financial frictions fit the data better in terms of implied volatilities of inflation and interest
rate, getting closer to the data in this dimension. Note that our ‘best’ 2-sector model
clearly outperforms the other two models in capturing the volatilities of output, inflation
and interest rate and does extremely well at matching the inflation volatility in the data.
Table 5 also reports the cross-correlations of the four observable variables vis-a-vis
output. All models perform successfully in generating the positive contemporaneous cor-
relations observed in the data. It is worth noting that our ‘preferred’ model, 2-sector NK
model, does very well at capturing the contemporaneous cross-correlation of the interest
rate and output, suggesting that the financial accelerator and informal sector help fitting
the Indian data in this dimension. The 2-sector NK model appears to match well the
autocorrelations (order=1) of interest rate and investment. Using this model, output is
more autocorrelated while inflation seems to be less autocorrelated then those in the data
at order 1. Nevertheless, the 2-sector NK model, in general, is able to capture the main
features of the data in most dimensions and strengthens the argument that the presence of
financial friction mechanisms and informality is supported by the data.
5.3 Unconditional Autocorrelations
To further illustrate how the estimated models capture the data statistics, we plot the
unconditional autocorrelations of the actual data and those of the endogenous variables
generated by the model variants in Figure 9. In general, all models match reasonably
well the autocorrelations of output, interest rate and investment shown in the data within
a shorter period horizon. The data report that these three variables are positively and
very significantly autocorrelated over short horizons. At lags of one-two quarters, all the
estimated models are able to generate the observed autocorrelations of interest rate and
investment as noted above. Output is more autocorrelated in all models than in the data,
but the 2-sector model with informality gets closer to the data towards the end of sample
period. When it comes to matching inflation, all models exhibit the shortcoming of the
inability of predicting the dynamics in the data.
Of particular interest is that, when assuming informality and/or the presence of financial
frictions, the implied autocorrelograms produced by the 2-sector model and NK model
with FA fit well the observed autocorrelation of interest rate, while the simplest NK model
generates less sluggishness and is less able to match the autocorrelation observed in the
data from the second lag onwards. The 2-sector model outperforms in terms of getting
closer to the autocorrelation observed in the Indian GDP and investment. Overall, the
results in this exercise generally show again that the estimated DSGE models are able to
capture the some important features of Indian data and the presence of financial frictions
and informality helps improve the model fit to data.
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