Economic data analysis - Essay Example

The situation is likely to be very different if we are dealing with time series data, for the observations in such data follow a natural ordering over time so that successive observations are likely to exhibit inter correlations, especially if the time interval between successive observations is short, such as a day, a week, or a month rather than. The classical model assumes that the disturbance term relating to any observation is not influenced by the disturbance term relating to any other observation.

For example, if we are dealing with quarterly time series data involving the regression of output on labor and capital inputs and there is labor strike affecting output in one quarter, there is no reason to believe that this disruption will be carried over to the next quarter. That is, if output is lower this quarter, there is no reason to expect it to be lower next quarter. Similarly, if we are dealing with cross-sectional data involving regression of family consumption expenditure on family income, the effect of an increase of one family's income on its consumption expenditure is not expected to affect the consumption expenditure of another family.

If such dependence exists there exists autocorrelation. Symbolically, In this situation, the disruption caused by a strike this quarte. The situation is likely to be very different if we are dealing with time series data, for the observations in such data follow a natural ordering over time so that successive observations are likely to exhibit inter correlations, especially if the time interval between successive observations is short, such as a day, a week, or a month rather than. c) What do you understand by the term 'autocorrelation' What implications will this have for the properties of ordinary least squaresThe term 'autocorrelation' can be defined as "correlation between members of series of observations ordered in time [as in time series data] or space [as in cross-sectional data]"In the regression context, the classical linear regression model assumes that such autocorrelati8on does not exist in the disturbances ut.

Symbolically,E(ut1, ut2)=0 t1#t2The classical model assumes that the disturbance term relating to any observation is not influenced by the disturbance term relating to any other observation. For example, if we are dealing with quarterly time series data involving the regression of output on labor and capital inputs and there is labor strike affecting output in one quarter, there is no reason to believe that this disruption will be carried over to the next quarter. That is, if output is lower this quarter, there is no reason to expect it to be lower next quarter.

Similarly, if we are dealing with cross-sectional data involving regression of family consumption expenditure on family income, the effect of an increase of one family's income on its consumption expenditure is not expected to affect the consumption expenditure of another family. If such dependence exists there exists autocorrelation. Symbolically,E(ut1, ut2)#0 t1#t2In this