Penalised regressions vs. autoregressive moving average models for forecasting inflation


This paper relates seasonal autoregressive moving average (SARMA) models with linear regression. Based on this relation, the paper shows that penalised linear models may surpass the out-of-sample forecasting accuracy of the best SARMA models when forecasting inflation based on past values, due to penalisation and cross-validation. The paper constructs a minimal working example using ridge regression to compare both of the competing approaches when forecasting the monthly inflation in 35 selected countries of the Organisation for Economic Co-operation and Development and in three groups of countries. The results empirically verify the hypothesis that penalised linear regression, and ridge regression in particular, can outperform the best standard SARMA models computed through a grid search when forecasting inflation. Thus, a new and effective technique for forecasting inflation based on past values is provided for use by financial analysts and investors. The results indicate that more attention should be given to machine learning techniques for time series forecasting of inflation, even as basic as penalised linear regressions, due to their superior empirical performance.

Palabras clave: Regresión de arista, modelo lineal penalizado, ARMA, SARMA, pronóstico de la inflación.


Anzola, C., Vargas, P., & Morales, A. (2019). Transición entre sistemas financieros bancarios y bursátiles. Una aproximación mediante modelo de Swithing Markov. ECONÓMICAS CUC, 40(1).

Box, G. E. P., & Jenkin, G. M. (1976). Time series analysis, forecasting and control. San Francisco: Holden-Day.

Burnham, K. P., & Anderson, D. R. (2004). Multimodel inference. Sociological Methods & Research, 33(2), 261–304.

Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427–431.

Diebold, F. X., & Mariano, R. S. (2002). Comparing Predictive Accuracy. Journal of Business & Economic Statistics, 20(1), 134–144.

Faust, J., & Wright, J. H. (2013). Forecasting Inflation. In G. Elliott & A. Timmermann (Eds.), Handbook of Economic Forecasting, Vol. 2A (pp. 2–56). Amsterdam: Elsevier North Holland.

Gil, J., Castellanos, D., & Gonzalez, D. (2019). Margen de intermediación y concentración bancaria en Colombia: un análisis para el periodo 2000-2017. ECONÓMICAS CUC, 40(2), 9-30.

Gómez, C., Sánchez, V., & Millán, E. (2019). Capitalismo y ética: una relación de tensiones. ECONÓMICAS CUC, 40(2), 31-42.

Gu, S., Kelly, B. T., & Xiu, D. (2018). Empirical Asset Pricing Via Machine Learning. SSRN Electronic Journal.

Hoerl, A. E., & Kennard, R. W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.

Hyndman, R. J., & Khandakar, Y. (2008). Automatic Time Series Forecasting: The forecast Package for R. Journal of Statistical Software, 27(1), 1–22.

Kvalseth, T. O. (1985). Cautionary Note about R 2. The American Statistician, 39(4), 279–285.

MacKinnon, J. G. (1996). Numerical distribution functions for unit root and cointegration tests. Journal of Applied Econometrics, 11(6), 601–618.<601::AID-JAE417>3.0.CO;2-T

Mockus, J. (1989). Bayesian approach to global optimization. Dordrecht: Kluwer Academic Publishers.

Mullainathan, S., & Spiess, J. (2017). Machine Learning: An Applied Econometric Approach. Journal of Economic Perspectives, 31(2), 87–106.

OECD. (2019). Inflation (CPI) (indicator).

Osborn, D. R., Chui, A. P. L., Smith, J. P., & Birchenhall, C. R. (2009). Seasonality and the order of integration for consumption. Oxford Bulletin of Economics and Statistics, 50(4), 361–377.

Quinn, T., Kenny, G., & Meyler, A. (1999). Inflation analysis: An overview. Munich. Retrieved from

Santosa, F., & Symes, W. W. (1986). Linear Inversion of Band-Limited Reflection Seismograms. SIAM Journal on Scientific and Statistical Computing, 7(4), 1307–1330.

Tibshirani, R. (1996). Regression Shrinkage and Selection Via the Lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288.

Tikhonov, A. N., & Arsenin, V. Y. (1977). Solution of ill-posed problems. Washington: Winston & Sons.

Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301–320.


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Acerca de los Autores

Javier Humberto Ospina-Holguín, Universidad del Valle

Javier Humberto Ospina-Holguín es Profesor Asociado del Departamento de contabilidad y Finanzas de la Universidad del Valle, es Doctor en Administración, Magíster en Ciencias de la Organización y Físico de la misma universidad y Máster en Ciencias en Economía de la Universidad de Ámsterdam.

Ana Milena Ospina-Holguín, Universidad del Valle

Es Profesora Auxiliar del Departamento de Administración y Organizaciones, es Doctora en Administración, Magíster en Ciencias de la Organización y Administradora de la misma universidad.

Cómo citar
Ospina-Holguín, J., & Ospina-Holguín, A. (2019). Penalised regressions vs. autoregressive moving average models for forecasting inflation. ECONÓMICAS CUC, 41(1).
Artículos: Economía y Finanzas