Statistical Inference on Changes in Income Inequality in Australia

This paper studies the changes in income inequality of individuals in Australia between 1986 and 1999. Individuals are divided into various subgroups along several dimensions, such as region of residence, age, employment status etc. The changes in inequality over time, between and within the various subgroups is studied, and the bootstrap method is used to establish whether these changes are statistically significant.Income inequality; Gini coefficient; Theil inequality measure; bootstrap.

VARMA versus VAR for Macroeconomic Forecasting

In this paper, we argue that there is no compelling reason for restricting the class of multivariate models considered for macroeconomic forecasting to VARs given the recent advances in VARMA modelling methodology and improvements in computing power. To support this claim, we use real macroeconomic data and show that VARMA models forecast macroeconomic variables more accurately than VAR models.Forecasting, Identification, Multivariate time series, Scalar components, VARMA models.

Global Temperature Trends

Are global temperatures on a warming trend? It is difficult to be certain about trends when there is so much variation in the data and very high correlation from year to year. We investigate the question using statistical time series methods. Our analysis shows that the upward movement over the last 130-160 years is persistent and not explained by the high correlation, so it is best described as a trend. The warming trend becomes steeper after the mid-1970s, but there is no significant evidence for a break in trend in the late 1990s. Viewed from the perspective of 30 or 50 years ago, the temperatures recorded in most of the last decade lie above the confidence band of forecasts produced by a model that does not allow for a warming trend.Land and ocean temperatures; deterministic and stochastic trends; persistence; piecewise linear trends

The Effect of Household Characteristics on Living Standards in South Africa 1993 - 98: A Quantile Regression Analysis with Sample Attrition

This paper examines whether the dismantling of apartheid has resulted in the improvement in the standard of living for the vast majority of South Africans. The study is based on a panel data set from the Kwazulu-Natal province. Despite the best efforts of the interview team, the attrition rate in this panel is around 16%. We find that household income and size in 1993, several community characteristics and survey quality in 1993 significantly affect the probability of attrition. We use weighted quantile regressions to examine the distribution of standards of living, which corrects for the potential bias arising from non-random sample attrition. Our results show that there has been a significant increase in the spread of the distribution of household expenditure of the Non-White households residing in Kwazulu-Natal province. We argue that the stretch to the right of the upper tail of distribution can be attributed to significant increase in returns to primary and high school education, while movement to the left of the lower quantiles can be associated with the increase in the proportion of female headed households and household size.

A Complete VARMA Modelling Methodology Based on Scalar Components

This paper proposes an extension to scalar component methodology for the identification and estimation of VARMA models. The complete methodology determines the exact positions of all free parameters in any VARMA model with a predetermined embedded scalar component structure. This leads to an exactly identified system of equations that is estimated using full information maximum likelihood.Identification, Multivariate time series, Scalar components, VARMA models.

Forecasting Australian GDP Growth Using Coefficients Constrained by A Term Structure Model

Yield spread between long and short bonds has been used to forecast economic activity for a long time and has yielded some positive results, particularly for the U.S. data. Recently it has been shown that the forecast can be improved by incorporating the economic activity variable into a term structure model with observable factors. The idea is to constrain the parameters of the system by the term structure model and see whether the constrained model produces better forecasts for the economic activity. This has been done for the U.S. We test this model on Australian GDP growth. We find the forecast results using constrained parameters are quite poor compared to those for the unconstrained model. The reason is that in the traditional affine yield model, researchers normally assume a mean reverting process for factors such as short rate. When this is not supported by the data, the forecast results could be quite poor. To overcome this problem, one might want to twist the affine factor model so that it can accommodate non-mean reverting processes for factors such as the sTerm Structure, VAR and GDP Growth