Bayesian Inference in the Time Varying Cointegration Model

There are both theoretical and empirical reasons for believing that the parameters of macroeconomic models may vary over time. However, work with time-varying parameter models has largely involved Vector autoregressions (VARs), ignoring cointegration. This is despite the fact that cointegration plays an important role in informing macroeconomists on a range of issues. In this paper we develop time varying parameter models which permit cointegration. Time-varying parameter VARs (TVP-VARs) typically use state space representations to model the evolution of parameters. In this paper, we show that it is not sensible to use straightforward extensions of TVP-VARs when allowing for cointegration. Instead we develop a specification which allows for the cointegrating space to evolve over time in a manner comparable to the random walk variation used with TVP-VARs. The properties of our approach are investigated before developing a method of posterior simulation. We use our methods in an empirical investigation involving a permanent/transitory variance decomposition for inflation.

Type II Errors in IO Multipliers

This paper compares methods for calculating Input-Output (IO) Type II multipliers. These are formulations of the standard Leontief IO model which endogenise elements of household consumption. An analytical comparison of the two basic IO Type II multiplier methods with the Social Accounting Matrix (SAM) multiplier approach identifies the treatment of non-wage income generated in production as a central problem. The multiplier values for each of the IO and SAM methods are calculated using Scottish data for 2009. These results can be used to choose which Type II IO multiplier to adopt where SAM multiplier values are unavailable.