An Estimated DSGE Model of the Indian Economy.

model correctly points to a higher entrepreneurs survival rate and a lower leverage ratio
in the formal sector, the parameter χ is somewhat larger in the formal sector, but not
statistically different from the informal economy case.

One interesting aspect revealed by these estimates is that prices are estimated to be a
lot stickier in the formal sector, with an estimated frequency of price adjustments around
4 quarters, while it remains low for the informal sector, consistent with the estimates
obtained previously. This suggests that prices in the informal sector are more flexible.
However, the model confirms that there is a sizeable degree of wage rigidities, with the
estimated rW close to our prior. On the other hand, the estimated parameters capturing
the policy response to inflation largely confirm that the RBI appears to be quite aggressive
in preempting inflationary pressures, particularly expected future inflation. Indeed, θ is
again higher than 2, with a somewhat lower φ. The policy horizons are again estimated to
be on the short-term side, approximately 1 quarter.

Another distinct feature of these new estimates concerns the size of the standard er-
ror for the government spending shock. While most of the shocks appear to be of the
same magnitude as before, this model suggests that a great deal more volatility is is being
transmitted by the fiscal side of the economy. One possible explanation is that all sorts of
exogenous uncertainty, and potential misspecifications (particularly on the demand side)
are being picked up by this shock process. Note that we do not explicitly model fiscal
policy and we have circumscribed our analysis to the closed economy case. All in all, this
suggests that careful modelling of fiscal policy and the open economy might be required to
understand this result better.

5 Empirical Applications

After having shown the model estimates and the assessment of relative model fit to its other
rivals with different restrictions, we use them to investigate a number of key macroeconomic
issues in India. The model favoured in the space of competing models may still be poor (po-
tentially misspecified) in capturing the important dynamics in the data. To further evaluate
the absolute performance of one particular model against data, it is necessary to compare
the model’s implied characteristics with those of the actual data. Also in this section, we
address the following questions: (i) can the models capture the underlying characteristics of
the actual data? (ii) what are the driving forces of the observed business cycle fluctuations?
(iii) what are the impacts of the structural shocks on the main macroeconomic time series?

5.1 Further Model Validation

Summary statistics such as first and second moments have been standard as means of
validating models in the literature on DSGE models, especially in the RBC tradition. As
the Bayes factors (or posterior model odds) are used to assess the relative fit amongst a
number of competing models, the question of comparing the moments is whether the models

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