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A factor analysis for the Spanish economy

Author

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  • Ángel Cuevas

  • Enrique Quilis

Abstract

We present a medium-scale dynamic factor model to estimate and forecast the rate of growth of the Spanish economy in the very short term. The intermediate size of the model overcomes the serious specification problems associated with large-scale models and the implicit loss of information of small-scale models. The estimated common factor is used to forecast the gross domestic product by means of a transfer function model. Likewise, the model solves the operational and informational limits posed by the presence of an unbalanced panel of indicators and generates multivariate forecasts of the basic indicators. Copyright The Author(s) 2012

Suggested Citation

  • Ángel Cuevas & Enrique Quilis, 2012. "A factor analysis for the Spanish economy," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 3(3), pages 311-338, September.
    • Handle: RePEc:spr:series:v:3:y:2012:i:3:p:311-338
    DOI: 10.1007/s13209-011-0060-9
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    Cited by:

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    2. Daniel Armeanu & Jean Vasile Andrei & Leonard Lache & Mirela Panait, 2017. "A multifactor approach to forecasting Romanian gross domestic product (GDP) in the short run," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-23, July.
    3. Dimitar EFTIMOSKI, 2019. "Improving Short-Term Forecasting of Macedonian GDP: Comparing the Factor Model with the Macroeconomic Structural Equation Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 32-53, June.
    4. Alifatussaadah, Ardiana & Primariesty, Anindya Diva & Soleh, Agus Mohamad & Andriansyah, Andriansyah, 2019. "Nowcasting Indonesia's GDP Growth: Are Fiscal Data Useful?," MPRA Paper 105252, University Library of Munich, Germany.
    5. Diego Fresoli, 2024. "Spanish GDP short-term point and density forecasting using a mixed-frequency dynamic factor model," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 15(2), pages 145-177, June.
    6. Priscila Espinosa & Jose M. Pavía, 2023. "Automation in Regional Economic Synthetic Index Construction with Uncertainty Measurement," Forecasting, MDPI, vol. 5(2), pages 1-19, April.
    7. Martínez-García, Enrique & Grossman, Valerie & Mack, Adrienne, 2015. "A contribution to the chronology of turning points in global economic activity (1980–2012)," Journal of Macroeconomics, Elsevier, vol. 46(C), pages 170-185.

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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