Autonomy-enhancing paternalism

We present a form of soft paternalism called "autonomy-enhancing paternalism" that seeks to in-crease individual well-being by facilitating the individual ability to make critically reflected, autonomous decisions. The focus of autonomy-enhancing paternalism is on helping individuals to become better decision-makers, rather than on helping them by making better decisions for them. Autonomy-enhancing paternalism acknowledges that behavioral interventions can change the strength of decision-making anomalies over time, and favors those interventions that improve, rather than reduce, individuals ability to make good and unbiased decisions. By this it prevents manipulation of the individual by the soft paternalist, accounts for the heterogeneity of individuals, and counteracts slippery slope arguments by decreasing the probability of future paternalistic interventions. Moreover, autonomy-enhancing paternalism can be defended based on both liberal values and welfare considerations.

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.

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.