Swedish paper finds benefit in increasing lag-length in VARs
A working paper published by the Sveriges Riksbank today says that increasing the lag length in structural vector autoregressions (VARs) can reduce the bias and variance in data generated through an underlying dynamic stochastic general equilibrium model.
The paper, Un-truncating VARs by Ferre De Graeve and Andreas Westermark, acknowledges that increasing the lag length "rapidly "increases the number of parameters and thereby reduces the degrees of freedom and makes the confidence bandwidth
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