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Spectral analysis of business and consumer survey data

Author

Listed:
  • Oscar Claveria

    (AQR–IREA, Department of Econometrics, Statistics and Applied Economics, University of Barcelona, Diagonal 690, 08034 Barcelona, Spain.)

  • Enric Monte

    (Department of Signal Theory and Communications, Polytechnic University of Catalunya (UPC))

  • Salvador Torra

    (Riskcenter–IREA, Department of Econometrics, Statistics and Applied Economics, University of Barcelona (UB))

Abstract

The main objective of this study is two-fold. First, we aim to detect the underlying existing periodicities in business and consumer survey data. With this objective, we conduct a spectral analysis of all survey indicators. Second, we aim to provide researchers with a filter especially designed for business and consumer survey data that circumvents the a priori assumptions of other filtering methods. To this end, 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. The European Commission (EC) conducts monthly business and consumer tendency surveys in which respondents are asked whether they expect a set of variables to rise, fall or remain unchanged. We apply the Welch method for the detection of periodic components in each of the response options of all monthly survey indicators. This approach allows us to extract the harmonic components that correspond to the cyclic and seasonal patterns of the series. Unlike other methods for spectral density estimation, the Welch algorithm provides smoother estimates of the periodicities. We find remarkable differences between the periodicities detected in the industry survey and the consumer survey. While business survey indicators show a common cyclical component of low frequency that corresponds to about four years, 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. Most methods for seasonal adjustment are based on a priori assumptions about the structure of the components and do not depend on the features of the specific series. In order to overcome this limitation, we design a low-pass filter for survey indicators. We opt for a Butterworth filter and apply a zero-phase filtering process to preserve the time alignment of the time series. This procedure allows us to reject the frequency components of the survey indicators that do not have a counterpart in the dynamics of economic activity. We use the filtered series to compute diffusion indexes known as balances, and compare them to the seasonally-adjusted balances published by the EC. Although both series are highly correlated, filtered balances tend to be smoother for the consumer survey indicators.

Suggested Citation

  • Oscar Claveria & Enric Monte & Salvador Torra, 2020. "Spectral analysis of business and consumer survey data," IREA Working Papers 202006, University of Barcelona, Research Institute of Applied Economics, revised May 2020.
    • Handle: RePEc:ira:wpaper:202006
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    References listed on IDEAS

    as
    1. Oscar Claveria, 2019. "Forecasting the unemployment rate using the degree of agreement in consumer unemployment expectations," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 53(1), pages 1-10, December.
    2. Juan Gabriel Brida & Bibiana Lanzilotta & Lucía Rosich, 2019. "Common trends in producers’ expectations, the nonlinear linkage with Uruguayan GDP and its implications in economic growth forecasting," Documentos de Trabajo (working papers) 19-28, Instituto de Economía - IECON.
    3. 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.
    4. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "Using Survey Data to Forecast Real Activity with Evolutionary Algorithms. a Cross-Country Analysis," Journal of Applied Economics, Taylor & Francis Journals, vol. 20(2), pages 329-349, November.
    5. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Evolutionary Computation for Macroeconomic Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 833-849, February.
    6. 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.
    7. Claveria, Oscar & Pons, Ernest & Ramos, Raul, 2007. "Business and consumer expectations and macroeconomic forecasts," International Journal of Forecasting, Elsevier, vol. 23(1), pages 47-69.
    8. Oscar Claveria, 2019. "Forecasting the unemployment rate using the degree of agreement in consumer unemployment expectations," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 53(1), pages 1-10, December.
    9. Juhro, Solikin M. & Iyke, Bernard Njindan, 2020. "Consumer confidence and consumption expenditure in Indonesia," Economic Modelling, Elsevier, vol. 89(C), pages 367-377.
    10. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Empirical modelling of survey-based expectations for the design of economic indicators in five European regions," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 46(2), pages 205-227, May.
    11. repec:iab:iabjlr:v:53:i:1:p:art.3 is not listed on IDEAS
    12. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "A new approach for the quantification of qualitative measures of economic expectations," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(6), pages 2685-2706, November.
    13. Oscar Claveria, 2019. "A new consensus-based unemployment indicator," Applied Economics Letters, Taylor & Francis Journals, vol. 26(10), pages 812-817, June.
    14. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "A Data-Driven Approach to Construct Survey-Based Indicators by Means of Evolutionary Algorithms," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(1), pages 1-14, January.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Business and consumer surveys; Spectral analysis; Seasonality; Signal processing; Low-pass filter. JEL classification: C65; C82.;
    All these keywords.

    JEL classification:

    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools

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