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What drives inflation and how? Evidence from additive mixed models selected by cAIC

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  • Philipp F. M. Baumann
  • Dr. Enzo Rossi
  • Alexander Volkmann

Abstract

We analyze the forces that explain inflation using a large panel of 122 countries from 1997 to 2015. Models motivated by the economic theory are compared to a boosting algorithm, and non-linearities and structural breaks are explicitly considered. The boosting algorithm outperforms theory-based models. Further, we provide compelling evidence that the interaction of energy price and energy rents stand out among 37 explanatory variables. Other important determinants are demographic developments. Contrary to common belief, globalization and technology, public debt, central bank independence and transparency as well as countries’ political characteristics, are less relevant. Exchange rate arrangements are more important than inflation-targeting regimes. Moreover, GDP per capita is more relevant than the output gap and credit growth is generally superior to M2 growth. Many predictors exhibit a structural break since the financial crisis. In particular, credit growth has lost its grip on the inflation process.

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  • Philipp F. M. Baumann & Dr. Enzo Rossi & Alexander Volkmann, 2021. "What drives inflation and how? Evidence from additive mixed models selected by cAIC," Working Papers 2021-12, Swiss National Bank.
  • Handle: RePEc:snb:snbwpa:2021-12
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    Cited by:

    1. Philipp Baumann & Enzo Rossi & Michael Schomaker, 2022. "Estimating the effect of central bank independence on inflation using longitudinal targeted maximum likelihood estimation," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Machine learning in central banking, volume 57, Bank for International Settlements.
    2. In Do Hwang & Dr. Enzo Rossi, 2020. "Does communication influence executives' opinion of central bank policy?," Working Papers 2020-17, Swiss National Bank.

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    More about this item

    Keywords

    Theories of inflation; longitudinal data; additive mixed models; model-based boosting; conditional Akaike criterion;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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