Turnovsky and Chattopadhyay study three types of volatility for small-open economy: fiscal – government spending, monetary – money supply and trade. All have significant effect. While they do not have much importance in more stable economies, where only money supply volatility is statistically significant. This view is similar to Malik and Temple (2009). They explored the geography of output volatility and stated that it is very important for developing countries. So the external shock is the main source for production volatility (Friedman, 1986). Moreover Malik and Temple add that trade is responsible for fluctuation as well. This statement agrees with Turnovsky and Chattopadhyay who say that export intensity can cause volatility. “Remoteness is associated with a lack of export diversification, leading to high volatility in the terms of trade and output” (Malik, Temple, 2009).
Macri and Sinha get the same relationship by modelling the variance for the ARCH methodology instead of the mean of a variable which typically suppose to be primary concern. They tested for autocorrelation with time-series data. While classical linear regression model states that the variance is constant, in the case of autocorrelation it is violated because of inconsistent fluctuations of the forecast errors. Finally, they fail to reject null hypothesis and therefore concluded that the errors are normally distributed. The mean equation states that there is a negative relationship between the conditional standard deviation and the growth rate of industrial production. Meanwhile for GDP they did not find significant evidence for ARCH effects as test for stationary suggested.
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