Disaggregation Comparison Analysis of Economic Time Series Data

Abstract

Econometrics modeling often implies the use of a number of time series data,some of which could be available at lower frequency and therefore, it could be convenient todisaggregate these data to high frequency form instead of estimating with a significant loss ofinformation. The main aim of temporal disaggregation is to derive an estimate of theunderlying high frequency (HF) observation of an observed low frequency (LF) time series.The method adopted by Chow-Lin, Fernadez, Litterman, (static model), and SantoSilvacardoso (dynamic model) were used to make comparison in disaggregation economicanalysis of time series data. The parameters employed in this study are Autoregressive test,Correlation and standard Deviation.Result of analysis in low frequency form (Annual) confirmed that Chow-Lin has thecorrelation value of 0.9914, Fernandez has the correlation value 0.9914, Litterman has thecorrelation value 0.9701 and Santo Silvacardoso has the correlation value of 0.9914. Result ofanalysis in high frequency (monthly) confirmed that Chow-Lin has the correlation value of0.9899 and Standard Deviation of 212850.48, Fernadez has the correlation value of 0.9899and Standard Deviation of 78553.54, Litterman has the correlation value of 0.9997 andStandard Deviation of 789109.18 while Santo Silvacardoso has the correlation value of0.9898 and Standard Deviation of 2337.24.The performance indicators of disaggregated values for Chow-Lin, Fernandez,Litterman being a static model and Santo Silvacardso being a dynamic model, annual andmonthly data confirms that the results of analysis are very good with high correlation figureswhile the ability of the estimated monthly data captured the true dynamic of the series. SantoSilvacardoso being a dynamic model preformed better with minimum standard deviationwhile Litterman technique being a classic and static model preformed poorly indisaggregating to high frequency form.Keywords: Disaggregation, Low frequency data, High Frequency Data, Static Model,Dynamic model.