Briefly explain the meaning of R-squared. A time series analysis of demand tends to result in a higher R-squared than one using cross-sectional data. Why do you think this is the case? R-squared measures the goodness of fit of a regression equation. A time series analysis of demand tends to result in a higher R-squared than one using cross-sectional data because data is being gathered at multiple periods of time as opposed to one period of time when using cross-sectional data. II.
What is the identification problem? What effect will this problem have on the regression estimates of a demand function? Explain. The identification problem occurs when there is an inability in the principle to identify the best estimate of values of one or more variables in regression. This problem effects regression estimates of a demand function because there is a simultaneous shifting of both the supply and demand, which results in biased results. III.
a. Why are manufacturers’ new orders, nondefense capital goods, an appropriate leading indicator? They are an appropriate indicator because they are commitments that show that economic activity will take place in the future. b. Why is the index of industrial production an appropriate coincident indicator? The index of industrial production is an appropriate coincident indicator because it provides information about the current state of the economy. c. Why is the average prime rate charged by banks an appropriate lagging indicator? It’s an appropriate lagging indicator because changes in the prime rate generally trail changes in the rest of the economy. IV.
You have been asked to produce a forecast for your company’s product, bottled water. Discuss the kind of information you would look for in order to make this forecast. An effective forecast for bottled water would include sales revenue, marketing, competition, possible issues that may arise in the future, and information about the target demographic.
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