In this paper we present an autoregressive model with neural networks modeling and standard error backpropagation algorithm training optimization in order to predict the gross domestic product (GDP) growth rate of four countries. Specifically we propose a kind of weighted regression, which can be used for econometric purposes, where the initial inputs are multiplied by the neural networks final...

First reported monthly and quarterly time series data on nine macroeconomic variables from 1960-1993 are given. Features of this so called "unrevised" or " ̄rst reported data" are discussed, and the data is compared with standard "fully revised" data using Granger causality tests. We reiterate that for the purposes of real-time forecating as well as comparing professional forecasts with traditio...

In this paper we present an autoregressive model with neural networks modeling and standard error backpropagation algorithm training optimization in order to predict the gross domestic product (GDP) growth rate of four countries. Specifically we propose a kind of weighted regression, which can be used for econometric purposes, where the initial inputs are multiplied by the neural networks final...

In many applications it is necessary to use a simple and therefore highly misspecified econometric model as the basis for decision-making. We propose an approach to developing a possibly misspecified econometric model that will be used as the beliefs of an expected utility maximiser. An objective function that measures the value of predictive distributions in decision-making is introduced and u...

In the discipline of economics, the vast array of economic theories which are available to economists often contain nonlinear elements (e.g. first order conditions, functional forms, etc.). In the past, however, many of the testable implications of economic theories, for example were formulated and derived with one of the main objectives being the specification of linear econometric models. Thi...

It has been observed that older high traffic motorways experience lower traffic growth than newer ones (ceteris paribus). This phenomenon is known as traffic maturity; however, it is not captured through traditional time-series long-term forecasts, due to constant elasticity to GDP these models assume. In this paper we argue that traffic maturity results from decreasing marginal utility of tran...

This paper draws on the metaphor of a spectrum of models ranging from the most theorydriven to the most evidence-driven. The issue of concern is the practice and criteria that will be appropriate to validation of different models. In order to address this concern, two modelling approaches are investigated in some detailed — one from each end of our metaphorical spectrum. Windrum et al. (2007) (...

Forecasting is required in many situations: deciding whether to build another power generation plant in the next five years requires forecasts of future demand; scheduling staff in a call centre next week requires forecasts of call volume; stocking an inventory requires forecasts of stock requirements. Forecasts can be required several years in advance (for the case of capital investments), or ...