Macromedia University, Munich, Germany.
University of South Australia Business School, Adelaide, Australia.
PLoS One. 2019 Jan 10;14(1):e0209850. doi: 10.1371/journal.pone.0209850. eCollection 2019.
Do conservative econometric models that comply with the Golden Rule of Forecasting provide more accurate forecasts?
To test the effects of forecast accuracy, we applied three evidence-based guidelines to 19 published regression models used for forecasting 154 elections in Australia, Canada, Italy, Japan, Netherlands, Portugal, Spain, Turkey, U.K., and the U.S. The guidelines direct forecasters using causal models to be conservative to account for uncertainty by (I) modifying effect estimates to reflect uncertainty either by damping coefficients towards no effect or equalizing coefficients, (II) combining forecasts from diverse models, and (III) incorporating more knowledge by including more variables with known important effects.
Modifying the econometric models to make them more conservative reduced forecast errors compared to forecasts from the original models: (I) Damping coefficients by 10% reduced error by 2% on average, although further damping generally harmed accuracy; modifying coefficients by equalizing coefficients consistently reduced errors with average error reductions between 2% and 8% depending on the level of equalizing. Averaging the original regression model forecast with an equal-weights model forecast reduced error by 7%. (II) Combining forecasts from two Australian models and from eight U.S. models reduced error by 14% and 36%, respectively. (III) Using more knowledge by including all six unique variables from the Australian models and all 24 unique variables from the U.S. models in equal-weight "knowledge models" reduced error by 10% and 43%, respectively.
This paper provides the first test of applying guidelines for conservative forecasting to established election forecasting models.
Election forecasters can substantially improve the accuracy of forecasts from econometric models by following simple guidelines for conservative forecasting. Decision-makers can make better decisions when they are provided with models that are more realistic and forecasts that are more accurate.
符合预测黄金法则的保守计量经济学模型是否提供更准确的预测？
为了测试预测准确性的影响，我们将三项基于证据的指南应用于 19 个用于预测澳大利亚、加拿大、意大利、日本、荷兰、葡萄牙、西班牙、土耳其、英国和美国的 154 次选举的已发表回归模型。这些指南指导使用因果模型的预测者通过以下三种方法保持保守，以考虑不确定性：(I) 通过将系数调整为无影响或均等化来调整效应估计值，从而反映不确定性；(II) 结合来自不同模型的预测；(III) 通过纳入更多具有已知重要影响的变量来纳入更多知识。
与原始模型的预测相比，通过对计量经济学模型进行修改使其更保守可以减少预测误差：(I) 将系数衰减 10%，平均可减少 2%的误差，但进一步衰减通常会损害准确性；通过均等化系数来修改系数，可根据均等化程度，平均减少 2%至 8%的误差。将原始回归模型预测与等权重模型预测进行平均，可减少 7%的误差。(II) 结合来自两个澳大利亚模型和八个美国模型的预测，可分别减少 14%和 36%的误差。(III) 通过纳入澳大利亚模型的所有六个独特变量和美国模型的所有 24 个独特变量来纳入更多知识，并在等权重的“知识模型”中，可分别减少 10%和 43%的误差。
本文首次对应用保守预测指南进行选举预测模型进行了测试。
选举预测者可以通过遵循保守预测的简单指南，大大提高计量经济学模型预测的准确性。当决策者获得更现实的模型和更准确的预测时，他们可以做出更好的决策。
