The main objective of this study is two-fold. First, we aim to detect the underlying existing periodicities in business and consumer survey expectations through spectral analysis. We use the Welch method to extract the harmonic components that correspond to the cyclic and seasonal patterns in all response options of monthly survey indicators. We find that business survey indicators show a common cyclical component of low frequency that corresponds to about four years, while for most consumer survey indicators, we do not detect any relevant cyclic components, and the obtained lower frequency periodicities show a very irregular pattern across questions and reply options. Second, we aim to provide researchers with a filter specially designed for business and consumer survey expectations that circumvents other filtering methods’ assumptions. Most seasonal adjustment methods are based on a priori assumptions about the structure of the components and do not depend on the features of the specific series. To overcome this limitation, we design a low-pass filter that allows extracting the components with periodicities similar to those that can be found in the dynamics of economic activity. We opt for a Butterworth filter and apply a zero-phase filtering process to preserve the time series’ time alignment. We find that the balances computed with the filtered series are highly correlated with the seasonally-adjusted balances published by the European Commission, albeit the former tend to be smoother for the consumer survey indicators.

这项研究的主要目的是双重的。首先，我们旨在通过频谱分析来发现商业和消费者调查预期中潜在的现有周期性。在月度调查指标的所有响应选项中，我们使用Welch方法提取与周期和季节模式相对应的谐波分量。我们发现，业务调查指标显示了一个常见的低频周期性成分，大约相当于四年，而对于大多数消费者调查指标，我们没有检测到任何相关的周期性成分，并且所获得的低频周期性在问题之间显示出非常不规则的模式和回复选项。其次，我们旨在为研究人员提供一个专门针对商业和消费者调查期望而设计的过滤器，它可以绕开其他过滤方法的假设。有关组件结构的先验假设，并不取决于特定系列的特征。为了克服此限制，我们设计了一个低通滤波器，该滤波器允许以与经济活动动态中发现的周期相似的周期提取分量。我们选择巴特沃思滤波器，并应用零相位滤波过程，以保留时间序列的时间对齐。我们发现，使用过滤后的序列计算出的余额与欧盟委员会发布的经季节性调整的余额高度相关，尽管前者对于消费者调查指标而言较为平滑。