Short-run money growth volatility: evidence of misbehaving money demand?

Demand for money

Dynamic forecasting and the demand for money

Demand for money

Deficits and inflation

Inflation (Finance)

Seasonal adjustment of the money supply

Seasonal variations (Economics) ; Business cycles

Comparing futures and survey forecasts of near-term Treasury bill rates

Treasury bills ; Futures ; Forecasting

Generating Interpretable Fuzzy Controllers using Particle Swarm Optimization and Genetic Programming

Autonomously training interpretable control strategies, called policies, using pre-existing plant trajectory data is of great interest in industrial applications. Fuzzy controllers have been used in industry for decades as interpretable and efficient system controllers. In this study, we introduce a fuzzy genetic programming (GP) approach called fuzzy GP reinforcement learning (FGPRL) that can select the relevant state features, determine the size of the required fuzzy rule set, and automatically adjust all the controller parameters simultaneously. Each GP individual's fitness is computed using model-based batch reinforcement learning (RL), which first trains a model using available system samples and subsequently performs Monte Carlo rollouts to predict each policy candidate's performance. We compare FGPRL to an extended version of a related method called fuzzy particle swarm reinforcement learning (FPSRL), which uses swarm intelligence to tune the fuzzy policy parameters. Experiments using an industrial benchmark show that FGPRL is able to autonomously learn interpretable fuzzy policies with high control performance.Comment: Accepted at Genetic and Evolutionary Computation Conference 2018 (GECCO '18

Constructing an alternative measure of changes in reserve requirement ratios

Bank reserves

The dynamics and estimation of short-run money demand

Demand for money

The wayward money supply: a post-mortem of 1982

Money supply