C3 Multiple or Simultaneous Equation Models
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Institute
Commodity connectedness
(2017)
We use variance decompositions from high-dimensional vector autoregressions to characterize connectedness in 19 key commodity return volatilities, 2011-2016. We study both static (full-sample) and dynamic (rolling-sample) connectedness. We summarize and visualize the results using tools from network analysis. The results reveal clear clustering of commodities into groups that match traditional industry groupings, but with some notable differences. The energy sector is most important in terms of sending shocks to others, and energy, industrial metals, and precious metals are themselves tightly connected.
Microeconometric evidence on demand-side real rigidity and
implications for monetary non-neutrality
(2016)
To model the observed slow response of aggregate real variables to nominal shocks, most macroeconomic models incorporate real rigidities in addition to nominal rigidities. One popular way of modelling such a real rigidity is to assume a non-constant demand elasticity. By using a homescan data set for three European countries, including prices and quantities bought for a large number of goods, in addition to consumer characteristics, we provide estimates of price elasticities of demand and on the degree of demand-side real rigidities. We find that price elasticites of demand are about 4 in the median. Furthermore, we find evidence for demand-side real rigidities. These are, however, much smaller than what is often assumed in macroeconomic models. The median estimate for demand-side real rigidity, the super-elasticity, is in a range between 1 and 2. To quantitatively assess the implications of our empirical estimates, we calibrate a menu-cost model with the estimated super-elasticity. We find that the degree of monetary non-neutrality doubles in the model including demand-side real rigidity, compared to the model with only nominal rigidity, suggesting a multiplier effect of around two. However, the model can explain only up to 6% of the monetary non-neutrality observed in the data, implying that additional multipliers are necessary to match the behavior of aggregate variables.
We use a novel disaggregate sectoral euro area dataset with a regional breakdown that allows explicit estimation of the sectoral component of price changes (rather than interpreting the idiosyncratic component as sectoral as done in other papers). Employing a new method to extract factors from over-lapping data blocks, we find for our euro area data set that the sectoral component explains much less of the variation in sectoral regional inflation rates and exhibits much less volatility than previous findings for the US indicate. Country- and region-specific factors play an important role in addition to the sector-specific factors. We conclude that sectoral price changes have a “geographical” dimension, as yet unexplored in the literature, that might lead to new insights regarding the properties of sectoral price changes.
