Sectoral Output Effects of Monetary Policy: Do Sticky Prices Matter?

Henkel, Lukas

doi:10.2139/ssrn.3703656

Cited by 4 publications

(1 citation statement)

References 31 publications

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“…A standard prediction of sticky price models is that the repricing rate is a key predictor of the real effects of monetary policy. For instance, Henkel (2020) shows that the output reaction of US manufacturing industries to monetary policy shocks is systematically related to an industry degree of price stickiness. For the euro area, show that inflation in sectors with a higher repricing rate reacts more strongly to a monetary policy shock.…”

Section: Further Micro-based Evidence On State Dependence and Selectionmentioning

confidence: 99%

Some Implications of Micro Price-Setting Evidence for Inflation Dynamics and Monetary Transmission

Dedola,

Gautier,

Nakov

et al. 2023

SSRN Journal

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Section: Further Micro-based Evidence On State Dependence and Selectionmentioning

confidence: 99%

Some Implications of Micro Price-Setting Evidence for Inflation Dynamics and Monetary Transmission

Dedola,

Gautier,

Nakov

et al. 2023

SSRN Journal

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Economic Policy Uncertainty and Industrial Linkage in Japan: A Granger Causality in Quantile Test

Long

Morgan

2022

Emerging Markets Finance and Trade

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Forecasting Russia’s GDP through the production method

Krupkina

Vinogradova

Orlova

et al. 2022

Vestnik Moskovskogo universiteta. Seriâ 6, Èkonomika

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The paper analyses the variables covering real, financial and external sectors of the economy alongside various sectoral, price and survey indicators. We have obtained forecast values of gross value added by industry and an aggregated estimate of Russia’s productionbased GDP. Drawing on dynamic factor model (DFM) as the main approach, we obtained quarterly point forecasts for production-based Russian GDP and for individual sectors for 2011-21. The forecast accuracy is compared to Bayesian vector autoregression (BVAR),s imple benchmarks based on aggregated and disaggregated GDP modeling and consensus expert forecasts. The results of the study show that the dynamic factor model outperforms the benchmarks and, in some cases, also BVAR in forecast ability (measured on the basis of an out-of-sample forecast error). The superiority of the factor model can be traced back to its ability to capture sectoral information on the gross value added of individual industries. The covariance matrix analysis of sectoral forecast errors confirms that the factor model superiority is based on its ability to capture sectoral dynamics more accurately, especially during the periods of high volatility. The dynamic evaluation of point forecasts for 4 quarters ahead and comparison of modeling results with consensus forecasts of experts shows that forecasting based on the DFM model for production allows for more stable and consistent results.

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Sectoral Output Effects of Monetary Policy: Do Sticky Prices Matter?

Cited by 4 publications

References 31 publications

Some Implications of Micro Price-Setting Evidence for Inflation Dynamics and Monetary Transmission

Some Implications of Micro Price-Setting Evidence for Inflation Dynamics and Monetary Transmission

Economic Policy Uncertainty and Industrial Linkage in Japan: A Granger Causality in Quantile Test

Forecasting Russia’s GDP through the production method

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