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Time Series Regression: Interpretation and Prediction Exampl

A security analyst specializing in the stocks of the motion picture industry the relation between the number of movie theater tickets sold in December and the annual level of earnings in the motion picture industry.

Time-series data for the last 15 years are used to estimate the regression model. E = a + bN where E is total earnings of the motion picture industry measured in dollars per year and N is the number of tickets sold in December. The regression output is as follows:

DEPENDENT VARIABLE: E
R-SQUARE 0. 8311
F-RATIO 63.96
P-VALUE ON F 0.0001
OBSERVATIONS: 15
Coefficient Standard Error T-Ratio p- value
Intercept 25042000.00 20131000.00 1.24 0.2369
N 32.31 8.54 3.78 0.0023

How well do movie ticket sales in December explain the level of earnings for the entire year? Present statistical evidence to support your answer. Also, sales of movie tickets in December are expected to be approximately 950,000. According to this regression analysis, what do you expect earnings for the year to be? Prior to this analysis, the estimates for earnings in December are $48 million. Is this evidence strong enough for you to consider improving the current recommendation for the motion picture industry?

Solution Summary

This solution provides an example for interpreting a time regression model and using the model for prediction. Detailed explanations of the concepts and meanings of the various characteristics such as R square, t values, F values and their interpretations using p values are provided. The model is used to predict the future values.

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