Economic forecast is the process of analyzing data to make predictions about the future. This is an important process for businesses, as it helps them plan for things such as investments in new equipment or hiring new employees. Government officials also rely on economic forecasts when making decisions about taxes and spending. Economic forecasts can be controversial, however, because they often contain many subjective assumptions. These are based on the forecaster’s theory of how the economy works and can lead to biases. For example, if a forecaster believes that business activity is driven by the supply of money, they may pay more attention to indicators like corporate profits than to other economic indicators, such as inflation.
Other subjective reasons for forecasting errors can include how easy or hard the data are to collect and how well a model fits the data. For example, financial series, such as stock market prices or interest rates, are difficult to forecast because they fluctuate so much. But a model that accounts for seasonality or other factors could improve the accuracy of the forecast.
Economic forecasts are not always accurate, particularly when the economy is nearing a recession. Various models have been shown to yield large forecast errors during these periods, including autoregressive and multivariate linear regression. Other models, such as the DFMS model, tend to have better performance during recessions. In addition, a growing body of literature has shown that non-linear models can provide valuable information not captured by linear models.