Niko Hauzenberger, Michael Pfarrhofer
arXiv 14 Nov 2019 · Econometrics · publishedScandinavian Journal of Economics (2021) · 1 citations (OpenAlex)
arXiv:1911.06206 · PDF · DOI · OpenAlex · Extracted main text
Understanding disaggregate channels in the transmission of monetary policy is of crucial importance for effectively implementing policy measures. We extend the empirical econometric literature on the role of production networks in the propagation of shocks along two dimensions. First, we allow for industry-specific responses that vary over time, reflecting non-linearities and cross-sectional heterogeneities in direct transmission channels. Second, we allow for time-varying network structures and dependence. This feature captures both variation in the structure of the production network, but also differences in cross-industry demand elasticities. We find that impacts vary substantially over time and the cross-section. Higher-order effects appear to be particularly important in periods of economic and financial uncertainty, often coinciding with tight credit market conditions and financial stress. Differentials in industry-specific responses can be explained by how close the respective industries are to end-consumers.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Ozdagli A, and Weber M (2020) Monetary policy through production networks: Evidence from the stock market | 1.000 | 15 | 3 | 100% |
| 2 | Gürkaynak RS, Sack BP, and Swanson ET (2005) Do actions speak louder than words? The response of asset prices to monetary policy actions and statements | 0.874 | 9 | 2 | 100% |
| 3 | Bernanke BS, and Kuttner KN (2005) What explains the stock market's reaction to Federal Reserve policy? | 0.874 | 6 | 2 | 100% |
| 4 | Chen SS (2007) Does monetary policy have asymmetric effects on stock returns? | 0.874 | 5 | 2 | 100% |
| 5 | Basistha A, and Kurov A (2008) Macroeconomic cycles and the stock market's reaction to monetary policy | 0.811 | 4 | 2 | 100% |
| 6 | Ehrmann M, and Fratzscher M (2004) Taking stock: Monetary policy transmission to equity markets | 0.737 | 3 | 2 | 100% |
| 7 | Baker SR, Bloom N, and Davis SJ (2016) Measuring Economic Policy Uncertainty | 0.644 | 4 | 1 | 100% |
| 8 | Husted L, Rogers J, and Sun B (2019) Monetary policy uncertainty | 0.644 | 4 | 1 | 100% |
| 9 | Frühwirth-Schnatter S, and Wagner H (2010) Stochastic model specification search for Gaussian and partial non-Gaussian state space models | 0.644 | 3 | 2 | 67% |
| 10 | Kurov A (2010) Investor sentiment and the stock market's reaction to monetary policy | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 54 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Interpreting and predicting the economy flows: A time-varying parameter global vector autoregressive integrated the machine learning model | 0.644 | 2 | 2 |