Signing Out Confounding Shocks in Variance-Maximizing Identification Methods
Recent papers have examined the dominant drivers of business cycles using variance-maximizing techniques for identification. However, identification is poor when shocks other than the target of interest play large roles in driving volatility at the targeted frequency or horizon, leading them to capt...
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| Veröffentlicht in: | AEA papers and proceedings 2022-05, Vol.112, p.476-480 |
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| creator | Francis, Neville Kindberg-Hanlon, Gene |
| description | Recent papers have examined the dominant drivers of business cycles using variance-maximizing techniques for identification. However, identification is poor when shocks other than the target of interest play large roles in driving volatility at the targeted frequency or horizon, leading them to capture a "hybrid" shock. This paper suggests a simple fix that lowers biases in the impulse responses. The fix is to include theoretically informed sign and magnitude restrictions at the identification stage of the vector autoregression. Applying this to US data, we find an equal role for demand and supply shocks in generating business cycle fluctuations. |
| doi_str_mv | 10.1257/pandp.20221046 |
| format | Article |
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| source | American Economic Association Web; EBSCOhost Business Source Complete |
| subjects | 1953– 2018 ESTIMATION OF DYNAMIC CAUSAL EFFECTS IN MACRO: PROMISES AND PITFALLS Konjunkturforschung Neoklassische Synthese Schock USA VAR-Modell Varianzanalyse Volatilität |
| title | Signing Out Confounding Shocks in Variance-Maximizing Identification Methods |
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