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Bayesian averaging of classical estimates in forecasting macroeconomic indicators with application of business survey data

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  • Piotr Białowolski
  • Tomasz Kuszewski
  • Bartosz Witkowski

Abstract

In this paper, we develop a methodology for forecasting key macroeconomic indicators, based on business survey data. We estimate a large set of models, using an autoregressive specification, with regressors selected from business and household survey data. Our methodology is based on the Bayesian averaging of classical estimates method. Additionally, we examine the impact of deterministic and stochastic seasonality of the business survey time series on the outcome of the forecasting process. We propose an intuitive procedure for incorporating both types of seasonality into the forecasting process. After estimating the specified models, we check the accuracy of the forecasts. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Piotr Białowolski & Tomasz Kuszewski & Bartosz Witkowski, 2014. "Bayesian averaging of classical estimates in forecasting macroeconomic indicators with application of business survey data," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 41(1), pages 53-68, February.
    • Handle: RePEc:kap:empiri:v:41:y:2014:i:1:p:53-68
    DOI: 10.1007/s10663-013-9227-x
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    References listed on IDEAS

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    1. Xavier Sala-I-Martin & Gernot Doppelhofer & Ronald I. Miller, 2004. "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach," American Economic Review, American Economic Association, vol. 94(4), pages 813-835, September.
    2. Piotr Białowolski & Tomasz Kuszewski & Bartosz Witkowski, 2010. "Business Survey Data in Forecasting Macroeconomic Indicators with Combined Forecasts," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 4(4), December.
    3. Lof, Marten & Hans Franses, Philip, 2001. "On forecasting cointegrated seasonal time series," International Journal of Forecasting, Elsevier, vol. 17(4), pages 607-621.
    4. Próchniak, Mariusz & Witkowski, Bartosz, 2013. "Time stability of the beta convergence among EU countries: Bayesian model averaging perspective," Economic Modelling, Elsevier, vol. 30(C), pages 322-333.
    5. Moral-Benito, Enrique, 2010. "Model averaging in economics," MPRA Paper 26047, University Library of Munich, Germany.
    6. Kevin Clinton & Marianne Johnson & Mr. Jaromir Benes & Mr. Douglas Laxton & Mr. Troy D Matheson, 2010. "Structural Models in Real Time," IMF Working Papers 2010/056, International Monetary Fund.
    7. Jaewoo Lee & Pau Rabanal & Damiano Sandri, 2010. "U.S. Consumption after the 2008 Crisis," IMF Staff Position Notes 2010/01, International Monetary Fund.
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    Citations

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    Cited by:

    1. Marcin Błażejowski & Jacek Kwiatkowski & Paweł Kufel, 2020. "BACE and BMA Variable Selection and Forecasting for UK Money Demand and Inflation with Gretl," Econometrics, MDPI, vol. 8(2), pages 1-29, May.
    2. Marcin Blazejowski & Jacek Kwiatkowski, 2018. "Bayesian Averaging of Classical Estimates (BACE) for gretl," gretl working papers 6, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    3. Mariusz Prochniak & Katarzyna Wasiak, 2017. "The impact of the financial system on economic growth in the context of the global crisis: empirical evidence for the EU and OECD countries," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 44(2), pages 295-337, May.
    4. Martin Feldkircher & Florian Huber & Josef Schreiner & Marcel Tirpák & Peter Tóth & Julia Wörz, 2015. "Bridging the information gap: small-scale nowcasting models of GDP growth for selected CESEE countries," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue 2, pages 56-75.
    5. Martin Feldkircher & Florian Huber & Josef Schreiner & Julia Woerz & Marcel Tirpak & Peter Toth, 2015. "Small-scale nowcasting models of GDP for selected CESEE countries," Working and Discussion Papers WP 4/2015, Research Department, National Bank of Slovakia.
    6. Piotr Białowolski, 2016. "The influence of negative response style on survey-based household inflation expectations," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(2), pages 509-528, March.
    7. Miros³aw Szreder, 2015. "Probabilistic aspects of risk management (Probabilistyczne aspekty zarz¹dzania ryzykiem)," Problemy Zarzadzania, University of Warsaw, Faculty of Management, vol. 13(55), pages 47-55.
    8. Mariusz Próchniak & Katarzyna Wasiak, 2016. "The impact of macroeconomic performance on the stability of financial system in the EU countries," Collegium of Economic Analysis Annals, Warsaw School of Economics, Collegium of Economic Analysis, issue 41, pages 145-160.
    9. Rajat Deb & Prasenjit Deb & Sujit Majumder & Sourav Chakraborty & Kiran Sankar Chakraborty, 2019. "Answering Savings Puzzle About Small Saving Schemes and Mutual Funds: Evidence from Tripura," Metamorphosis: A Journal of Management Research, , vol. 18(1), pages 7-19, June.
    10. Piotr Białowolski, 2015. "Concepts of Confidence in Tendency Survey Research: An Assessment with Multi-group Confirmatory Factor Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 123(1), pages 281-302, August.

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    More about this item

    Keywords

    Bayesian averaging of classical estimates; Business survey data; Seasonality; Automatic forecasting; C10; C83; E32; E37;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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