geometric.coefficients

A vector composed of corresponding geometric distributed lag model coefficients.

Author(s)

Haydar Demirhan

Maintainer: Haydar Demirhan <[email protected]t.edu.au>

References

B.H. Baltagi. Econometrics, Fifth Ed. Springer, 2011.

R.C. Hill, W.E. Griffiths, G.G. Judge. Undergraduate Econometrics. Wiley, 2000.

Examples

1
2
3
4
data(warming)
model.koyck = koyckDlm(x = warming$NoMotorVehicles , 
                       y = warming$Warming)
summary(model.koyck)                       

Example output

Loading required package: dynlm
Loading required package: zoo

Attaching package: 'zoo'

The following objects are masked from 'package:base':

    as.Date, as.Date.numeric

Loading required package: wavethresh
Loading required package: MASS
WaveThresh: R wavelet software, release 4.6.8, installed

Copyright Guy Nason and others 1993-2016

Note: nlevels has been renamed to nlevelsWT

Loading required package: AER
Loading required package: car
Loading required package: lmtest
Loading required package: sandwich
Loading required package: survival
Loading required package: Hmisc
Loading required package: lattice
Loading required package: Formula
Loading required package: ggplot2

Attaching package: 'Hmisc'

The following objects are masked from 'package:base':

    format.pval, round.POSIXt, trunc.POSIXt, units


Call:
ivreg(formula = y.t ~ Y.t_1 + X.t | Y.t_1 + X.t_1)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.135894 -0.090056  0.003193  0.076844  0.143812 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept) 0.060814   0.136314   0.446   0.6615  
Y.t_1       0.239589   0.331208   0.723   0.4799  
X.t         0.006074   0.002814   2.158   0.0464 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.09509 on 16 degrees of freedom
Multiple R-Squared: 0.5459,	Adjusted R-squared: 0.4891 
Wald test: 9.655 on 2 and 16 DF,  p-value: 0.001777 

                              alpha        beta       phi
Geometric coefficients:  0.07997527 0.006074464 0.2395892
                       Length Class      Mode
model                  19     ivreg      list
geometric.coefficients  3     data.frame list
call                    3     -none-     call

dLagM documentation built on Oct. 22, 2018, 5:08 p.m.