The women’s apparel industry has specific characteristics and requires suitable tools for sales forecast. The proposed model, VAR, widely used in various fields, can incorporate multiple endogenous variables and the interactions of these variables would lead to appropriate forecast. This study discusses how the external economic conditions in Taiwan affect women’s apparel sales performance. It applies the reduced autoregression model to analyze data collected from a certain fashion retailer. Four variables, cotton price, exchange rate, CPI index of apparel, and the average monthly salary per job in Taiwan, are chosen as the external economics environmental parameters in this article. The result from impulse response function shows the sales revenue will grow following the month of salary increase, and vice versa. It indicates a corresponding relationship between sales and salary. After analyzing the variance decomposition, it is found that excluding sales’ fluctuation, salary has the most impact on sales, accounting for 21.5% of the variation in the long term. In addition, exchange rate explains 10% as well. In conclusion, the proposed model has introduced a new direction in sales forecast for women’s apparel industry.