In this paper, a structural time series model is estimated to analyse the effect of quantitative easing (QE) on stock prices for the US, UK and Japan. The model is estimated by maximum likelihood in a time-varying parametric framework, using the DJIA, S&P500, NASDAQ, FTSE100 and the NIKKEI225 as the dependent variable and the balance sheet of the respective Central Bank as an explanatory variable, along with the unobserved components that account for the behaviour of other explanatory variables that are not explicitly specified in the model. The results show that QE had a significant but not exclusive effect on the DJIA, S&P500 and the NASDAQ, suggesting that these stock prices are also affected by other missing variables and cyclical movements. However, for the UK and Japan, no effect of QE on the FTSE100 and the NIKKEI225 is found, suggesting that variables other than QE are important for the rise in these stock prices. One plausible explanation for this result is that perhaps QE becomes effective only after a certain threshold level is met.
In this article, an optimal macroeconomic uncertainty index is constructed for the Australian economy. This index is derived from a small structural macroeconomic model. The structural model is first estimated using GMM to extract the parameter estimates, which are then used to initialise maximum likelihood techniques in order to obtain the optimal coefficient values for the relevant variables. The relevant variables are then weighted by the obtained optimal coefficients and, finally, are aggregated to produce the optimal macroeconomic uncertainty index for Australian economy. The empirical results show that the uncertainty index constructed is a good indicator of the optimal economic conditions in Australia, providing a useful tool to assist the Reserve Bank of Australia in its decision-making process.
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