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We study whether forecasts of the rate of change of the price of oil are rational. To this end, we consider a model that allows the shape of forecasters’ loss function to be studied. The shape of forecasters’ loss function may be consistent with a symmetric or an asymmetric loss function. We find that an asymmetric loss function often (but not always) makes forecasts look rational, and we also report that forecast rationality may have changed over time.

Oil is an important raw material for many industrialized and developing countries, and an important source of export revenues for oil-exporting countries. Because changes in the price of oil can have significant effects on macroeconomic dynamics, it is not surprising that many researchers have applied various econometric techniques that help to model and to forecast changes in the price of oil [

Survey data on forecasts of future changes of the price of oil are an alternative to formal econometric techniques when it comes to forecasting the dynamics of the price of oil. An important question, thus, is whether survey data on forecasts of future changes of the price of oil are consistent with the rational-expectations paradigm of economics. Such questionnaires measure market’s expectations directly by surveying a large number of analysts working in the financial industry. Deviations from rationality may arise for several reasons. For example, Pierdzioch

We study the rationality of forecasts of future changes of the price of oil using a general modeling framework that allows forecasters’ loss function to be asymmetric [

Pierdzioch

Adopting the approach developed by Elliott _{t}_{ + 1}− _{t}_{ + 1} < 0)]} × |_{t}_{+ 1}− _{t }_{+ 1}|^{p}_{t}_{+ 1} denotes the log change in the price of oil (that is, the annualized rate of change), _{t }_{+ 1} denotes the corresponding period-

Given the parameter, _{i}_{i}

Following [

The price of oil.

Estimated asymmetry parameter (mean across forecasters).

Model specification | _{1} |
_{2} |
_{3} |
_{4} |
---|---|---|---|---|

Full sample, lin-lin | 0.31 | 0.30 | 0.30 | 0.29 |

Full sample, quad-quad | 0.21 | 0.18 | 0.19 | 0.17 |

Subsample, lin-lin | 0.25 | 0.21 | 0.20 | 0.20 |

Subsample, quad-quad | 0.16 | 0.12 | 0.11 | 0.11 |

Note: We use forecasts formed by 19 forecasters to compute 19 estimates of the asymmetry parameter; We then compute the average asymmetry parameter as the mean value of the 19 individual asymmetry parameters; We use the following instruments: a constant (Model 1), a constant and the lagged forecast error (Model 2), a constant and the lagged rate of change of the oil price (Model 3), and, a constant, the lagged forecast error, and the lagged rate of change of the oil price (Model 4); The full sample covers the period of time 2002Q4–2010Q4; The subsample covers the period of time up to and including 2006Q4.

_{i}

Results of the rationality tests.

Rationality results | Rational under symmetric and asymmetric loss | Rational only under asymmetric loss | Tests indicate violation of rationality under both loss functions |
---|---|---|---|

Forecasts of individual forecasters | |||

Full sample, lin-lin | 6 | 12 | 1 |

Full sample, quad-quad | 4 | 12 | 3 |

Subsample, lin-lin | 4 | 10 | 5 |

Subsample, quad-quad | 3 | 11 | 4 |

Forecasts pooled across forecasters | |||

Full sample, lin-lin | no | no | yes |

Full sample, quad-quad | no | yes | no |

Subsample, lin-lin | no | no | yes |

Subsample, quad-quad | no | no | yes |

Note: When analyzing forecasts of individual forecasters, we compute the

The results clearly demonstrate that an asymmetric loss function leads to a rejection of forecast rationality less often than does a symmetric loss function. Moreover, forecast rationality is rejected more often (for approximately 21% to 25% of forecasters) when we estimate the loss function on data for the subsample period. When we pool forecasts across forecasters, we do not find much evidence that an asymmetric loss function helps to reconcile forecasts with the hypothesis of forecast rationality.

Because

Recursive-estimation window.

Two key results emerge. First, the estimated asymmetry parameter tended to increase somewhat during the sample period. (This tendency becomes more apparent when we use a rolling rather than a recursive-window approach.) Second, evidence of forecast rationality, given a lin-lin loss function, is stronger in the second half of the sample period. Forecast rationality cannot be rejected when the

The results that we have derived for forecasts of the

We are grateful to four anonymous referees for helpful comments and suggestions.

The authors declare no conflict of interest.