Measuring the Effects of a Shock to Monetary Policy - Humboldt ...

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Bayesian FAVARs with Agnostic Identification 49 Regarding the impulse responses one can see that most of them deliver tighter error bands than the results by BBE, which favors the alternative identification. In partic- ular regarding the commodity price index and the capacity utilization rate the results with Gibbs sampling combined with the agnostic identification seem not only more reasonable compared to the Gibbs sampling approach by BBE but also compared to their results estimated with the two-step PCA. There e.g. the capacity utilization rate and and the commodity price index increase increases directly after a shock. One of the striking results is the certainty with which the reactions of output (IP), CPI react and in par- ticular the moneytary aggregates react. Alltogether one can conclude that output still has an ambigous effect in that sense that all output variables considered in the dataset react identically and in the same direction. There are some that increase, but only very slightly. Therefore one can conclude that the results are not necessarily inconsistent with the monetary neutrality. All the responses have been calculated for the different horizons for sign restriction imposed. The results approved the ones by Uhlig [2005] in so far that the longer the restriction horizon the stronger is the reaction. The results reported have a restriction horizon of six month. As the results approve Uhlig and serve no new insights I decided, due to space limitations not provide the results in this thesis. There are more impulse responses provided in the Appendix(B) for different block criteria and number of factors specified. There results reporte Impulse responses for 42 variables out of the panel of 120. They are lables with the respective mnemonics reported in the data tabel in appendix(B). 8 Discussion This section will provide a brief discussion of the results presented and a critical assess- ment. Furthermore some suggestions for future research are given. The impulse responses
50 Bayesian FAVARs with Agnostic Identification presented serve the indication that, for the researcher interested in measuring the effects of a shock to monetary policy, it is crucial to apply identifying restrictions that are consistent with the conventional wisdom, such as the agnostic identification using sign restrictions. When comparing the results from the Gibbs sampling approach and com- pare them with the ones provided by BBE it is quite evident that the results seem more reasonable especially w.r.t. the quantitative measure and the certainty with with the results are reported. They do not show the great uncertainty as the results generated eith standard identification. The results are even more accurate some variables compared to the principal componant approach. Such variables are e.g. the commodity price index and the capacity utilization rate. However one should be cautious and still try out draws of at least 10000 in order to be conclusively certain with respect to the accuracy of the results. Although I have tried out many versions and several runs producing very similar results I think the results will still have to be confirmed with an iteration of say 10000 draws to be completely sure. This was not feasible due to severe time constraints and the lack of time an appropriate (fast) computer that has no memory problems. It is advisable to use an unix based system. The most interesting suggestion to me seem to be even more strict with the restriction in that sense that one should set the restrictions for many prices, money aggregates the some short term interest rates. This could be accomplished only partly as the acceptance of ”reasonable” impulse responses decreases sharply. Hence one should be patient whiöe waiting for the results. Further extensions w.r.t. the model could be to model time vary- ing factor loadings and stochastic volatility e.g.in order to analyze the change of monetary policy in a ”data-rich environment” over time 36 As a next step one could also start to identify further shocks and measure the respective effects like the one to fiscal policy in a FAVAR framework, as it has been done by Mountford and Uhlig [2004] in the VAR framework. 36 See Cogley and Sargent [2003].
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