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

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Bayesian FAVARs with Agnostic Identification 9 estimates of the impact of monetary policy shocks and changes in policy on various sectors of the economy, if there exists no reasonable objective means of determining the direction and size of changes in policy stances 6 . Monetary policy decisions are implicitly assumed to take place in a ”data-rich envi- ronment” 7 , reflecting that the central bankers analyze a vast amount of economic time series 8 prior to their policy decisions taken. The fact that central bankers bear this huge cost to analyze such a plethora of data and assuming that they do not have a great irrational interest in wasting their time with dispensable data analysis, should provide the indication that a broad set of ”information” is of great relevance for the decisions to be taken. Hence much more variables would have to be considered for example if a researcher would be interested to model the monetary transmission mechanism, or the Fed’s reaction function. The limitation does not come from the modeling researchers ig- noring the importance of the large data set, but from the methodologies mainly applied. The advantage of the SVAR framework is that it is small and therefore more tractable and easier to compute. Bernanke and Boivin (2003) were the first who tried to tackle the problem of ”information dimensions” in the context of monetary policy in a framework that combines the advances of dynamic factor models with standard VAR analysis. They ”explore the feasibility to incorporate a richer information set into the analysis of positive and normative Fed policy making” 9 . They achieve this through a factor model approach based on the work of Stock and Watson (1999,2002) and Watson (2000). Dynamic factor models that were introduced to economics by the seminal papers of Geweke (1977) and Sargent and Sims (1977), put the classical factor model, that only regards cross-sectional data, in a dynamic setting. These models have become increasingly popular since the work by Stock and Watson (1989,1999,2003) that outperformed the forecasting accuracy of the 6 See Bernanke and Mihov (1998a) 7 see Bernanke and Boivin (2003) 8 Bernanke and Boivin (2003), state that central bankers literally monitor several hundreds or even up to thousand time series. 9 See Bernanke, Boivin [2003]
10 Bayesian FAVARs with Agnostic Identification standard autoregression approach, but only for the real variables except employment. A lot of good research has revealed advances to these models, in particular w.r.t. the esti- mation procedure that allows for large datasets, in length and dimension, so that it can be applied to the economic question at hand. Hence one can overcome the dimensionality problem faced w.r.t. the large datasets to be included. The advantage of factor models is that the main driving forces in a large set of cross-sectional data, which in our case is required to consider, can be represented by a much smaller number of ”factors” extracted from the dataset. For our analysis I decide to apply the so-called FAVAR 10 methodology which was introduced in Bernanke and Boivin (2003), advanced in Bernanke, Boivin and Eliasz (2005). Stock and Watson (2005) added some variations to it, and provide a broad survey on its different approaches. These models exploit the advances of dynamic factor models and combine them with the VAR methodology. And the crucial innovation is to combine the Bayesian FAVAR with an Agnostic identification. The question of identification is an issue that has been covered a huge body of lit- erature and applied to SVAR. The most prevalent schemes are the Recursive Cholesky identification, the Long-run Identification, the combination of the two (Zero restrictions; Leeper, Sims and Zha [1996]) and the Agnostic Identification introduced by Uhlig [2005]. In his paper Uhlig also seeks to measure the effects of a shock to monetary policy, strictly speaking to a ”contractionary” monetary policy shock, in particular he focuses on the effects on Output and finds out that there is no clear effect on output, and that the neutrality of monetary policy shocks are not inconsistent with the data. But what is so crucial about the paper by Uhlig is the new more sophisticated identification scheme he introduces, namely the ”agnostic identification” 11 which imposes for a certain period of time sign-restrictions on the impulse responses, that are consistent with the conventional wisdom. 10 This terminology comes from Bernanke, Boivin and Eliaz [2005] 11 The term agnostic refers to the missing restriction on the variable to be analysed.
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