Forecasting Economic Time Series in the Presence of Variance Instability and Outliers

Alexandros E Milionis,Nikolaos G Galanopoulos

doi:10.4236/tel.2019.98182

Abstract

This work examines the impact of data transformation (for variance stabilization) and outlier adjustment (“linearization”) on the quality of univariate time series forecasts, considering each one separately, as well as in combination. Twenty of the most important time series of the Greek economy were used for this purpose. Empirical findings show a significant improvement in forecasts’ confidence intervals, but no substantial improvement in point forecasts. Furthermore, the combined transformation-linearization procedure improves substantially the non-normality problem encountered in many macroeconomic time series.

Highlights

A univariate AutoRegressive Integrated Moving Average ( ARIMA) model is a concise quantitative summary of the internal dynamics of a time series in a linear framework, as such, is useful for several reasons, amongst others for forecasting and model-based time series decomposition in unobserved components
Typical statistics to be used for the assessment of the quality of point forecasts are the following: 1) the Mean Absolute Percentage Error (MAPE) statistic given by:
This work dealt with the effect of data transformation for variance stabilization and linearization for outlier adjustment on the quality of univariate time series forecasts, following a practical approach

Summary

Introduction

A univariate AutoRegressive Integrated Moving Average ( ARIMA) model is a concise quantitative summary of the internal dynamics of a time series in a linear framework, as such, is useful for several reasons, amongst others for forecasting and model-based time series decomposition in unobserved components. Economic time series from the real world are not usually “ready” to be used for forecasting purposes and they need to undergo some statistical preparation and pre-adjustment. This is because in time series of raw data variance non-stationarity may be present. G. Galanopoulos disrupt the underlying stochastic process (existence of outliers, calendar effects, etc.). Galanopoulos disrupt the underlying stochastic process (existence of outliers, calendar effects, etc.)

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