An Introduction to Bayesian Inference in Econometrics by Arnold Zellner

By Arnold Zellner

This can be a classical reprint version of the unique 1971 version of An creation to Bayesian Inference in Economics. This ancient quantity is an early advent to Bayesian inference and method which nonetheless has lasting worth for cutting-edge statistician and scholar. The assurance levels from the elemental recommendations and operations of Bayesian inference to research of purposes in particular econometric difficulties and the checking out of hypotheses and versions.

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However, when we consider the autocorrelation of the squared returns and compare its values with those of the theoretical autocorrelation functions of the Gaussian SV and GARCH models we observe that both models follow the sample autocorrelations reasonably well but that their performance deteriorates once leptokurtic distributions are imposed. Taking all the above criteria into account this then suggests that the SV model best captures the salient features of the stock index return volatility.

Inclusion of both volatility measures however results not only in a highly significant negative relation between the unexpected components but also turns the sign for the ex ante relationship which becomes weakly negative. The negative relation between the unexpected components therefore dominates the weaker, presumably positive, relation between the expected components. We further observe that the largest negative values for d are found for the shorter samples where they are also closest to being statistically significant.

1998). 27 28 Empirical Studies on Volatility in International Stock Markets The development of an SV model that includes volatility as one of the determinants of the mean makes our model suitable for empirical applications in which returns are partially dependent on volatility, such as studies that investigate the relationship between the mean and variance of stock returns. The SVM model can therefore be viewed as the SV counterpart of the ARCH-M model of Engle, Lilien and Robins (1987). The main difference between the two classes of models is that the ARCHM model intends to estimate the relationship between expected returns and expected volatility, whereas the aim of the SVM model is the simultaneous estimation of the ex ante relation between returns and volatility and the volatility feedback effect.

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