Question

problem of autocorrelation in regression analysis of time series is overcome using​

Answer 1

Answer:

• Improve model fit. Try to capture structure in the data in the model.
• If no more predictors can be added, include an AR1 model.

Autocorrelation can cause problems in conventional analyses (such as ordinary least squares regression) that assume independence of observations. In a regression analysis, autocorrelation of the regression residuals can also occur if the model is incorrectly specified.

Explanation:

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Answer 2

The problem of autocorrelation in regression analysis of time series is overcome using​ the addition of independent variables and data transformation.

Addition of Independent Variables: When performing a regression analysis, autocorrelation is frequently caused by the exclusion of one or more significant predictor variables. The autocorrelation may be greatly reduced by including this variable in the regression model.

Data transformation: Data transformation may be used to address the issue when the addition of extra variables is ineffective in reducing autocorrelation. The "initial differences approach" is an illustration of converting the variable.

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