Professional forecasters can rely on econometric models, on their personal expertise or on both. To accommodate for adjustments to model forecasts, this paper proposes to use two stage least squares (TSLS) (and not ordinary least squares [OLS]) for the familiar Mincer–Zarnowitz regression when examining bias in professional forecasts, where the instrumental variable is the consensus forecast. An illustration for 15 professional forecasters with their quotes for real gross domestic product (GDP) growth, inflation and unemployment for the United States documents the usefulness of this new estimation method. It also shows that TSLS suggests less bias than OLS does.

中文翻译：

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专业预测中的测试偏差

专业预测人员​​可以依靠计量经济学模型、他们的个人专业知识或两者兼而有之。为了适应模型预测的调整，本文建议在检查专业预测中的偏差时对熟悉的 Mincer-Zarnowitz 回归使用两阶段最小二乘法 (TSLS)（而不是普通最小二乘法 [OLS]），其中工具变量是共识预测。15 位专业预测员对美国实际国内生产总值 (GDP) 增长、通货膨胀和失业率的报价说明了这种新估计方法的实用性。它还表明 TSLS 建议的偏差比 OLS 少。