Unemployment and Durable Goods Sales
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This research examines the relationship between durable goods sales and the unemployment rate. During the past year in the United States, the unemployment rate has improved (Council of Economic Advisers, 1994, p. 12). Nevertheless, durable goods sales have yet to regain strength as had been anticipated (Council of Economic Advisers, 1994, pp. 17-18, 20-21). Managers of a major appliance chain are concerned about inventory levels. Understocking is undesirable because sales may be missed if products are not available when customers want to buy (Kotler, 1993, p. 543). Conversely, however, overstocking is equally undesirable because of the exposure to excess inventory expenses. A decision was made to analyze the macroeconomic aggregate data to determine the extent to which durable goods sales may be expected to change in relation to changes in the unemployment rate for persons 20 years old and older. The results of this analysis will provide the managers of the appliance chain with information that will improve the quality of their decisions related to inventory stocking level decisions.The principal statistical analysis procedure which will be used in assessing the relationship between changes in durable goods sales and changes in the unemployment rate is simple regression analysis (Summers, Peters, and Armstrong, 1993, p. 295). Data included in the analysis will be for the 1970-1993 time period on an annual basis. Additionally, however,
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past data which may have little relevance to the future time periods being projected. Unfortunately, the determination of how many prior time periods to include in a moving average trend determination is, essentially, a judgment call.
Both simple regression analysis and multiple regression analysis may be applied in the curve fitting process for moving average projections. As the number of past time periods included in a moving average projection is increased, the forecast produced becomes closer to the forecast produced through the use of arithmetic trend projection; thus, most projections relying on the moving average use only a fraction of the total available data.
The simple regression formula for the determination of a trend through the use of the moving average approach is the same as that for the determination of the arithmetic trend--y = a + bx. The difference in the two approaches lies in the definition of b. In the arithmetic mean approach, b is the change in the variable value per time unit, which is determined for each prior time unit by comparing the value the dependent variable of one time unit with the value of the dependent variable in the immediately preceding time unit. In the equation, for the moving ave
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Some common words found in the essay are:
Peters Armstrong, DESCRIPTION DATA, RESULTS CONCLUSION, Unemployment Rate, Economic Advisers, EMPIRICAL TESTS, moving average, , durable sales, unemployment rate, basic data, dependent variable, regression analysis, Kotler Philip, Charles Armstrong, variable value, simple regression, durable lagged, lagged period, durable lagged period, unemployment rate persons, rate persons 20, statistical analyses research, value dependent variable, Unemployment Rates,
Approximate Word count = 1375
Approximate Pages = 6 (250 words per page)
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