Performance in Forecasting Quarterly earnings per Share number of Forecasts                            Mean Forecast error                                                             Standard Deviations of                                                                                   (Predicted – actual)                                                                  Forecast errors analyst a              101                                    0.05                                                                                            0.10

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Performance in Forecasting Quarterly earnings per Share

number of Forecasts                            Mean Forecast error                                                             Standard Deviations of               

                                                                   (Predicted – actual)                                                                  Forecast errors

analyst a              101                                    0.05                                                                                            0.10

 analyst b             121                                    0.02                                                                                            0.09                                                                                                                                                                   

Investment analysts often use earnings per share (EPS) forecasts. one test of forecasting quality is the zero-mean test, which states that optimal forecasts should have a mean fore- casting error of 0. (Forecasting error = Predicted value of variable − actual value of variable. you have collected data (shown in the table above) for two analysts who cover two different industries: analyst a covers the telecom industry; analyst b covers automotive parts and suppliers.

  1. with μ as the population mean forecasting error, formulate null and alternative hypotheses for a zero-mean test of forecasting quality.
  2. For analyst a, using both a t-test and a z-test, determine whether to reject the null at the 0.05 and 0.01 levels of significance.
  3. For analyst b, using both a t-test and a z-test, determine whether to reject the null at the 0.05 and 0.01 levels of significance.
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