IDEAS home Printed from https://ideas.repec.org/p/zbw/bubtps/305277.html
   My bibliography  Save this paper

Forecasting HICP package holidays with forward-looking booking data

Author

Listed:
  • Schnorrenberger, Richard
  • Schwind, Patrick
  • Wieland, Elisabeth

Abstract

Forecasting consumer prices for package holidays, which represent a major driver of the inflation rate in Germany, poses some practical challenges. With a substantial share in the underlying consumer basket, prices for package holidays exhibit strong seasonality, notable volatility, and methodological breaks. We present two modelling strategies for predicting this volatile component based on the unadjusted price series and the seasonally adjusted series. Moreover, we exploit the forward-looking dimension of high-frequency booking data to compile a price indicator that provides early signals about the underlying trend of the target series. Our forecasting exercise shows that accurate forecasts are obtained with a modelling strategy tailored to the seasonally adjusted target series, alongside precise projections of the future seasonal component. Finally, augmenting the forecasting model with the forwardlooking price indicator yields considerable gains that increase with the forecast horizon. Specifically, adding forward-looking information to the best-performing model increases the nowcast precision by 2.6% to 8% for short-term horizons of one to seven months, and the improvement exceeds 17% for longer horizons.

Suggested Citation

  • Schnorrenberger, Richard & Schwind, Patrick & Wieland, Elisabeth, 2024. "Forecasting HICP package holidays with forward-looking booking data," Technical Papers 04/2024, Deutsche Bundesbank.
  • Handle: RePEc:zbw:bubtps:305277
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/305277/1/1906970645.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    2. Dietrich, Andreas & Eiglsperger, Martin & Mehrhoff, Jens & Wieland, Elisabeth, 2021. "Chain linking over December and methodological changes in the HICP: view from a central bank perspective," Statistics Paper Series 40, European Central Bank.
    3. Eiglsperger, Martin, 2019. "A new method for the package holiday price index in Germany and its impact on HICP inflation rates," Economic Bulletin Boxes, European Central Bank, vol. 2.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Beck, Günter W. & Carstensen, Kai & Menz, Jan-Oliver & Schnorrenberger, Richard & Wieland, Elisabeth, 2023. "Nowcasting consumer price inflation using high-frequency scanner data: Evidence from Germany," Discussion Papers 34/2023, Deutsche Bundesbank.
    2. Anatoly A. Peresetsky & Ruslan I. Yakubov, 2017. "Autocorrelation in an unobservable global trend: does it help to forecast market returns?," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 7(1/2), pages 152-169.
    3. Kelly Burns & Imad Moosa, 2017. "Demystifying the Meese–Rogoff puzzle: structural breaks or measures of forecasting accuracy?," Applied Economics, Taylor & Francis Journals, vol. 49(48), pages 4897-4910, October.
    4. João C. Claudio & Katja Heinisch & Oliver Holtemöller, 2020. "Nowcasting East German GDP growth: a MIDAS approach," Empirical Economics, Springer, vol. 58(1), pages 29-54, January.
    5. Antonio Rubia & Trino-Manuel Ñíguez, 2006. "Forecasting the conditional covariance matrix of a portfolio under long-run temporal dependence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(6), pages 439-458.
    6. Athanasopoulos, George & de Carvalho Guillén, Osmani Teixeira & Issler, João Victor & Vahid, Farshid, 2011. "Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions," Journal of Econometrics, Elsevier, vol. 164(1), pages 116-129, September.
    7. Hao Chen & Qiulan Wan & Yurong Wang, 2014. "Refined Diebold-Mariano Test Methods for the Evaluation of Wind Power Forecasting Models," Energies, MDPI, vol. 7(7), pages 1-14, July.
    8. Antonello D’Agostino & Kieran Mcquinn & Karl Whelan, 2012. "Are Some Forecasters Really Better Than Others?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(4), pages 715-732, June.
    9. Cavit Pakel & Neil Shephard & Kevin Sheppard, 2009. "Nuisance parameters, composite likelihoods and a panel of GARCH models," Economics Papers 2009-W12, Economics Group, Nuffield College, University of Oxford.
    10. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Post-Print halshs-01442618, HAL.
    11. Carlo Altavilla & Paul De Grauwe, 2010. "Forecasting and combining competing models of exchange rate determination," Applied Economics, Taylor & Francis Journals, vol. 42(27), pages 3455-3480.
    12. Matsumura, Marco & Moreira, Ajax & Vicente, José, 2011. "Forecasting the yield curve with linear factor models," International Review of Financial Analysis, Elsevier, vol. 20(5), pages 237-243.
    13. Carlo Altavilla & Raffaella Giacomini & Giuseppe Ragusa, 2017. "Anchoring the yield curve using survey expectations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(6), pages 1055-1068, September.
    14. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020. "Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss," Journal of International Money and Finance, Elsevier, vol. 104(C).
    15. Castro, Luciano de & Galvao, Antonio F. & Kim, Jeong Yeol & Montes-Rojas, Gabriel & Olmo, Jose, 2022. "Experiments on portfolio selection: A comparison between quantile preferences and expected utility decision models," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 97(C).
    16. Vitek, Francis, 2006. "Measuring the Stance of Monetary Policy in a Small Open Economy: A Dynamic Stochastic General Equilibrium Approach," MPRA Paper 802, University Library of Munich, Germany.
    17. Grace Lee Ching Yap, 2020. "Optimal Filter Approximations for Latent Long Memory Stochastic Volatility," Computational Economics, Springer;Society for Computational Economics, vol. 56(2), pages 547-568, August.
    18. Xiaojie Xu, 2017. "The rolling causal structure between the Chinese stock index and futures," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 31(4), pages 491-509, November.
    19. Xilong Chen & Eric Ghysels, 2011. "News--Good or Bad--and Its Impact on Volatility Predictions over Multiple Horizons," The Review of Financial Studies, Society for Financial Studies, vol. 24(1), pages 46-81, October.
    20. Faria, Gonçalo & Verona, Fabio, 2023. "Forecast combination in the frequency domain," Bank of Finland Research Discussion Papers 1/2023, Bank of Finland.

    More about this item

    Keywords

    Inflation forecasting; consumer prices; seasonality; travel booking data;
    All these keywords.

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:bubtps:305277. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/dbbgvde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.