Purpose The aim of this paper is to suggest a new approach to the problem of sales forecasting for improving forecast accuracy. The proposed method is capable of combining, by means of appropriate weights, both the responses supplied by the best-performing conventional algorithms, which base...

Several approaches for subset recovery and improved forecasting accuracy have been proposed and studied. One way is to apply a regularization strategy and solve the model selection task as a continuous optimization problem. One of the most popular approaches in this research field is given by...

A two-regime self-exciting threshold autoregressive process is estimated for quarterly aggregate GDP of the fifteen countries that compose the European Union, and the forecasts from this nonlinear model are compared, by means of a Monte Carlo simulation, with those from a simple autoregressive...

This paper examines the corporation tax forecasting techniques used by the Institute for Fiscal Studies. For current year forecasts a judgemental forecast is found to have performed better than relying solely on a simple model or information on the receipts available so far in the current...

We analyze the performance of Bayesian model averaged exchange rate forecasts for euro/US dollar, euro/Japanese yen, euro/Swiss franc and euro/British pound rates using weights based on the out-of-sample predictive likelihood. The paper also presents a simple stratified sampling procedure in the...

We propose a new time series model aimed at forecasting crude oil prices. The proposed specification is an unobserved components model with an asymmetric cyclical component. The asymmetric cycle is defined as a sine-cosine wave where the frequency of the cycle depends on past oil price...

We suggest a theoretical basis for the comparative evaluation of forecasts. Instead of the general assumption that the data is generated from a stochastic model, we classify three stages of prediction experiments: pure non-stochastic prediction of given data, stochastic prediction of given data,...

Many macroeconometric models depict situations where the shares of the major demand aggregates in output are stable over time. The joint dynamic behavior of the considered demand aggregate and output may thus be approximated by a cointegrated vector autoregression. However, the shares of many...

This paper uses Monte Carlo techniques to assess the loss in terms of forecast accuracy which is incurred when the true DGP exhibits parameter instability which is either overlooked or incorrectly modelled. We find that the loss is considerable when a FCM is estimated instead of the true TVCM,...