# Texas State Sales Tax Forecasting

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MBA 643-17 April - May 2015 Problem-Solving Skills Assignment One Texas State sales Tax Forecasting Due Date May 22, 2015 A major source of revenue in Texas is a state sales tax on certain types of goods and services. Data are compiled and the state controller uses them to project future revenues for the state budget. One particular category of goods is classified as Retail Trade. Four years of quarterly data (in millions) for one particular area of southeast Texas follows. Quarter | YEAR 1 | YEAR 2 | YEAR 3 | YEAR 4 | 1 | 218 | 225 | 234 | 250 | 2 | 247 | 254 | 265 | 283 | 3 | 245 | 255 | 264 | 289 | 4 | 292 | 299 | 327 | 356 | 1. Compute seasonal indices for each quarter for year 5 based on CMA. Seasonal indices…show more content…
Discuss the possibility of using the results found in question (c) to obtain the final forecasts. Results need to multiplied by the seasonal indices to get the final forecasts “adjusted” Q1, year5 = 299.937 * 0.8821 = 264.57 Q2 = 297.98 Q3 = 298.93 Q4 = 359.32 5. Develop a multiple regression model to predict sales (both trend and seasonal components) using dummy variables to incorporate the seasonal factor into the model. Y^ = 206.16 + 3.66 (X) + 26.84 (Q2) + 24.19 (Q3) + 75.78 (Q4) 6. Use this model to forecast sales for each quarter of the next year. Using above formula forecasts for year five will be: Q1 = 268.38, Q2 = 298.88, Q3 = 299.89, Q4 = 355.14 7. Critically analyse the accuracy of all the above models. Accuracy of forecasted data using above models are vary from model to model but we can say in this situation forecasts that we got in Q4 using seasonal indices are more accurate that using dummy method in projected line the reason behind that can be difficulty to implanting this model in real life because each quarter in each year are having different