Rakesh K. Bissoondeeal

A comparison of linear forecasting models and neural networks: an application to Euro inflation and Euro Divisia

Linear models reach their limitations in applications with nonlinearities in the data. In this paper new empirical evidence is provided on the relative Euro inflation forecasting performance of linear and non-linear models. The well established and widely used univariate ARIMA and multivariate VAR models are used as linear forecasting models whereas neural networks (NN) are used as non-linear forecasting models. It is endeavoured to keep the level of subjectivity in the NN building process to a minimum in an attempt to exploit the full potentials of the NN. It is also investigated whether the historically poor performance of the theoretically superior measure of the monetary services flow, Divisia, relative to the traditional Simple Sum measure could be attributed to a certain extent to the evaluation of these indices within a linear framework. Results obtained suggest that non-linear models provide better within-sample and out-of-sample forecasts and linear models are simply a subset of them. The Divisia index also outperforms the Simple Sum index when evaluated in a non-linear framework.

Forecasting exchange rates with linear and nonlinear models

In this paper, the exchange rate forecasting performance of neural network models are evaluated against the random walk, autoregressive moving average and generalised autoregressive conditional heteroskedasticity models. There are no guidelines available that can be used to choose the parameters of neural network models and therefore, the parameters are chosen according to what the researcher considers to be the best. Such an approach, however, implies that the risk of making bad decisions is extremely high, which could explain why in many studies, neural network models do not consistently perform better than their time series counterparts. In this paper, through extensive experimentation, the level of subjectivity in building neural network models is considerably reduced and therefore giving them a better chance of performing well. The results show that in general, neural network models perform better than the traditionally used time series models in forecasting exchange rates.

A composite leading indicator of the inflation cycle for the Euro area

We evaluate the performance of composite leading indicators of turning points of inflation in the Euro area, constructed by combining the techniques of Fourier analysis and Kalman filters with the National Bureau of Economic Research methodology. In addition, the study compares the empirical performance of Euro Simple Sum and Divisia monetary aggregates and provides a tentative answer to the issue of whether or not the UK should join the Euro area. Our findings suggest that, first, the cyclical pattern of the different composite leading indicators very closely reflect that of the inflation cycle for the Euro area; second, the empirical performance of the Euro Divisia is better than its Simple Sum counterpart and third, the UK is better out of the Euro area.

Monetary models of exchange rates and sweep programs

Numerous studies find that monetary models of exchange rates cannot beat a random walk model. Such a finding, however, is not surprising given that such models are built upon money demand functions and traditional money demand functions appear to have broken down in many developed countries. In this article, we investigate whether using a more stable underlying money demand function results in improvements in forecasts of monetary models of exchange rates. More specifically, we use a sweep-adjusted measure of US monetary aggregate M1 which has been shown to have a more stable money demand function than the official M1 measure. The results suggest that the monetary models of exchange rates contain information about future movements of exchange rates, but the success of such models depends on the stability of money demand functions and the specifications of the models.

Forecasting the UK/US Exchange Rate with Divisia Monetary Models and Neural Networks

This paper compares the UK/US exchange rate forecasting performance of linear and nonlinear models based on monetary fundamentals, to a random walk (RW) model. Structural breaks are identified and taken into account. The exchange rate forecasting framework is also used for assessing the relative merits of the official Simple Sum and the weighted Divisia measures of money. Overall, there are four main findings. First, the majority of the models with fundamentals are able to beat the RW model in forecasting the UK/US exchange rate. Second, the most accurate forecasts of the UK/US exchange rate are obtained with a nonlinear model. Third, taking into account structural breaks reveals that the Divisia aggregate performs better than its Simple Sum counterpart. Finally, Divisia-based models provide more accurate forecasts than Simple Sum-based models provided they are constructed within a nonlinear framework.

Household-Sector Money Demand for the UK

We test for the existence of a long-run money demand relationship for the UK involving household-sector Divisia and simple sum monetary indexes for the period from 1977 to 2008. We construct our Divisia index using non-break-adjusted levels and break-adjusted flows following the Bank of England. We test for cointegration between the real Divisia and simple sum indexes, their corresponding opportunity cost measures, real income and real share prices. Our results support the existence of a long-run money demand relationship for both the Divisia and simple sum indexes.

Post-Bretton Woods evidence on PPP under different exchange rate regimes

This article investigates the behaviour of exchange rates across different regimes for a post-Bretton Woods period. The exchange rate regime classification is based on the classification of Frankel et al. (2004) who condensed the 10 categories of exchange rate regimes reported by the International Monetary Fund (IMF) into three categories. Panel unit-root tests and panel cointegration are used to examine the Purchasing Power Parity (PPP) hypothesis. The latter test is used to check for both the weak and strong forms of PPP. The panel unit-root tests show no evidence of PPP and suggest there is no difference in the behaviour of exchange rates across different regimes. However, failure to detect PPP across any of the regimes could be due to structural breaks. This assumption is reinforced by the results of cointegration tests, which suggest that there exists at least a weak form of PPP for the different regimes. The evidence for strong PPP decreases as the exchange rate regime moves away from a flexible exchange rate regime.

Breaks in the UK Household Sector Money Demand Function

We use non-parametric procedures to identify breaks in the underlying series of UK household sector money demand functions. Money demand functions are estimated using cointegration techniques and by employing both the Simple Sum and Divisia measures of money. P-star models are also estimated for out-of-sample inflation forecasting. Our findings suggest that the presence of breaks affects both the estimation of cointegrated money demand functions and the inflation forecasts. P-star forecast models based on Divisia measures appear more accurate at longer horizons and the majority of models with fundamentals perform better than a random walk model.

Trade balance and real exchange rate: new evidence from Mauritius-UK trade

Two main questions are addressed here: is there a long-run relationship between trade balance and real exchange rate for the bilateral trade between Mauritius and UK? Does a J-curve exist for this bilateral trade? Our findings suggest that the real exchange rate is cointegrated with the trade balance and we find evidence of a J-curve effect. We also find bidirectional causality between the trade balance and the real exchange rate in the long-run. The real exchange rate also causes the trade balance in the short-run. In an out-of-sample forecasting experiment, we also find that real exchange rate contains useful information that can explain future movements in the trade balance.