Economic data analysis Essay Example | Topics and Well Written Essays - 1250 words

frugal data analysis - Essay ExampleThe situation is likely to be very different if we ar dealing with condemnation serial data, for the observations in such(prenominal) data dramatize a natural order over time so that successive observations atomic number 18 likely to testify 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 fracas term relating to any observation is not influenced by the hinderance term relating to any other observation. For example, if we argon dealing with quarterly time series data involving the retrogression toward the mean of output on labor and capital inputs and there is labor strike affecting output in adept quarter, there is no reason to believe that this disruption will be carried over to the undermenti onenessd quarter. That is, if output is lower this quarter, there is no reason to expect it to be lower succeedin g(prenominal) quarter. Similarly, if we are dealing with cross-section(a) data involving regression of family consumption expenditure on family income, the effect of an increase of one familys 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 collapse for the properties of ordinary least squaresThe term autocorrelation can be defined as correlation between member s of series of observations ordered in time as in time series data or space as in cross-sectional dataIn the regression context, the classical linear regression model assumes that such autocorrelati8on does not exist in the disturbances ut. Symbolically,E(ut1, ut2)=0 t1t2The 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 familys income on its consu mption expenditure is not expected to affect the consumption expenditure of another family. If such dependence exists there exists autocorrelation. Symbolically,E(ut1, ut2)0 t1t2In this