logo

Time Series Analysis for VAR Model and Engle-Granger Test

Solve two exercises on time series analysis using a VAR model with a specific information set.

11 Pages1401 Words415 Views
   

Added on  2023-06-16

About This Document

This article explains the process of time series analysis for VAR model and Engle-Granger test. It covers the identification of unit root, stationary points, and cointegration. It also discusses the transmission mechanism of monetary policy and the impact of growth rate, inflation rate, and policy interest rate. The article is relevant for students studying economics, finance, and related courses.

Time Series Analysis for VAR Model and Engle-Granger Test

Solve two exercises on time series analysis using a VAR model with a specific information set.

   Added on 2023-06-16

ShareRelated Documents
Running head: TIME SERIES ANALYSIS 1
Time Series Analysis
Name:
Institution:
Time Series Analysis for VAR Model and Engle-Granger Test_1
TIME SERIES ANALYSIS 2
Time Series Analysis
Question 1
To test the VAR model, it is important to consider which variable needs to be considered for the
model. First, the unit root and stationary points must be identified. Then time series plots are
constructed in separate graphs as shown in picture 1to know if there is any trend or even a drift.
Having done the modeling of the transmission mechanism of the monetary policy as shown in
picture 1, it can be observed that there is no trend and that there is also no drift and also that the
mean is around 0 (refer to picture 1). Next, an Augmented Dickey-Fuller test is performed in
efforts to identify the presence of unit root as shown in picture 2. Therefore, as seen in picture 2,
all the p-values move to zero (0); therefore, rejecting the null hypothesis in favor of the
alternative hypothesis which holds that there is unit root. Thus, there are three roots in the VAR
model. Furthermore, in order to clarify this, the KPSS test can be used to confirm as shown in
picture 3.
Next, an estimation of the VAR model with single lag is computed. Thus, having worked out
this, it is clear that the VAR model need to be constructed with 1 lag as shown on table 1;
therefore, running this model, offers 3 models including growth rate (gdp), inflation rate (infl) as
well as policy interest rate (rate). This is given below:
Model 7: Heteroskedasticity-corrected, using observations 1973:01-2006:04 (T = 400)
Dependent variable: gdp
Coefficient Std. Error t-ratio p-value
const −0.00226586 0.0250289 −0.0905 0.9279
infl −0.0776565 0.0595692 −1.3036 0.1931
rate 0.520195 0.0605574 8.5901 <0.0001 ***
Time Series Analysis for VAR Model and Engle-Granger Test_2
TIME SERIES ANALYSIS 3
Statistics based on the weighted data:
Sum squared resid 1481.686 S.E. of regression 1.931892
R-squared 0.203055 Adjusted R-squared 0.199040
F(2, 397) 50.57601 P-value(F) 2.71e-20
Log-likelihood −829.4696 Akaike criterion 1664.939
Schwarz criterion 1676.914 Hannan-Quinn 1669.681
rho 0.481165 Durbin-Watson 1.037508
Statistics based on the original data:
Mean dependent var −0.015256 S.D. dependent var 0.561590
Sum squared resid 99.55367 S.E. of regression 0.500764
Model 8: Heteroskedasticity-corrected, using observations 1973:01-2006:04 (T = 400)
Dependent variable: infl
Coefficient Std. Error t-ratio p-value
const −0.00082813
1
0.0199054 −0.0416 0.9668
rate 0.603397 0.0428899 14.0685 <0.0001 ***
gdp −0.0435722 0.0407155 −1.0702 0.2852
Statistics based on the weighted data:
Sum squared resid 1654.746 S.E. of regression 2.041599
R-squared 0.363629 Adjusted R-squared 0.360423
F(2, 397) 113.4250 P-value(F) 1.09e-39
Log-likelihood −851.5631 Akaike criterion 1709.126
Schwarz criterion 1721.101 Hannan-Quinn 1713.868
rho 0.285452 Durbin-Watson 1.429081
Statistics based on the original data:
Mean dependent var −0.014763 S.D. dependent var 0.503304
Sum squared resid 61.49732 S.E. of regression 0.393580
Model 9: Heteroskedasticity-corrected, using observations 1973:01-2006:04 (T = 400)
Dependent variable: rate
Coefficient Std. Error t-ratio p-value
const −0.0100434 0.0190961 −0.5259 0.5992
gdp 0.295415 0.0340808 8.6681 <0.0001 ***
infl 0.592345 0.0451036 13.1330 <0.0001 ***
Statistics based on the weighted data:
Sum squared resid 1624.072 S.E. of regression 2.022588
R-squared 0.442160 Adjusted R-squared 0.439350
Time Series Analysis for VAR Model and Engle-Granger Test_3
TIME SERIES ANALYSIS 4
F(2, 397) 157.3367 P-value(F) 4.81e-51
Log-likelihood −847.8209 Akaike criterion 1701.642
Schwarz criterion 1713.616 Hannan-Quinn 1706.384
rho 0.507999 Durbin-Watson 0.984011
Statistics based on the original data:
Mean dependent var −0.024936 S.D. dependent var 0.535993
Sum squared resid 58.80561 S.E. of regression 0.384870
It is evident that there are three statistically significant factors that are considered in the
transmission mechanism of the monetary policy and the same logic also applies to the other two
variables, including growth rate (gdp), inflation rate (infl) as well as policy interest rate (rate),
with both growth rate, inflation and rate of interest having statistically important negative and
positive signs.
Question 2
Using the engle-Granger the three variables growth rate (gdp), inflation rate (infl) as well as
policy interest rate (rate) can be modeled to test for counteraction. As shown in the following
figure, the p-values of these three variables are greater than (0.05) and that uhat equals to zero
(0).
Time Series Analysis for VAR Model and Engle-Granger Test_4

End of preview

Want to access all the pages? Upload your documents or become a member.

Related Documents
Applied Finance with Eviews : Assignment - Desklib
|31
|5859
|216

Forecasting Nike Quarterly Revenue with Regression Analysis
|14
|1538
|318

Data Presentation and Hypotheses Testing for Banks in Nigeria
|12
|2086
|475

Probability of Chi Square Statistics
|17
|3311
|188

Econ241 Major Assignment: Linear Model
|15
|2173
|42

Total panel observations: Variable Coefficient t-Statistic
|7
|1148
|117