Do Market Prices Improve the Accuracy of Inflation Forecasting in Poland? : a Disaggregated Approach

• Autorzy: Łukasz Lenart , Agnieszka Leszczyńska-Paczesna
• Języki publikacji: EN

Abstrakty

EN

This paper investigates short-term forecasts of Polish year-on-year (y-o-y) inflation using current market data and a disaggregated month-on-month (m-o-m) consumer price index (CPI). We propose a model based on a set of multivariate exponential smoothing models (ESM in short) and a simple nonlinear switching model. To this end, the total m-o-m CPI is disaggregated to six COICOP (4-digit) components (with an approx. 25% contribution in the total CPI) and the remaining part of the CPI. To improve forecasts accuracy (in particular in nowcasting) for each COICOP we use the available current market data on electricity, gas, food and petrol prices. We investigate and test the forecasting accuracy of the models with market data against benchmark models (without market prices) in a pseudo real-time framework. Our findings suggest that for most of the m-o-m components, the models with market prices outperform the considered benchmark models that use CIOCOP data sets only. (original abstract)

Słowa kluczowe

EN

Inflation; Forecasting; Switching model; Prices; Consumer price index (CPI)

PL

Inflacja; Prognozowanie; Model przełącznikowy; Ceny; Indeks cen konsumpcyjnych

• Czasopismo: Bank i Kredyt
• Rocznik: 2016
• Numer: nr 5
• Strony: 365--393

Twórcy

autor

Łukasz Lenart

• Narodowy Bank Polski; Cracow University of Economics, Poland

autor

Agnieszka Leszczyńska-Paczesna

• Narodowy Bank Polski; University of Lodz

Bibliografia

• Bańbura M., Modugno M. (2010), Maximum likelihood estimation of factor models on data sets with arbitrary pattern of missing data, ECB Working Paper, 1189.
• Corberan-Vallet A., Bermudez J., Vercher E. (2011), Forecasting correlated time series with exponential smoothing models, International Journal of Forecasting, 27(2), 252-265.
• Gaedner E. (1985), Exponential smoothing: the state of the art, Journal of Forecasting, 4(1), 1-28.
• Ghysels E., Santa-Clara O., Valkanov R. (2006), Predicting volatility: getting the most out of return data sampled at different frequencies, Journal of Econometrics, 131(1-2), 59-95.
• Giannone D., Reichlin L., Small D. (2008), Nowcasting: the real-time informational content of macroeconomic data, Journal of Monetary Economics, 55(4), 665-676.
• Gneiting T., Raftery A. (2007), Strictly proper scoring rules, prediction, and estimation, Journal of the American Statistical Association, 102(477), 359-378.
• Harvey D., Leybourne S., Newbold P. (1997), Testing the equality of prediction mean squared errors, International Journal of Forecasting, 13(2), 281-291.
• Hyndman R., Koehler A., Ord J., Snyder R. (2008), Forecasting with exponential smoothing, Springer.
• Hyndman R., Koehler A., Snyder R., Grose S. (2002), A state space framework for automatic forecasting using exponential smoothing methods, International Journal of Forecasting, 18(3), 439-454.
• Kan R. (2008), From moments of sum to moments of product, Journal of Multivariate Analysis, 99(3), 542-554.
• Lawton R. (1998), How should additive Holt-Winters estimates be corrected?, International Journal of Forecasting, 14(3), 393-403.
• Makridakis S., Hibon M. (2000), The M3-competition: results, conclusions and implications, International Journal of Forecasting, 16(4), 451-476.
• McNees S. (1990), The role of judgment in macroeconomic forecasting accuracy, International Journal of Forecasting, 6(3), 287-299.
• Modugno M. (2013), Now-casting inflation using high frequency data, International Journal of Forecasting, 29(4), 664-675.
• Sanders N., Ritzman L. (2001), Judgmental adjustment of statistical forecasts, in: J.S. Armstrong (ed.), Principles of forecasting, Springer.
• Sweet A. (1985), Computing the variance of the forecast error for the Holt-Winters seasonal models, Journal of Forecasting, 4(2), 235-243.
• ---