Replication Data for: Accuracy gains from conservative forecasting

Problem: Do conservative econometric models that comply with the Golden Rule of Forecasting provide more accurate forecasts? Methods: 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. Findings: 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. Originality: This paper provides the first test of applying guidelines for conservative forecasting to established election forecasting models. Usefulness: 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.研究问题：遵循预测黄金法则（Golden Rule of Forecasting）的保守计量经济学模型（econometric models），能否提供更为精准的预测结果？ 研究方法：为检验预测精度的影响效应，我们将三项循证指南（evidence-based guidelines）应用于19篇已发表的回归模型（regression models），这些模型用于预测澳大利亚、加拿大、意大利、日本、荷兰、葡萄牙、西班牙、土耳其、英国（U.K.）、美国（U.S.）共计154场选举的结果。该指南指导使用因果模型的预测者采取保守策略以应对不确定性，具体方式包括：（I）调整效应估计值以反映不确定性，即向无效应方向收缩系数，或对系数进行均等化处理；（II）融合来自不同模型的预测结果；（III）纳入更多具备已知重要影响的变量以扩充知识储备。 研究发现：将计量经济学模型调整为更保守的形式后，相较于原始模型的预测结果，其预测误差有所降低：（I）将系数收缩10%可使平均误差降低2%，但进一步收缩通常会损害预测精度；通过均等化方式调整系数则可持续降低误差，根据均等化程度的不同，平均误差降幅介于2%至8%之间。将原始回归模型的预测结果与等权重模型的预测结果取平均，可使误差降低7%。（II）融合两个澳大利亚模型与八个美国模型的预测结果，分别可使误差降低14%与36%。（III）通过纳入澳大利亚模型全部6个独特变量、美国模型全部24个独特变量以构建等权重"知识模型"，可分别使误差降低10%与43%。 研究创新性：本文首次针对已成熟的选举预测模型，检验了保守型预测指南的应用效果。 研究价值：选举预测研究者可通过遵循保守型预测的简易指南，大幅提升计量经济学模型的预测精度。决策者在获取更贴合实际的模型与更精准的预测结果后，能够做出更合理的决策。