Monetary Policy Delegation and Equilibrium Coordination

This paper revisits the argument that the stabilisation bias that arises under discretionary monetary policy can be reduced if policy is delegated to a policymaker with redesigned objectives. We study four delegation schemes: price level targeting, interest rate smoothing, speed limits and straight conservatism. These can all increase social welfare in models with a unique discretionary equilibrium. We investigate how these schemes perform in a model with capital accumulation where uniqueness does not necessarily apply. We discuss how multiplicity arises and demonstrate that no delegation scheme is able to eliminate all potential bad equilibria. Price level targeting has two interesting features. It can create a new equilibrium that is welfare dominated, but it can also alter equilibrium stability properties and make coordination on the best equilibrium more likely.

Stochastic Search Variable Selection in Vector Error Correction Models with an Application to a Model of the UK Macroeconomy

This paper develops methods for Stochastic Search Variable Selection (currently popular with regression and Vector Autoregressive models) for Vector Error Correction models where there are many possible restrictions on the cointegration space. We show how this allows the researcher to begin with a single unrestricted model and either do model selection or model averaging in an automatic and computationally efficient manner. We apply our methods to a large UK macroeconomic model.

What Belongs Where? Variable Selection for Zero-Inflated Count Models with an Application to the Demand for Health Care

This paper develops stochastic search variable selection (SSVS) for zero-inflated count models which are commonly used in health economics. This allows for either model averaging or model selection in situations with many potential regressors. The proposed techniques are applied to a data set from Germany considering the demand for health care. A package for the free statistical software environment R is provided.

Environmental Degradation and the Limits to LOV

We show how consumers’ environmental concerns may limit ‘love of variety’ (LOV) and be reflected in consumers decisions. We investigate how the impact of environmental degradation on LOV influences demand and optimal product variety, and how a pollution tax on firms might be used to improve upon the market outcome and increase welfare.

Inverted Haavelmo Effects in a General Equilibrium Analysis of the Impact of Implementing the Scottish Variable Rate of Income Tax.

The Scottish Parliament has the authority to make a balanced-budget expansion or contraction in public expenditure, funded by corresponding local changes in the basic rate of income tax of up to 3p in the pound. This fiscal adjustment is known as the Scottish Variable Rate of income tax, though it has never, as yet, been used. In this paper we attempt to identify the impact on aggregate economic activity in Scotland of implementing these devolved fiscal powers. This is achieved through theoretical analysis and simulation using a Computable General Equilibrium (CGE) model for Scotland. This analysis generalises the conventional Keynesian model so that negative balanced-budget multipliers values are possible, reflecting a regional “inverted Haavelmo effect”. Key parameters determining the aggregate economic impact are the extent to which the Scottish Government create local amenities valuable to the Scottish population and the extent to which this is incorporated into local wage bargaining.

Bayesian Model Averaging in the Instrumental Variable Regression Model

This paper considers the instrumental variable regression model when there is uncertainty about the set of instruments, exogeneity restrictions, the validity of identifying restrictions and the set of exogenous regressors. This uncertainty can result in a huge number of models. To avoid statistical problems associated with standard model selection procedures, we develop a reversible jump Markov chain Monte Carlo algorithm that allows us to do Bayesian model averaging. The algorithm is very exible and can be easily adapted to analyze any of the di¤erent priors that have been proposed in the Bayesian instrumental variables literature. We show how to calculate the probability of any relevant restriction (e.g. the posterior probability that over-identifying restrictions hold) and discuss diagnostic checking using the posterior distribution of discrepancy vectors. We illustrate our methods in a returns-to-schooling application.