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We would also not use the overall mean to make inferences (e.g. exhibits a trend and we make decisions based on this mean, we would certainly not, for example, want to use this parameter as a forecast of the future level of the series.
#HOW TO FORECAST IN EVIEWS SERIES#
However, if the series is steadily increasing overtime, i.e. How would one compute the mean of a time series of a specified length? Calculating the mean of a sequence of observations might appear to be a trivial problem, as we would just sum all readings and divide by their number. Time series data consist of readings on a variable taken at equally intervals of time. Motasam Tatahi is a specialist in the areas of Macroeconomics, Financial Economics, and Financial Econometrics at the European Business School, Regent’s University London, where he serves as Principal Lecturer and Dissertation Coordinator for the MSc in Global Banking and Finance at The European Business School-London. Dr Aljandali is an established member of the British Accounting and Finance Association and the Higher Education Academy. His previously published work includes Exchange Rate Volatility in Emerging Markers, Quantitative Analysis, Multivariate Methods & Forecasting with IBM SPSS Statistics and Multivariate Methods and Forecasting with IBM® SPSS® Statistics. He is currently leading the Stochastic Finance Module taught as part of the Global Financial Trading MSc. This highly hands-on resource includes more than 200 illustrative graphs and tables and tutorials throughout.Ībdulkader Aljandali is Senior Lecturer at Coventry University in London. The package provides convenient ways to enter or upload data series, create new series from existing ones, display and print series, carry out statistical analyses of relationships among series, and manipulate results and output. It allows users to quickly develop statistical relations from data and then use those relations to forecast future values of the data.
#HOW TO FORECAST IN EVIEWS SOFTWARE#
Statistical and econometrics concepts are explained visually with examples, problems, and solutions.ĭeveloped by economists, the Eviews statistical software package is used most commonly for time-series oriented econometric analysis. It uses a step-by-step approach to equip readers with a toolkit that enables them to make the most of this widely used econometric analysis software. Consequently, there are two possible moving average components MA(1) and MA(3).This practical guide in Eviews is aimed at practitioners and students in business, economics, econometrics, and finance. We can see that lags 1, and 3 exceed the confidence bands. Next, to determine the order of the moving average component (“q”), we have to observe the Autocorrelation column (ACF). For the purpose of this example, I will only consider an AR(1) component. Looking at the correlogram, the first lag is a highly significant AR(1) component, and then lags 2 and 3 are on the line and could be tested. The values that exceed the band suggest the possible order of the autoregressive component. In the column, we observe a confidence band on the sides. In order to determine the order of the autoregressive component (“p”), we have to observe the partial autocorrelation column (PACF). The aim of this step is to find all the possible models to estimate. We are displaying the correlogram in the first differences because we have confirmed that “CPI” is stationary in the first differences. To identify the order of the autoregressive and moving average components, we will focus on the correlogram of “CPI” in the first differences. If our variable is non stationary in levels, we need to apply the appropriate transformations (logs/differences) to make it stationary. Why? Our series needs to be stationary in order to forecast it. We have to begin our analysis by checking for stationarity. In our example, we are trying to fit an ARIMA model for the series “ consumer price index – USA“. In other words, on stage 1 we will determine “p”, “d” and “q”. Next, determining the order of our autoregressive and moving average components. We are first checking for stationarity of our variable of interest. ARIMA is written as ARIMA(p,d,q) where “p” is the order of the autoregressive component, “d” is the times we need to differentiate the variable to achieve stationarity, and “q” is the order of the moving average element.