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SCENARIO 16-9 Given Below Are EXCEL Outputs for Various Estimated Autoregressive Models

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SCENARIO 16-9
Given below are EXCEL outputs for various estimated autoregressive models for a company's real operating revenues (in billions of dollars) from 1989 to 2012.From the data,you also know that the real operating revenues for 2010,2011,and 2012 are 11.7909,11.7757 and 11.5537,respectively.
First-Order Autoregressive Model:
SCENARIO 16-9 Given below are EXCEL outputs for various estimated autoregressive models for a company's real operating revenues (in billions of dollars) from 1989 to 2012.From the data,you also know that the real operating revenues for 2010,2011,and 2012 are 11.7909,11.7757 and 11.5537,respectively. First-Order Autoregressive Model:    Second-Order Autoregressive Model:     -Referring to Scenario 16-9 and using a 5% level of significance,what is the appropriate autoregressive model for the company's real operating revenue? A) First-Order Autoregressive Model B) Second-Order Autoregressive C) Third-Order Autoregressive D) Any of the above Second-Order Autoregressive Model:
SCENARIO 16-9 Given below are EXCEL outputs for various estimated autoregressive models for a company's real operating revenues (in billions of dollars) from 1989 to 2012.From the data,you also know that the real operating revenues for 2010,2011,and 2012 are 11.7909,11.7757 and 11.5537,respectively. First-Order Autoregressive Model:    Second-Order Autoregressive Model:     -Referring to Scenario 16-9 and using a 5% level of significance,what is the appropriate autoregressive model for the company's real operating revenue? A) First-Order Autoregressive Model B) Second-Order Autoregressive C) Third-Order Autoregressive D) Any of the above
-Referring to Scenario 16-9 and using a 5% level of significance,what is the appropriate autoregressive model for the company's real operating revenue?


Definitions:

Forecast Error

The variance between what actually happens and the forecasts from prediction models.

Expected Value

Expected value is a concept in probability that calculates the average outcome when the future involves scenarios that may or may not happen.

Moving Average Forecast

A method used in time series analysis to smooth out short-term fluctuations and highlight longer-term trends or cycles.

Observable Trend

A pattern, change, or movement in data or events that can be detected and analyzed through observation.

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