The Global Dimension to Fiscal Sustainability

This paper examines the issue of fiscal sustainability in emerging market countries and industrial countries. We highlight the importance of the time series properties of the primary surplus and debt, and find evidence of a positive long run relationship. Consequently we emphasise, that especially for emerging markets, it is important to recognise the implications of global capital market shocks for fiscal sustainability, a relationship which has hitherto been ignored in the empirical literature. Using a factor model we demonstrate that the relationship between deficit and debt is conditional upon a global factor and we suggest that this global factor is related to world-wide liquidity. We also demonstrate that this acts as a constraint on emerging market economies’ fiscal policy.

Income stratification and the measurement of interdistributional inequality between multiple groups

This paper proposes a new class of stratification indices that measure interdistributional inequality between multiple groups. The class is based on a conceptualisation of stratification as a process that results in a hierarchical ordering of groups and therefore seeks to capture not only the extent to which groups form well-defined strata in the income distribution but also the scale of the resultant differences in income standards between them, where these two factors play the same role as identification and alienation respectively in the measurement of polarisation. The properties of the class as a whole are investigated as well as those of selected members of it: zeroth and first power indices may be interpreted as measuring the overall incidence and depth of stratification respectively, while higher power indices members are directly sensitive to the severity of stratification between groups. An illustrative application provides an empirical analysis of global income stratification by regions in 1993.

Time Varying Dimension Models

Time varying parameter (TVP) models have enjoyed an increasing popularity in empirical macroeconomics. However, TVP models are parameter-rich and risk over-fitting unless the dimension of the model is small. Motivated by this worry, this paper proposes several Time Varying dimension (TVD) models where the dimension of the model can change over time, allowing for the model to automatically choose a more parsimonious TVP representation, or to switch between different parsimonious representations. Our TVD models all fall in the category of dynamic mixture models. We discuss the properties of these models and present methods for Bayesian inference. An application involving US inflation forecasting illustrates and compares the different TVD models. We find our TVD approaches exhibit better forecasting performance than several standard benchmarks and shrink towards parsimonious specifications.