Input-Output analysis provides important information about the structure of a country’s economy. The construction of input-output tables based on detailed census or surveys is a complex procedure requiring substantial financial outlay, human capital, and time. This is the main reason why Malaysia Input-Output (MIO) Table is produced and published on average once every five years. For policy makers past data is not seen as suitable for planning economic policies. The aim of this study is to compare RAS and Euro methods to project input-output tables for Malaysia. The data for the study are MIO table and Gross Domestic Product for the years 2000, 2005 and 2010. The RAS and Euro method were used to project the MIO table 2005 using MIO table 2000 and also projection of MIO table 2010 using MIO table 2005. The projection of I-O tables involved an intensive iterative procedure using Excel Visual Basic programming. The projection performance of RAS and Euro methods were assessed based on Mean Absolute Deviation (MAD), Root Mean Squared Error (RMSE) and Dissimilarity Index (DI). The results show that Euro method performed better than the RAS method in the projection of MIO table.
Combining forecast values based on simple univariate models may produce more favourable results than complex models. In this study, the results of combining the forecast values of Naïve model, Single Exponential Smoothing Model, The Autoregressive Moving Average (ARIMA) model, and Holt Method are shown to be superior to that of the Error Correction Model (ECM).Malaysia’s unemployment rates data are used in this study. The independent variable used in the ECM formulation is the industrial production index. Both data sets were collected for the months of January 2004 to December 2010. The selection criteria used to determine the best model, is the Mean Square Error (MSE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). Initial findings showed that both time series data sets were not influenced by the seasonality effect.