This paper utilizes the nonparametric functional autoregressive approach (FAR) to model the time-varying distribution of UK monthly inflation rates using disaggregated cross-sectional data. Our approach is free of any assumptions on the class or structure of the density functions themselves, or the number of dimensions in which the densities may vary. The “pseudo real time” in-sample forecastin...

We evaluate various economic models’ relative performance in forecasting future US output growth and inflation on a monthly basis. Our approach takes into account the possibility that the models’ relative performance can be varying over time. We show that the models’ relative performance has, in fact, changed dramatically over time, both for revised and realtime data, and investigate possible f...

We evaluate various economic models’ relative performance in forecasting future US output growth and inflation on a monthly basis. Our approach takes into account the possibility that the models’ relative performance can be varying over time. We show that the models’ relative performance has, in fact, changed dramatically over time, both for revised and realtime data, and investigate possible f...

Previous work has used neural networks to predict the rate of inflation in Taiwan using four measures of ‘money’ (simple sum and three Divisia measures). In this work a new approach is developed that uses an evolutionary strategy as a predictive tool. This approach is simple to implement yet produces results that compare favourably with the neural network predictions. Our preferred inflation fo...

The U.S. economy in the 1980s saw a decline in the trend growth rate of monetary velocity, the ratio of nominal GNP to the money supply. This unexpected development was reflected in the systematic overprediction of inflation and nominal GNP growth by econometric models and economic forecasters. Robert Lucas (1976) showed that econometric models would err when simulating policy alternatives or w...

This work applies state-of-the-art artificial intelligence forecasting methods to provide new evidence of the comparative performance of statistically weighted Divisia indices vis a vis their simple sum counterparts in a simple inflation forecasting experiment. We develop a new approach that uses coevolution (using neural networks and evolutionary strategies) as a predictive tool. This approach...

We propose behavioral learning equilibria as a plausible explanation of coordination of individual expectations and aggregate phenomena such as excess volatility in stock prices and high persistence in inflation. Boundedly rational agents use a simple univariate linear forecasting rule and correctly forecast the unconditional sample mean and first-order sample autocorrelation. In the long run, ...