An Estimated DSGE Model of the Indian Economy.

correctly predict population moments, such as the variables’ volatility or their correlation,
i.e. to assess the absolute fit of a model to macroeconomic data. In practice, one could
compute a Taylor approximation around the computed steady state of order one or two (for
a model that is not log-linearized). By computing approximated (Taylor) decision rules and
transition equations (the equations listing current values of the endogenous variables of the
model as a function of the previous state of the model and current shocks) for the model
by a perturbation method. At this stage, one could simulate the model and compute
the simulated moments by assuming that the shocks follow a normal distribution. In
what follows, all theoretical moments, correlation and autocorrelation coefficients, impulse
responses and variance decomposition are computed based on the solutions of estimated
models of order one.

5.2 Standard Moment Criteria

To assess the contributions of assuming different specifications in our estimated models, we
compute some selected second moments and present the results in this subsection. Tables
4 and 5 present the second moments implied by the above estimations and compares with
those in the actual data. In particular, we compute these model-implied statistics by solving
the models at the posterior means obtained from estimation. The results of the model’s
second moments are compared with the second moments in the actual data to evaluate the
models’ empirical performance.

First it is interesting to note that the data exhibit higher volatility in real GDP and
real investment for the US than for India. There are two reasons for this: i) the sample size
used for the US (1980:1-2006:4) is more than twice as large as that used for India (1996:1-
2008:4); ii) in the data transformation process in Section 2.5.2 we extract the cyclical
components in GDP and investment using different filters for the US and Indian data.
The differences in volatility are due to the filter used as each different filter extracts cycles
with properties statistically different from each other. The magnitude of the standard
deviations in the transformed data is affected by the choice of de-trending filters. The
relatively larger standard deviation in the US data occurs because a linear de-trending
filter does not remove entirely the low frequencies from the spectral density representation,
and leaves in the spectrum a large portion of persistent fluctuations (see Ferroni (2010) and
Section 6 for more discussions on data-filtering).

For the moment analysis in this subsection, we focus on the Indian economy. In terms of
the standard deviations, almost all models generate relative high volatility compared to the
actual data (except for the interest rate). By providing a feedback from the shocks to net
worth, the financial accelerator increases the volatilities of output and investment. Inflation
volatility is practically unchanged owing to the higher responsiveness (and volatility) of the
interest rate. Overall, the estimated models are able to reproduce acceptable volatility for
the main variables of the DSGE model. The inflation volatilities implied by the models are
close to that of the data. In line with the Bayesian model comparison, the NK models with

23

<<   24   25   26   27   28   29   30   >>

More intriguing information