Discovering prediction models for Brazilian inflation rate: an analysis based on a great number of series
DOI:
https://doi.org/10.1590/0101-41614833aseKeywords:
inflation, forecasting, model selection, autometrics, model confidence setAbstract
This work evaluates the prediction capabilities of econometric time series models based on macroeconomics indicators for Brazilian consumer price index (IPCA). We run a pseudo real time prediction exercise with twelve steps ahead horizon. Predictions are compared with univariate models such as the first order autoregressive model among others. The sample period goes from January 2000 to August 2015. We evaluated over 1170 different economic variable for each forecast period, searching for the best predictor set in each point in time using Autometrics algorithm as model selector. Models’ performance is compared using Model Confidence Set, developed by Hansen, Lunde and Nason (2010). Our results suggest possible gains in predictions that use a high number of indicators particularly at longer horizons.
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References
Akaike, H. 1973. “Information theory and an extension of the likelihood principle”. In B. Petrov and F. Czaki
(Eds.), Second International Symposium on Information Theory, pp. 267-287. Budapest: Akademia Kiado.
Atkeson, A., & Ohanian, L. E. 2001. “Are Phillips curves useful for forecasting inflation?” Federal Reserve Bank
of Minneapolis Quarterly Review 25(1): 2-11.
Bader, F. L. C., Koyama, S. M., & Tsuchida, M. H. 2014. “Modelo FAVAR Canônico para Previsão do Mercado
de Crédito” 369.
Bernanke, B., Boivin, J., Eliasz, P. 2005. “Measuring the effects of monetary policy: a factor-augmented vector
autoregressive (FAVAR) approach”. Quarterly Journal of Economics 120: 387–422.
Box, G. E. P., Jenkins, G. M. 1976. “Time Series Analysis: Forecasting and Control” (2nd ed.). San Francisco, CA: Holden-Day.
Campos, J., & Ericsson, N. R. 1999. “Constructive data mining: modeling consumers’ expenditure in Venezuela”. The Econometrics Journal 2(2): 226-240.
Campos, J., Hendry, D. F., & Krolzig, H. M. 2003. “Consistent Model Selection by an Automatic Gets Approach”. Oxford Bulletin of Economics and Statistics 65(s1): 803-819.
Carlo, T C.; Marçal, E F. 2016. “Forecasting Brazilian inflation by its aggregate and disaggregated data: a test of predictive power by forecast horizon”. Applied Economics 48(50): 4846-4860.
Castle, J.L., Hendry, D.F., Clements, M. P. 2011. “Forecasting by Factors, by Variables, by Both, or Neither?” Working paper, Economics Department, University of Oxford.
Castle, J. L., Qin, X., & Robert Reed, W. 2013. “Using model selection algorithms to obtain reliable coefficient estimates”. Journal of Economic Surveys 27(2): 269-296.
Castle, J. L., Doornik, J. A., & Hendry, D. F. 2011. “Evaluating automatic model selection”. Journal of Time Series Econometrics 3(1).
Cecchetti, S. G., Chu, R. S., & Steindel, C. 2000. “The unreliability of inflation indicators”. Current Issues in
Economics and Finance 6(4): 1-6.
Clements, M. P., & Hendry, D. F. 1993. “On the limitations of comparing mean square forecast errors”. Journal of Forecasting 12(8): 617-637.
Dees, S., Mauro, F. D., Pesaran, M. H., & Smith, L. V. 2007. “Exploring the international linkages of the euro area: a global VAR analysis”. Journal of Applied Econometrics 22(1): 1-38.
Dickey, D. A., & Fuller, W. A. 1979. “Distribution of the estimators for autoregressive time series with a unitroot”. Journal of the American statistical association 74(366a): 427-431.
Doornik, J. A., & Ooms, M. 2007. “Introduction to Ox: An Object-Oriented Matrix Language”.
Doornik, J. A. 2009. “Autometrics. In in Honour of David F. Hendry”.
Doornik & Hendry. 2014. “Empirical Model Discovery and Theory Evaluation, Arne Ryde Memorial Lectures”, MIT Press.
Doornik & Hendry. 2015. “Statistical model selection with “Big Data” Cogent Economics & Finance 3: 1045216
Doornik, J. A., Hendry, D. F., & Pretis, F. 2013. “Step-indicator saturation”. Discussion Paper 658.
Figueiredo, F.M.R. 2010. “Forecasting Brazilian inflation using a large data set”. Central Bank of Brazil Working Paper 228.
Figueiredo, F.M.R., Guillén, O.T.C. 2013. “Forecasting Brazilian consumer inflation with FAVAR models using target variables”, mimeo.
Granger, C. W. J., Anderson, A. P. 1978. “Introduction to Bilinear Time Series Models”. Vandenhoeck and Puprecht. Estud. Econ., São Paulo, vol.48 n.3, p. 423-450, jul.-set. 2018
Anderson Moriya Silva e Emerson Fernandes Marçal
Hansen, P. R. 2005. “A Test for Superior Predictive Ability”. Journal of Business and Economic Statistics 23: 365–380.
Hansen, P. R., Lunde, A., & Nason, J. M. 2010. “The model confidence set”. Available at SSRN 522382.
Hendry, D. F. 1987. “Econometric methodology: A personal perspective”. Advances in econometrics 2: 29-48.
Hendry, D. F. 1995. “Dynamic econometrics”. Oxford University Press.
Hendry, D. F. 2005. “Predictive failure and econometric modelling in macroeconomics: The transactions demand
for money”. In General-to-specific modelling, pp. 535-560, Edward Elgar.
Hendry, D. F., & Nielsen, B. 2007. “Econometric modeling: a likelihood approach”. Princeton University Press.
Hoover, K. D., & Perez, S. J. 1999. “Data mining reconsidered encompassing and the general‐to‐specific approach
to specification search”. The Econometrics Journal 2(2): 167-191.
Hendry, D. F., & Krolzig, H. M. 1999. “Improving on Data mining reconsidered by KD Hoover and SJ Perez”. The Econometrics Journal 202-219.
Kozicki, S., & Tinsley, P. A. 2001. “Shifting endpoints in the term structure of interest rates”. Journal of Monetary Economics 47(3): 613-652.
Makridakis, S., Andersen, A., Carbone, R., Fildes, R., Hibon, M., Lewandowski, R., & Winkler, R. 1982. “The accuracy of extrapolation (time series) methods: Results of a forecasting competition”. Journal of Forecasting 1(2): 111-153.
Makridakis, S., & Hibon, M. 2000. “The M3-Competition: results, conclusions and implications”. International Journal of Forecasting 16(4): 451-476.
Romano, J. P., & Wolf, M. 2005. “Stepwise multiple testing as formalized data snooping”. Econometrica 73(4): 1237-1282.
Pesaran, M. H., Schuermann, T., & Smith, L. V. 2009. “Forecasting economic and financial variables with global VARs”. International Journal of Forecasting 25(4): 642-675.
Santos, C., Hendry, D. F., & Johansen, S. 2008. “Automatic selection of indicators in a fully saturated regression”. Computational Statistics 23(2): 317-335.
Sims, C. A. 1980. “Macroeconomics and Reality”. Econometrica: Journal of the Econometric Society 1-48.
Stock, J. H., & Watson, M. W. 1989. “New indexes of coincident and leading economic indicators”. In NBER Macroeconomics Annual 1989(4): 351-409. MIT press.
Stock, J. H., Watson, M. W. 1998. “Diffusion indexes”. Working Paper 6702, NBER.
Stock, J. H., Watson, M. W. 2002. “Macroeconomic forecasting using diffusion indices”. Journal of Business and Economic Statistics 20(2): 147–162.
Stock, J. H., Watson, M.W. 1999. “Forecasting inflation”. Journal of Monetary Economics 44: 293–335.
Stock, J. H., & Watson, M. W. 2006. “Forecasting with many predictors”. Handbook of Economic Forecasting 1: 515-554.
Stock, J. H., & Watson, M. 2009. “Forecasting in dynamic factor models subject to structural instability”. The Methodology and Practice of Econometrics. A Festschrift in Honour of David F. Hendry 173, 205.
White, H. 1980. “A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity”. Econometrica: Journal of the Econometric Society 817-838.
White, H. 2000. “A Reality Check for Data Snooping”. Econometrica 68: 1097– 1126.
White, H. 1990. “A consistent model selection procedure based on m-testing”. Modelling Economic Series: Readings in Econometric Methodology, Clarendon Press, Oxford, 369-383.
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