5.4 Variance Decomposition of Business Cycle Fluctuations
This section investigates the contribution of each of the structural shocks to the forecast
error variance of the observable variables in the models, i.e. the underlying sources of
fluctuations, at various horizons. The results are based on the models’ posterior distribution
reported in Table 3. The focus of this exercise is on our ‘preferred’ 2-sector NK model and
the results are summarized in Table 8.
In the short run, within a year (t=1,4), movements in real GDP are primarily driven
by supply-side shocks (with the dominant influence of around 60%). For instance, most of
the unexpected output fluctuations are mainly explained by the technology shock in the
informal sector (around 50%) and the price mark-up shock in the informal sector (around
3-7%). For the shocks that are associated to demand innovations, the government spending
shock together with the risk premium shock account for about 30-40% of the output forecast
error variance at the one-year horizon.
Not surprisingly, in the medium run the supply shocks and the risk premium shock
together continue to dominate, and the government spending shock also explains around
22% of India’s output variability. In the long run, the technology shock affecting the
informal sector, the exogenous spending shock and the risk premium shock dominate, but
the contribution of technology shock affecting the informal sector to output variability
seems to become smaller from medium to long run and the risk premium shock seems to
explain a bigger part of the long-run variations in output. In contrast, the monetary policy
shock and mark-up shock in the formal sector do not seem to matter for output variability,
regardless of forecast horizon.
Under the estimated interest rate, we find that the main determinant of the nominal
interest rate in India is the risk premium shock, which explains around 46% to 76% of its
forecast error variance from the short run to the long run. The second largest component
is the government spending shock within a year (25%) and the productivity shock in the
informal sector and in the short run (around 20%). This finding highlights the important
role of productivity factors over the informal sector in the Indian economy. Interestingly,
the shock that explains most of CPI inflation variance is the monetary policy shock. We
find that it is by far the most dominant influence to the inflation variability (over 70%) at
all forecast horizons. In contrast, our estimated 2-sector NK model shows that inflation
fluctuations are almost unaffected by either the productivity shock affecting the formal
sector or price mark-up shocks and there are only limited effects on inflation from the
informal productivity shock and various demand shocks. One reason for this is, according
to Smets and Wouters (2007), that the estimated slope of the NK Phillips curve is very
small (in particular in the formal sector), so that only large and persistent changes in the
marginal cost will have an impact on inflation. Finally, the risk premium and government
spending shocks together explain the largest part of investment in both the short run and
the long run.
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