Serial Correlation (Univariate models)

autocorrelation testing but it has one limitation that if there are lags in model, we cannot

use this.

disadvantages. 1. If models comtain lagged variables DW test cannot use for detecting

autocorrelation. 2.It has indesicion area. 3. DW test derermine only first order

autocorrelation

the data series is sufficiently large... about more than 30 0bservations then you can ignore

the violation of normality assumption coz by central limit theorem a long series will

converge to normality.... for auto-correlation and heteroskedasticity we can try first some

transformation in the variables.... however, if that does not work then use Newey-West

test for HC and HAC... This is readily available in R and Stata... Not sure about Eviews....

hope this helps..

best option instead of lag of dependent because it just solves first order serial corelation.

correlation is likely to have understated or overstated standard errors of parameter

estimates. In either case, this can negatively affect our calculated t-values and hence our

test of hypothesis. In summary, it may lead us to draw false conclusion about the

statistical significance or otherwise of parameter estimates. For this reason, I cannot

accept a model with serial correlation.

can take the first difference. What if after taking the first difference there is still

autocorrelation. What should i do?"

(heteroscrdasticity and autocorrelation consistent) standard errors. The option is

Muzammil Bhatti commented>First difference just removes first order auto correlation.

HAC is good option while in time series you should also introduce autoregressive part, in

this way your model not only becomes a dynamic model but also ensures almost no

autocorrelation.

have impact on the current government debt. If government failed to repay back in full

(interest and principal) the debt of last year, surely the outstanding debt will be carried

over to the current year. In accounting, it is termed, balance brought down /forward.

degree of similarity between a given time series and a lagged version of itself over

successive time intervals. It is the same as calculating the correlation between two

different time series, except that the same time series is used twice: once in its original

form and once lagged one or more time periods.

Ade Kutu

Afolabi Luqman

Abdullah Sonnet

Asad Zaman

Atiq Rehman

Burcu Özcan

Ghumro Niaz Hussain

Muhammad Anees

Mohammad Zhafran

Muzammil Bhatti

Monis Syed

Mine PD

Moulana N. Cholovik

Muili Adebayo Hamid

Nicat Gasim

Najid Iqbal

Nasiru Inuwa

Noman Arshed

Rapelanoro Nady

Seye Olasehinde-Williams

Suborno Aditya

Sayed Hossain

Shishir Sakya

Sheikh Muzammil Naseer

Tella Oluwatoba Ibrahim

Younes Azzouz

Hossain Academy Note

Univariate Models |

Multivariate Models |

Panel Data Model |