Large Time-Varying Parameter VARs

In this paper we develop methods for estimation and forecasting in large timevarying parameter vector autoregressive models (TVP-VARs). To overcome computational constraints with likelihood-based estimation of large systems, we rely on Kalman filter estimation with forgetting factors. We also draw on ideas from the dynamic model averaging literature and extend the TVP-VAR so that its dimension can change over time. A final extension lies in the development of a new method for estimating, in a time-varying manner, the parameter(s) of the shrinkage priors commonly-used with large VARs. These extensions are operationalized through the use of forgetting factor methods and are, thus, computationally simple. An empirical application involving forecasting inflation, real output, and interest rates demonstrates the feasibility and usefulness of our approach.

How Heavy Is A Job?: A Critical Survey of Job Evaluation as a Payment Device.

With salaries subjected to scrutiny more than ever, it is increasingly important that the process by which they are determined be understood and justifiable. Both public and private organisations now routinely rely on so-called “job evaluation” as a means of constructing an appropriate pay-scale and as such it is ever more necessary that we appreciate how this system works and that we recognise its limits. Only with such an understanding of the way in which salaries are set can we hope to have a meaningful discussion of their economic function. This paper aims to expound the details of job evaluation both in theory and in practice, and critically assess its shortcomings. In Section 1 below we describe the job evaluation system and in Section 2 we briefly outline the history and the usage of the system in both the private and the public sector. In Section 3 we theoretically analyse the often unstated but nonetheless implicit assumptions made by practitioners of the art of job evaluation. Section 4 applies the analysis of Section 3 to review a particular and important case study, namely The Senior Salaries Review of the Welsh Assembly 2004. Section 5 concludes.

Model Switching and Model Averaging in Time- Varying Parameter Regression Models

This paper investigates the usefulness of switching Gaussian state space models as a tool for implementing dynamic model selecting (DMS) or averaging (DMA) in time-varying parameter regression models. DMS methods allow for model switching, where a different model can be chosen at each point in time. Thus, they allow for the explanatory variables in the time-varying parameter regression model to change over time. DMA will carry out model averaging in a time-varying manner. We compare our exact approach to DMA/DMS to a popular existing procedure which relies on the use of forgetting factor approximations. In an application, we use DMS to select different predictors in an in ation forecasting application. We also compare different ways of implementing DMA/DMS and investigate whether they lead to similar results.

Hierarchical Shrinkage in Time-Varying Parameter Models

In this paper, we forecast EU-area inflation with many predictors using time-varying parameter models. The facts that time-varying parameter models are parameter-rich and the time span of our data is relatively short motivate a desire for shrinkage. In constant coefficient regression models, the Bayesian Lasso is gaining increasing popularity as an effective tool for achieving such shrinkage. In this paper, we develop econometric methods for using the Bayesian Lasso with time-varying parameter models. Our approach allows for the coefficient on each predictor to be: i) time varying, ii) constant over time or iii) shrunk to zero. The econometric methodology decides automatically which category each coefficient belongs in. Our empirical results indicate the benefits of such an approach.