The last recession in Europe has shown us that econometric models that factor in the qualitative perceptions and expectations of businesses and consumers—along with commonly used quantitative macroeconomic variables—can produce better results in explaining and forecasting economic activity. The European Commission’s Business and Consumer Surveys (BCS) conducted by the European Commission (EC) are high-quality source for this kind of “soft” variables. One of the composite indicators based on BCS is the economic sentiment indicator (ESI), which is the main leading indicator for overall economic activity. We propose two new models for constructing the ESI. The first model is based on minimizing the sum of absolute values of estimation errors. The second model is based on maximizing the number of correctly predicted directions of change for GDP growth rates. Rather than using the EC’s official standardization procedure for data, our models use “raw” data, thus simplifying the process of preparing the data. The models were tested for various prognostic horizons (up to four quarters in advance), using aggregated quarterly data for the European Union from 1996Q4 to 2019Q2. The results show that our new models significantly improve the ESI’s predictive power, especially in predicting the direction of change of GDP growth rates, which is the main purpose of the BCS indicators. The best results are obtained for predictions made up to one quarter in advance, for which the second model correctly predicts the direction of change of GDP growth rates in 78.89% of cases versus the official ESI’s 65.56%.

欧洲最近一次的衰退向我们展示了将企业和消费者的定性看法和期望纳入考虑范围的计量经济学模型，以及常用的定量宏观经济变量，可以在解释和预测经济活动方面产生更好的结果。欧盟委员会（EC）进行的欧盟委员会商业和消费者调查（BCS）是此类“软”变量的高质量来源。基于BCS的综合指标之一是经济情绪指标（ESI），它是总体经济活动的主要领先指标。我们提出了两种用于构建ESI的新模型。第一模型基于最小化估计误差的绝对值之和。第二个模型基于最大化GDP增长率的正确预测变化方向的数量。我们的模型没有使用EC的官方标准化程序来处理数据，而是使用“原始”数据，从而简化了准备数据的过程。使用1996年第4季度至2019年第2季度的欧盟季度汇总数据，对模型进行了各种预测范围的测试（提前四个季度进行了测试）。结果表明，我们的新模型显着提高了ESI的预测能力，尤其是在预测GDP增长率的变化方向时，这是BCS指标的主要目的。对于提前到四分之一的预测，可以获得最佳结果，第二种模型可以正确地预测78个国家的GDP增长率变化方向。