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How far does monetary policy reach? Evidence from factor-augmented vector autoregressions for Poland

Author

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  • Mariusz Kapuściński

Abstract

This study applies factor-augmented vector autoregressions to identify the effects of monetary policy shocks in a small, open, emerging market economy. It uses data on 132 variables for Poland, ‘compressing’ them to either structural (having an economic interpretation) or economically uninterpretable factors, also known as diffusion indices. The tightening of monetary policy is found to have broad, contractionary effects. Among other things, production, building permits, retail trade, employment, job offers, prices, wages, loans and stock prices decrease, unemployment and non-performing loans increase. However, a rise in the interest rate does not appear to be associated with an appreciation of the exchange rate. But this result is not robust among studies using vector autoregressions, which calls for a different strategy to identify the causal effect. As one of extensions, the effects of changes in global and foreign factors are investigated. Domestic prices are found to respond to global prices of commodities and foreign prices. Domestic production and interest rates – to their foreign counterparts.

Suggested Citation

  • Mariusz Kapuściński, 2017. "How far does monetary policy reach? Evidence from factor-augmented vector autoregressions for Poland," NBP Working Papers 273, Narodowy Bank Polski.
    • Handle: RePEc:nbp:nbpmis:273
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    References listed on IDEAS

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    1. Robert Socha, 2014. "Asymetria relacji cen paliw płynnych w Polsce i cen ropy naftowej," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 5, pages 133-160.
    2. Szafranek, Karol, 2017. "Flattening of the New Keynesian Phillips curve: Evidence for an emerging, small open economy," Economic Modelling, Elsevier, vol. 63(C), pages 334-348.
    3. Karol Szafranek & Aleksandra Hałka, 2019. "Determinants of Low Inflation in an Emerging, Small Open Economy through the Lens of Aggregated and Disaggregated Approach," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 55(13), pages 3094-3111, October.
    4. Sims, Christopher A., 1992. "Interpreting the macroeconomic time series facts : The effects of monetary policy," European Economic Review, Elsevier, vol. 36(5), pages 975-1000, June.
    5. Szafranek, Karol, 2019. "Bagged neural networks for forecasting Polish (low) inflation," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1042-1059.
    6. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
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    More about this item

    Keywords

    factor analysis; vector autoregressions; factor-augmented vector autoregressions; high-frequency identification; monetary transmission mechanism;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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