Estimating large-dimensional connectedness tables: The great moderation through the lens of sectoral spillovers

Arbeitspapier

We estimate sectoral spillovers around the Great Moderation with the help of forecast error variance decomposition tables. Obtaining such tables in high dimensions is challenging since they are functions of the estimated vector autoregressive coefficients and the residual covariance matrix. In a simulation study, we compare various regularization methods for both and conduct a comprehensive analysis of their performance. We show that standard estimators of large connectedness tables lead to biased results and high estimation uncertainty, which can both be mitigated by regularization. To explore possible causes for the Great Moderation, we apply a cross-validated estimator on sectoral spillovers of industrial production in the US from 1972 to 2007. We find that a handful of sectors considerably decreased their outgoing links, which hints at a complimentary explanation for the Great Moderation.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Bank of Canada Staff Working Paper ; No. 2021-37

Classification and Topics

• Classification: Wirtschaft ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes ; Model Evaluation, Validation, and Selection ; Macroeconomics: Production ; Macroeconomics: Consumption, Saving, Production, Employment, and Investment: Forecasting and Simulation: Models and Applications
• Subject: Business fluctuations and cycles ; Econometric and statistical methods

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Brunner, Felix ; Hipp, Ruben
• Event: Veröffentlichung
• (who): Bank of Canada
• (where): Ottawa
• (when): 2021

Further information

• DOI: doi:10.34989/swp-2021-37
• Handle: http://hdl.handle.net/10419/247417
• Last update: 10.03.2025, 11:41 AM CET