Forecasting GDP Growth Using Artificial Neural Networks Staff Working Paper 1999-3 Greg Tkacz, Sarah Hu Financial and monetary variables have long been known to contain useful leading information regarding economic activity. In this paper, the authors wish to determine whether the forecasting performance of such variables can be improved using neural network models. The main findings are that, at the 1-quarter forecasting horizon, neural networks yield no significant forecast improvements. […] Content Type(s): Staff research, Staff working papers Research Topic(s): Econometric and statistical methods, Monetary and financial indicators JEL Code(s): C, C4, C45, E, E3, E37, E4, E44
A Modified P*-Model of Inflation Based on M1 Staff Working Paper 1996-15 Joseph Atta-Mensah This paper examines the performance of M1 in an indicator-model of inflation over time horizons as long as 16 quarters into the future. Content Type(s): Staff research, Staff working papers Research Topic(s): Economic models JEL Code(s): E, E3, E37
A Distant-Early-Warning Model of Inflation Based on M1 Disequilibria Staff Working Paper 1996-5 Joseph Atta-Mensah, Walter Engert, Scott Hendry, Jamie Armour A vector error-correction model (VECM) that forecasts inflation between the current quarter and eight quarters ahead is found to provide significant leading information about inflation. The model focusses on the effects of deviations of M1 from its long-run demand but also includes, among other things, the influence of the exchange rate, a simple measure of the output gap and past prices. Content Type(s): Staff research, Staff working papers Research Topic(s): Economic models, Monetary aggregates, Monetary policy transmission JEL Code(s): E, E3, E37, E5, E52