¿Qué indicadores económicos adelantan las recesiones en España?

  1. Máximo CAMACHO 1
  2. Salvador RAMALLO 1
  1. 1 Universidad de Murcia
    info

    Universidad de Murcia

    Murcia, España

    ROR https://ror.org/03p3aeb86

Revista:
Papeles de economía española

ISSN: 0210-9107

Año de publicación: 2020

Número: 165

Páginas: 33-51

Tipo: Artículo

DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Papeles de economía española

Resumen

We use classification trees to evaluate the usefulness of 270 monthly leading indicators to perform early inferences on business cycle recessions in Spain from 1971.01 to 2020.03. In the in-sample analysis, we find that the indicators give warning signals of recessions 3 and 6 months ahead with significant accuracy. In the pseudo real-time forecasting analysis, we find that financial indicators and indicators for measuring performance of construction played a special role to predict the Great Recession. However, to anticipate the severe economic consequences of the ongoing COVID-19 pandemic are better anticipated with trend indicators of output, with labor market indicators, and, to a lesser extent, with car sales indicators.

€ Ver financiación

Información de financiación

Los autores agradecen a los proyectos PID2019-107192 GB-I00 (AEI/10.13039/501100011033) y al Grupo de Excelencia de la Región de Murcia (Fundación Séneca) 19884/GERM/15.

Financiadores

Referencias bibliográficas

  • Breiman, L., Friedman, J., Olshen, R. y Stone, C. (1984). Classification and regression trees. Wadsworth, New York: Chapman & Hall.
  • Camacho, M., Caro, A. y López-Buenache. 2020. The two-speed Europe in business cycle synchronization. Empirical Economics, en prensa.
  • Camacho, M. y Doménech, R. (2012). MICA-BBVA a factor model of economic and financial indicators for short-term GDP forecasting. SERIEs: Journal of the Spanish Economic Association, 3, pp. 475-497.
  • Camacho, M., Gadea-Rivas, M. y Pérez Quirós, G. (2019). The Great Recession. Worse than ever? Revista de Economia Aplicada, 76, pp. 73:100.
  • Camacho, M., Pacce, M. y Ulloa, C. (2018). Regional business cycle phases in Spain. Estudios de Economia Aplicada, 36, pp. 875-896.
  • Camacho, M. y Pérez Quirós, G. (2011). Spain-STING: Spain Short Term INdicator of Growth. The Manchester School, 79, pp. 594-616.
  • Cancelo, J. (2004). El ciclo del empleo en España. Un análisis regional. Revista económica de Castilla-La Mancha, 4, pp. 107-138.
  • Cuevas, A., Pérez Quirós, G. y Quilis, E. (2017). Integrated model of short-term forecasting of the Spanish Economy (Mipred Model). Revista de Economía Aplicada, 25, pp. 5-25.
  • Cuevas, A. y Quilis, E. (2012). A factor analysis for the Spanish economy. Journal of the Spanish Economic Association, 3, pp. 311-338.
  • Döpke, J., Fritsche, U. y Pierdzioch, C. (2017). Predicting recessions with boosted regression trees. International Journal of Forecasting, 33, pp. 745-759.
  • Freund, Y. (1995). Boosting a weak learning algorithm by majority. Information and Computation, 121, pp. 256-285.
  • Freund, Y. y Schapire, R. (1996). Experiments with a new boosting algorithm. Proceedings of ICML, 13, pp. 148-156.
  • Friedman, J., Hastie, T. y Tibshirani, R. (2000). Additive logistic regression: A statistical view of boosting. The Annals of Statistics, 28, pp. 337-407.
  • Friedman, J. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29, pp. 1189- 1232.
  • Friedman, J. (2002). Stochastic gradient boosting. Computational Statistics and Data Analysis, 38, pp. 367-378.
  • Gadea-Rivas, M., Gómez-Loscos, A. y Bandrés, E. (2018). Clustering regional business cycles. Economic Letters, 162, pp. 171- 176.
  • Gadea-Rivas, M., Gómez-Loscos, A. y Montañés, A. (2012). Cycles inside cycles: Spanish regional aggregation. Journal of the Spanish Economic Association, 3, pp. 423-456.
  • Gadea-Rivas, M., Gómez-Loscos, A. y Leiva-León, D. (2019). Increasing linkages among European regions. The role of sectoral composition. Economic Modelling, 80, pp. 222-243.
  • Hastie, T., Tibshirani, R. y Friedman, J. (2001). The elements of statistical learning: Data mining, inference, and prediction. New York: Springer.
  • Mease, D., Wyner, A. y Buja, A. (2007). Cost-weighted boosting with jittering and over/undersampling: Jous-boost. Journal of Machine Learning Research, 8, pp. 409-439.
  • Ng, S. (2014). Boosting recessions. Canadian Journal of Economics, 47, pp. 1-34.
  • Páez, A., López, F., Ruiz, M. y Camacho, M. (2019). Inducing nonorthogonal and non-linear decision boundaries in decision trees via interactive basis functions. Expert Systems with Applications, 122, pp. 183-206.
  • Piger, J. (2020). Turning points and classification. En P. Fuleky (ed.), Macroeconomic forecasting in the era of big data: Theory and application. Springer International Publishing.
  • Schapire, R. (1990). The strength of weak learnability. Machine Learning, 5, pp. 197-227.
  • Schapire, R. y Freund, Y. (2012). Boosting: Foundations and algorithms. MIT Press.
  • Ward, F. (2017). Spotting the danger zone: Forecasting financial crisis with classification tree ensembles and many predictors. Journal of Applied Econometrics, 32, pp. 359-378
Fuente de los datos: Dialnet