Wild Bootstrap Tests for Autocorrelation in Vector Autoregressive Models

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dc.contributor.author Ahlgren, Niklas
dc.contributor.author Catani, Paul
dc.date.accessioned 2012-09-11T08:18:56Z
dc.date.available 2012-09-11T08:18:56Z
dc.date.issued 2012-09-11
dc.identifier.isbn 978-952-232-178-7
dc.identifier.uri http://hdl.handle.net/10138/36634
dc.description.abstract Standard asymptotic and residual-based bootstrap tests for error autocorrela- tion are unreliable in the presence of conditional heteroskedasticity. In this article we propose wild bootstrap tests for autocorrelation in vector autoregressive mod- els when the errors are conditionally heteroskedastic. In particular, we investigate the properties of Lagrange multiplier tests. Monte Carlo simulations show that the wild bootstrap tests have satisfactory size properties in models with con- stant conditional correlation generalised autoregressive conditional heteroskedas- tic (CCC-GARCH) errors, whereas the standard asymptotic and residual-based bootstrap tests are oversized. The tests are applied to credit default swap prices and Euribor interest rates. fi
dc.language.iso en fi
dc.publisher Hanken School of Economics fi
dc.relation.ispartofseries Working Paper - 562 fi
dc.subject vector autoregressive models fi
dc.subject tests for error autocorrelation fi
dc.subject conditional hetroskedasticity fi
dc.subject wild bootstrap fi
dc.title Wild Bootstrap Tests for Autocorrelation in Vector Autoregressive Models fi
dc.type Working Paper fi

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