Problems and Questions

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 Required 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 off 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. Ill. A. Why are manufacturers’ new orders, endogens 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 compacts 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, Seibel issues that may arise in the future, and information about the target demographic. V. One of the most difficult tasks in regression analysis is to obtain the data suitable for quantitative studies of this kind.
Suppose you are trying to estimate the demand for home furniture. Suggest kinds of variables that could be used to represent the following factors, which are believed to affect the demand for any product. Do you anticipate any difficulty in securing such data? Explain. Determinants of Demand for Furniture Suggested Variables to use in Regression Analysis Price Prices set for furniture at competing companies Tastes & Preferences % of people who like modern, rustic, traditional, contemporary, country, etc. Hypes of furniture Price of related products Price of accent Items (blinds, pillows, rugs) Income Average Income of buyers Cost or availability of credit % of people who purchase furniture with cash or credit Number of buyers # of sales per year Future expectations Availability of products, future income of buyers Other possible factors Seasonal sales I do not see any problem securing this data. Most of this Information can be maker of a leading brand of low-calorie microwaveable food estimated the following emend equation for its product using data from 26 supermarkets around the country for the month of April.