Forecasting Sales with Trend Models

A financial analyst is evaluating a firm's sales data over a 10-year period to identify trends in the data. They decide to use a linear trend model to forecast future sales based on historical data. After fitting a linear regression model, they calculate the residuals and find that they exhibit a pattern suggesting non-constant variance, indicating heteroscedasticity.

The analyst is contemplating whether to adjust their model by transforming the dependent variable. They are considering taking the logarithm of the sales data as a possible solution to stabilize variance. The last known sales figure for the firm is $1,000,000, and they want to estimate the forecast for Year 11 using the adjusted model.

What will be the forecast for Year 11 if the estimated linear model's slope coefficient (after transformation) is 0.05 and the intercept is 13.81 (in logged form)? Assume that sales grow at a constant percentage rate and that the annual growth rate as calculated by the model is sufficient for a 10-year adjustment.