EconometricsAutoCorrelation (ISS (Statistical Services) Statistics Paper III): Questions 1  5 of 5
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Question number: 1
» Econometrics » AutoCorrelation
Appeared in Year: 2015
Describe in Detail
Describe exact and near multicollinearity in a regression model. Discuss, along with justification, the effect of these situations on (i) sampling variation and (ii) prediction.
Explanation
Multicollinearity refers to the existence of a perfect or exact linear relationship among some or all explanatory variables of a regression model. So, there are two types of multicollinearity. They are
1. Exact Multicollinearity: If exist perfect linear relationship among the explanatory variables then it is treated as exact multicollinearity.
Question number: 2
» Econometrics » AutoCorrelation
Appeared in Year: 2009
Describe in Detail
Define multicollinearity. What are the causes of multicollinearity? How will you detect the same and solve it?
Explanation
Multicollinearity: Implies predictors that are correlated with other predictors in the model. Here two or more predictor variables in a multiple regression model are highly correlated and implies that one can be linearly predicted from the others with a substantial degree of accuracy.
Causes of multicollinearity:

Inaccurate use of dummy
Question number: 3
» Econometrics » AutoCorrelation
Appeared in Year: 2011
Describe in Detail
Discuss the effect of imperfect multicollinearity on tests and errors. Consider a model
with
and with
, ,
and
Do you think multicollinearity is present in the model? If yes, give reasons. Further show that as
increases, also increases.
Explanation
Effect of imperfect multicollinearity on tests and errors:

OLS estimators will be BLUE, but they have large variances and covariances. this makes the estimation difficult.

The confidence intervals will be wider. and hence, we may not reject the ’zero null hypothesis i. e. , We may conclude that B’s are
Question number: 4
» Econometrics » AutoCorrelation
Appeared in Year: 2014
Describe in Detail
Describe the problem of ‘multicollinearity’ in econometrics and explain how will you detect it.
Explanation
Multicollinearity may arise for various reasons, they are:
There is a tendency of economic variables to move together over time. In time series data, growth and trend factors are the main causes for multicollinearity problem. For example, in period of booms or rapid economic growth the basic economic magnitudes grow.
Question number: 5
» Econometrics » AutoCorrelation
Appeared in Year: 2014
Describe in Detail
Explain the term ‘autocorrelation’. What are the consequences of autocorrelation? Explain how the Durbin Watson d statistic is used to detect the presence of autocorrelation.
Explanation
Autocorrelation:
Autocorrelation refers to the correlation of a time series with its own past and future values.
It is a measurement of the degree of similarity between a given time series and a lagged version of the same time series.
It can be considered as a special case of correlation,