# Does More Expert Adjustment Associate with Less Accurate Professional Forecasts?

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## Abstract

**:**

## 1. Introduction

## 2. Persistence and Variance of Adjustment

^{,}5, $\mu $ is an intercept and u is an error term. Given the results in Franses (2014), we expect that more adjustment does not associate with better forecast performance, and hence we hypothesize that $\widehat{\alpha}$ and $\widehat{\beta}$ are positive and significant.

## 3. Forecasting Three Key Variables for the USA

## 4. Further Results

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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4 | By relying on the RMSPE measure we basically make an additional assumption and that is that the forecasters work under squared error loss. Extensions to alternative loss functions would be an interesting topic for further research. |

5 | We use the realizations (source: World Bank) of the relevant variables available in May 2019. |

**Table 1.**Estimation results for (1) for forecasting real GDP growth, USA, 1996–2018. Italics means significant at the 10% level.

Forecaster | Parameter Estimates (Standard Errors) for the Variables: | |||
---|---|---|---|---|

$\widehat{\mathit{\rho}}$ | ${\widehat{\mathit{\sigma}}}_{\mathit{\u03f5}}^{2}$ | |||

JP Morgan | −0.487 | (0.270) | 6.948 | (2.060) |

Nat Assn of Homebuilders | −0.097 | (0.368) | 7.331 | (4.162) |

Eaton Corporation | −0.538 | (0.363) | 20.908 | (5.099) |

Ford Motor Corp | 0.089 | (0.445) | 6.059 | (2.064) |

The Conference Board | −0.131 | (0.332) | 9.192 | (3.709) |

General Motors | −0.253 | (0.501) | 5.437 | (2.913) |

DuPont | −0.686 | (0.509) | 3.829 | (3.000) |

Fannie Mae | −0.239 | (0.362) | 5.363 | (3.430) |

Inforum—University of Maryland | −0.479 | (0.523) | 4.887 | (2.789) |

University of Michigan—RSQE | −0.531 | (0.422) | 4.775 | (1.534) |

Georgia State University | −0.948 | (0.477) | 4.728 | (2.086) |

**Table 2.**Estimation results for (1) for forecasting inflation, USA, 1996–2018. Italics means significant at the 10% level.

Forecaster | Parameter Estimates (Standard Errors) for the Variables: | |||
---|---|---|---|---|

$\widehat{\mathit{\rho}}$ | ${\widehat{\mathit{\sigma}}}_{\mathit{\u03f5}}^{2}$ | |||

JP Morgan | 0.128 | (0.144) | 6.243 | (2.147) |

Nat Assn of Homebuilders | 0.498 | (0.252) | 3.412 | (0.822) |

Eaton Corporation | −0.053 | (0.141) | 5.086 | (2.299) |

Ford Motor Corp | 0.188 | (0.207) | 4.999 | (1.504) |

The Conference Board | −0.028 | (0.149) | 2.008 | (0.369) |

General Motors | 0.011 | (0.186) | 4.089 | (2.016) |

DuPont | 0.358 | (0.193) | 2.348 | (0.551) |

Fannie Mae | 0.069 | (0.186) | 5.973 | (1.562) |

Inforum, University of Maryland | 0.032 | (0.143) | 3.980 | (0.795) |

University of Michigan—RSQE | −0.139 | (0.170) | 7.339 | (1.135) |

Georgia State University | 0.347 | (0.223) | 1.884 | (2.832) |

**Table 3.**Estimation results for (1) for forecasting unemployment, USA, 1996–2018. Italics means significant at the 10% level.

Forecaster | Parameter Estimates (Standard Errors) for the Variables: | |||
---|---|---|---|---|

$\widehat{\mathit{\rho}}$ | ${\widehat{\mathit{\sigma}}}_{\mathit{\u03f5}}^{2}$ | |||

JP Morgan | −0.188 | (0.179) | 13.289 | (2.801) |

Nat Assn of Homebuilders | 0.038 | (0.287) | 25.649 | (10.608) |

Eaton Corporation | −0.110 | (0.225) | 22.457 | (6.206) |

Ford Motor Corp | −0.013 | (0.220) | 8.131 | (1.791) |

The Conference Board | 0.077 | (0.240) | 10.225 | (4.105) |

General Motors | −0.004 | (0.227) | 6.016 | (3.866) |

DuPont | 0.053 | (0.336) | 5.730 | (5.319) |

Fannie Mae | 0.061 | (0.169) | 9.233 | (4.627) |

Inforum, University of Maryland | 0.303 | (0.239) | 9.387 | (5.669) |

University of Michigan—RSQE | −0.298 | (0.208) | 12.719 | (3.569) |

Georgia State University | 0.062 | (0.218) | 3.640 | (3.235) |

**Table 4.**Results for regression model (1) for other countries or regions, real GDP growth. The counts concern the number of cases with 10% significant estimation results.

Country | Forecasters | $\widehat{\mathit{\rho}}$ | ${\widehat{\mathit{\sigma}}}_{\mathit{\u03f5}}^{2}$ | ||
---|---|---|---|---|---|

Positive | Negative | Positive | Negative | ||

Eurozone | 14 | 0 | 1 | 14 | 0 |

France | 5 | 0 | 1 | 5 | 0 |

Germany | 17 | 1 | 2 | 17 | 0 |

Italy | 6 | 0 | 1 | 6 | 0 |

Japan | 8 | 0 | 0 | 7 | 0 |

Netherlands | 4 | 0 | 0 | 3 | 0 |

Norway | 2 | 0 | 0 | 2 | 0 |

Spain | 6 | 1 | 1 | 6 | 0 |

Sweden | 3 | 0 | 1 | 2 | 0 |

Switzerland | 7 | 0 | 1 | 1 | 0 |

UK | 12 | 1 | 2 | 3 | 0 |

Total | 84 | 3 | 10 | 66 | 0 |

3.60% | 11.90% | 78.60% | 0% |

**Table 5.**Results for regression model (1) for other countries or regions, inflation. The counts concern the number of cases with 10% significant estimation results.

Country | Forecasters | $\widehat{\mathit{\rho}}$ | ${\widehat{\mathit{\sigma}}}_{\mathit{\u03f5}}^{2}$ | ||
---|---|---|---|---|---|

Positive | Negative | Positive | Negative | ||

Eurozone | 14 | 2 | 0 | 1 | 0 |

France | 5 | 0 | 2 | 4 | 0 |

Germany | 17 | 0 | 0 | 9 | 0 |

Italy | 6 | 0 | 0 | 4 | 0 |

Japan | 7 | 0 | 1 | 7 | 0 |

Netherlands | 4 | 0 | 2 | 3 | 0 |

Norway | 2 | 0 | 0 | 0 | 0 |

Spain | 6 | 0 | 0 | 3 | 0 |

Sweden | 2 | 0 | 0 | 2 | 0 |

Switzerland | 7 | 0 | 1 | 6 | 0 |

UK | 4 | 0 | 0 | 1 | 0 |

Total | 74 | 2 | 6 | 40 | 0 |

2.70% | 8.10% | 54.10% | 0% |

**Table 6.**Results for regression model (1) for other countries or regions, unemployment. The counts concern the number of cases with 10% significant estimation results.

Title | Forecasters | $\widehat{\mathit{\rho}}$ | ${\widehat{\mathit{\sigma}}}_{\mathit{\u03f5}}^{2}$ | ||
---|---|---|---|---|---|

Positive | Negative | Positive | Negative | ||

Eurozone | 14 | 0 | 0 | 3 | 0 |

France | 5 | 0 | 0 | 0 | 0 |

Germany | 16 | 1 | 3 | 0 | 1 |

Italy | 5 | 0 | 0 | 2 | 0 |

Japan | 6 | 1 | 1 | 5 | 0 |

Netherlands | 0 | ||||

Norway | 0 | ||||

Spain | 0 | ||||

Sweden | 0 | ||||

Switzerland | 0 | ||||

UK | 5 | 1 | 0 | 0 | 0 |

Total | 51 | 3 | 4 | 10 | 1 |

2.70% | 7.80% | 19.60% | 2.00% |

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## Share and Cite

**MDPI and ACS Style**

Franses, P.H.; Welz, M.
Does More Expert Adjustment Associate with Less Accurate Professional Forecasts? *J. Risk Financial Manag.* **2020**, *13*, 44.
https://doi.org/10.3390/jrfm13030044

**AMA Style**

Franses PH, Welz M.
Does More Expert Adjustment Associate with Less Accurate Professional Forecasts? *Journal of Risk and Financial Management*. 2020; 13(3):44.
https://doi.org/10.3390/jrfm13030044

**Chicago/Turabian Style**

Franses, Philip Hans, and Max Welz.
2020. "Does More Expert Adjustment Associate with Less Accurate Professional Forecasts?" *Journal of Risk and Financial Management* 13, no. 3: 44.
https://doi.org/10.3390/jrfm13030044