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Frequency domain analysis and filtering of business and consumer survey expectations

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  • Oscar Claveria
  • Enric Monte
  • Salvador Torra

Abstract

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.

Suggested Citation

  • Oscar Claveria & Enric Monte & Salvador Torra, 2021. "Frequency domain analysis and filtering of business and consumer survey expectations," International Economics, CEPII research center, issue 166, pages 42-57.
  • Handle: RePEc:cii:cepiie:2021-q2-166-3
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    References listed on IDEAS

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    1. Altug, Sumru & Çakmaklı, Cem, 2016. "Forecasting inflation using survey expectations and target inflation: Evidence for Brazil and Turkey," International Journal of Forecasting, Elsevier, vol. 32(1), pages 138-153.
    2. Alessandro Girardi & Christian Gayer & Andreas Reuter, 2016. "The Role of Survey Data in Nowcasting Euro Area GDP Growth," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(5), pages 400-418, August.
    3. Sorić, Petar & Lolić, Ivana & Claveria, Oscar & Monte, Enric & Torra, Salvador, 2019. "Unemployment expectations: A socio-demographic analysis of the effect of news," Labour Economics, Elsevier, vol. 60(C), pages 64-74.
    4. Sarah Gelper & Christophe Croux, 2010. "On the Construction of the European Economic Sentiment Indicator," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(1), pages 47-62, February.
    5. Claveria, Oscar & Monte, Enric & Torra, Salvador, 2020. "Economic forecasting with evolved confidence indicators," Economic Modelling, Elsevier, vol. 93(C), pages 576-585.
    6. Juhro, Solikin M. & Iyke, Bernard Njindan, 2020. "Consumer confidence and consumption expenditure in Indonesia," Economic Modelling, Elsevier, vol. 89(C), pages 367-377.
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    Cited by:

    1. Petar Soric & Oscar Claveria, 2021. ""Employment uncertainty a year after the irruption of the covid-19 pandemic"," IREA Working Papers 202112, University of Barcelona, Research Institute of Applied Economics, revised May 2021.
    2. Marcela De Castro-Valderrama & Santiago Forero-Alvarado & Nicolás Moreno-Arias & Sara Naranjo-Saldarriaga, 2021. "Unraveling the Exogenous Forces Behind Analysts’ Macroeconomic Forecasts," Borradores de Economia 1184, Banco de la Republica de Colombia.
    3. Oscar Claveria & Petar Sorić, 2023. "Labour market uncertainty after the irruption of COVID-19," Empirical Economics, Springer, vol. 64(4), pages 1897-1945, April.

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    More about this item

    Keywords

    Business and consumer surveys; Spectral analysis; Seasonality; Signal processing; Low-pass filter;
    All these keywords.

    JEL classification:

    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access

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