Revista de Ciencias Económicas ISSN Impreso: 0252-9521 ISSN electrónico: 2215-3489

OAI: https://revistas.ucr.ac.cr/index.php/economicas/oai
Forecasting Costa Rican Quarterly Growth with Mixed-frequency Models
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Keywords

DATOS DE FRECUENCIA MIXTA
MODELOS MIDAS
MODELOS BRIDGE
PRONÓSTICO EN TIEMPO REAL
MIXED -FREQUENCY DATA
MIDAS MODELS
BRIDGE MODELS
NOWCASTING

How to Cite

Rodríguez Vargas, A. (2014). Forecasting Costa Rican Quarterly Growth with Mixed-frequency Models. Revista De Ciencias Económicas, 32(2), 189–226. https://doi.org/10.15517/rce.v32i2.17267

Abstract

We assess the utility of mixed-frequency models to forecast the quarterly growth rate of Costa Rican real GDP: we estimate bridge and MiDaS models with several lag lengths using information of the IMAE and compute forecasts (horizons of 0-4 quarters) which are compared between themselves, with those of ARIMA models and with those resulting from forecast combinations. Combining the most accurate forecasts is most useful when forecasting in real time, whereas MiDaS forecasts are the best-performing overall: as the forecasting horizon increases, their precisionis affected relatively little; their success rates in predicting the direction of changes in the growth rate are stable, and several forecastsremain unbiased. In particular, forecasts computed from simple MiDaS with 9 and 12 lags are unbiased at all horizons and information sets assessed, and show the highest number of significant differences in forecasting ability in comparison with all other models.
https://doi.org/10.15517/rce.v32i2.17267
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References

Antipa, P., Barhoumi, K., Brunhes-Lesange, V., & Darné, O. (2012). Nowcasting German GDP: A Comparison of Bridge and Factor Models (Document de Travail No. 401). Banque de France.

Armesto, M. T., Engemann, K. M., & Owyang, M. T. (2010). Forecasting with Mixed Frequencies. Federal Reserve Bank of St. Louis Review, 92(6), 521-36.

Barhoumi, K., Brunhes-Lesange, V., Darné, O., Ferrara, L., Pluyaud, B., & Rouvreau, B. (2008). Monthly Forecasting of French GDP: A Revised Version of the OPTIM Model (Notes d'Études et de Recherche 222). Banque de France.

Barhoumi, K., Darné, O., Ferrara, L., & Pluyaud, B. (2012). Monthly GDP Forecasting Using Bridge Models: Application for the French Economy. Bulletin of Economic Research, 64, s57-s70.

Baumeister, C., Guérin, P., & Kilian, L. (2014). Do High-Frequency Financial Data Help Forecast Oil Prices? The MIDAS Touch at Work (Working Paper / Document de travail 2014-11). Bank of Canada.

BCCR. (s.f). Metodología de cálculo del Índice Mensual de Actividad Económica (IMAE). Disponible en: http://indicadoreseconomicos.bccr.fi.cr/indicadoreseconomicos/Documentos//DocumentosMetodologiasNotasTecnicas/Metodología%20de%20cálculo%20del%20IMAE.htm

Box, G. E., & Pierce, D. A. (1970). Distribution of Residual Autocorrelations in Autoregressive Integrated Moving Average Time Series Models. Journal of the American Statistical Association, 65 (332), 1509–1526.

Box, G., & Jenkins, G. (1970). Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day.

Bulligan, G., Marcellino, M., & Venditti, F. (2012). Forecasting Economic Activity with Higher Frequency Targeted Predictors (Temi di Discussione 847). Banca d'Italia.

Capistrán, C., & Timmermann, A. (2009). Forecast Combination With Entry and Exit of Experts. Journal of Business & Economic Statistics, 27(4), 428-440.

Chen, X., & Ghysels, E. (2011). News good or bad and its impact on volatility predictions over multiple horizons. Review of Financial Studies, 24(1), 46-81.

Chow, G. C. (1960). Tests of Equality Between Sets of Coefficients in Two Linear Regressions. Econometrica, 28(3), 591–605.

Clements, M. P., & Galvão, A. B. (2006). Macroeconomic Forecasting with Mixed Frequency Data: Forecasting US output growth and inflation. (U. o. Department of Economics, Ed.) (Warwick Economic Research Papers No 773).

Cobb, M., Echavarría, G., Filippi, P., García, M., Godoy, C., González, W., y otros. (2011). Short-term GDP Forecasting Using Bridge Models: A Case for Chile (Documentos de Trabajo No. 626). Banco Central de Chile.

Diebold, F. X., & Mariano, R. (1995). Comparing predictive accuracy. Journal of Business and Economic Statistics, 13, 253-265.

Doornik, J., & Hansen, H. (1994). A Practical test for Univariate and Multivariate Normality (Discussion Paper). Nuffield College, Oxford University.

Doornik, J., & Hendry, D. F. (1996). Empirical Econometric Modelling Using PcGive 9 for Windows. Londres: Timberlake Consultants Press.

Doornik, J., & Hendry, D. F. (2007). PcGive 12. Londres: Timberlake Consultants Press.

Engle, R., Ghysels, E., & Sohn, B. (2013). Stock market volatility and macroeconomic fundamentals. Review of Economics and Statistics, 95(3), 776-797.

Ghysels, E. (2012). Macroeconomics and the reality of mixed frequency data. SSRN: http://ssrn.com/abstract=2069998orhttp://dx.doi.org/10.2139/ssrn.2069998.

Ghysels, E. (2014). Matlab Toolbox for Mixed Sampling Frequency Data Analysis usin MIDAS Regression Models. Disponible en http://www.unc.edu/~eghysels/papers/MIDAS_Usersguide_V1.0.pdf.

Ghysels, E., Santa-Clara, P., & Valkanov, R. (2004). The MIDAS Touch: Mixed Data Sampling Regression Models. CIRANO Working Papers 2004s-20.

Ghysels, E., Sinko, A., & Valkanov, R. (2006). MIDAS Regressions: Further Results and New Directions. Disponible en SSRN: http://ssrn.com/abstract=885683 or http://dx.doi.org/10.2139/ssrn.885683.

Godfrey, L. G. (1978). Testing for Higher-order Serial Correlation in Regression Equations When the Regressors Include Lagged Dependent Variables. Econometrica, 46(6), 1303-1313.

Golinelli, R., & Parigi, G. (2007). The Use of Monthly Indicators to Forecast Quarterly GDP in the Short Run: An Application to the G7 Countries. Journal of Forecasting, 26(2), 77-94.

Gómez, V., & Maravall, A. (1994). Prediction and Interpolation for Nonstationary Series with the Kalman Filter. Journal of the American Statistical Association, 89, 611-624.

Gómez, V., & Maravall, A. (1996). Programs TRAMO and SEATS, Instruction for User (Beta Version: september 1996) . Banco de España Working Papers 9628.

Harvey, D., Leybourne, S., & Newbold, P. (1997). Testing the equality of prediction mean squared errors. International Journal of Forecasting, 13, 281-291.

Hendry, D. F. (1979). Predictive Failure and Econometric Modelling in Macroeconomics: The Transactions Demand for Money. En P. Ormerod (Ed.), Economic Modelling (págs. 217-242). Londres: Heinemann.

Hood, C. C., Ashley, J. D., & Findley, D. F. (2000). An Empirical Evaluation fo the Performance of TRAMO/SEATS on Simulated Series. Proceedings of the American Statistical Association, Business and Economics Section, 171-176.

Hoover, K. D., & Perez, S. J. (1999). Data Mining Reconsidered: Encompassing and the General-to-specific Approach to Specification Search. Econometric Journal, 2(2), 167-191.

Ingenito, R., & Trehan, B. (1996). Using Monthly Data to Predict Quarterly Output. Economic Review(3), 3-11.

Jarque, C. M., & Bera, A. K. (1980). Efficient Tests for Normality, Homoscedasticity and Serial Independence of Regression Residuals. Economic Letters, 6(3), 255-259.

Klein, L. R., & Sojo, E. (1989). Combinations of High and Low Frequency Data in Macroeconometric Models. En L. R. Klein, & J. Márquez, Economics in Theory and Practice: An Eclectic Approach (págs. 3-16). Dordrecht: Kluwer.

Krolzig, H.-M., & Hendry, D. F. (2001). Computer Automation of General-to-specific Model Selection Procedures. Journal of Economic Dynamics and Control, 25(6-7), 831-866.

Krolzig, H.-M., & Hendry, D. F. (2005). The Properties of Automatic GETS Modelling. The Economic Journal, 115(502), C32-C61.

Kuzin, V., Marcellino, M., & Schumacher, C. (2011). MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area. International Journal of Forecasting, 27, 529–542.

Maravall, A., López-Pavón, R., & Pérez-Cañete, D. (2012). Reliability of the Automatic Identification of ARIMA Models in Program TRAMO. Presentado en el CEMFI Econometrics Workshop, 5 junio de 2012. Madrid, España.

Nicholls, D. F., & Pagan, A. R. (1983). Heteroskedasticity in Models with Lagged Dependent Variables. Econometrica, 51(4), 1233-1242.

Parigi, G., & Shlitzer, G. (1995). Quarterly Forecasts of the Italian Business Cycle by Means of Monthly Economic Indicators. Journal of Forecasting, 14, 117-141.

Trehan, B. (1992). Predicting Contemporaneous Output. Federal Reserve Bank of San Francisco Economic Review(2), 3-11.

White, H. (1980). A Heteroskedastic-consistent Covariance Matrix Estimator and A Direct Test for Heteroskedasticity. Econometrica, 48(4), 817-838.

Winkelried, D. (2012). Predicting quarterly aggregates with monthly indicators. (Serie Documentos de Trabajo DT. N° 2012-023). Banco Central de Reserva del Perú.

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