Divisia money is a monetary aggregate that gives each component an assigned weight. We use an evolutionary neural network to calculate new Divisia weights for each component utilising the Bank of England monetary data for the U.K. We propose a new money aggregate using our newly derived weights to carry out quantitative inflation prediction. The results show that this new money aggregate has be...

Forecasting Economic Aggregates by Disaggregates* We explore whether forecasting an aggregate variable using information on its disaggregate components can improve the prediction mean squared error over first forecasting the disaggregates and then aggregating those forecasts, or, alternatively, over using only lagged aggregate information in forecasting the aggregate. We show theoretically that...

The paper constructs various core inflation measures. These include various trimmed means using disaggregated data and a structural VAR estimate of core inflation for Ireland. The ability of these core inflation measures to forecast future headline inflation is compared using a regression model. An ARIMA model fitted to the headline inflation rate is used as the benchmark forecast. The forecast...

Despite the fact that the aggregation weights of many macroeconomic aggregates are time-varying, much of the literature on forecasting aggregates considers the situation of fixed, time-invariant aggregation weights. In this study a framework for contemporaneous aggregation with stochastic weights is developed and different predictors for an aggregate are compared theoretically as well as with s...

This paper investigates whether using natural logarithms (logs) of price indices for forecasting inflation rates is preferable to employing the original series. Univariate forecasts for annual inflation rates for a number of European countries and the USA based on monthly seasonal consumer price indices are considered. Stochastic seasonality and deterministic seasonality models are used. In man...

This study compares the forecasting performance of various Autoregressive integrated moving average (ARIMA) models by using time series data. Primarily, The Box-Jenkins approach is considered here for forecasting. For empirical analysis, we used CPI as a proxy for inflation and employed quarterly data from 1970 to 2006 for Pakistan. The study classified two important models for forecasting out ...

In this paper we apply an Adaptive Network-Based Fuzzy Inference System (ANFIS) with one input, the dependent variable with one lag, for the forecasting of four macroeconomic variables of US economy, the Gross Domestic Product, the inflation rate, six monthly treasury bills interest rates and unemployment rate. We compare the forecasting performance of ANFIS with those of the widely used linear...