Differentiate between conditional and unconditional forecasting

Q.9.1 Differentiate between conditional and unconditional forecasting. In your discussion also comment on the bias of each. Q.9.2 During a class discussion, a classmate suggested that the problem could be modelled using an ARIMA (2,1,1)

Unconditional forecasting refers to a method of making predictions in which the factors that affect the variable being forecast are not taken into account. This forecasting is applicable only to the trend of the variable over time. The bias of unconditional forecasting is positive, which means that it tends to overestimate the value of the variable being forecast. On the other hand, conditional forecasting makes predictions based on the assumption that the values of the independent variable(s) that influence the dependent variable are known. The bias of conditional forecasting is more likely to be small than positive. ARIMA (2,1,1) refers to a time series model consisting of two autoregressive terms, one moving average term, and one difference term. ARIMA models are widely used in time series forecasting because they are able to handle non-stationary time series data. The ARIMA (2,1,1) model is a special type of ARIMA model known as the Box-Jenkins model. This model can be used to forecast future values of a time series based on past observations. In order to use the ARIMA (2,1,1) model for forecasting, the model parameters must be estimated based on the time series data. The ARIMA (2,1,1) model is useful for forecasting time series data that exhibit a trend and a seasonal pattern.

Conditional vs Unconditional Forecasting

Conditional forecasting involves making predictions based on known values of independent variables that influence the dependent variable. This type of forecasting takes into account the relationships between variables and uses this information to make more accurate predictions. By considering the specific factors that affect the variable being forecasted, conditional forecasting can provide more precise results compared to unconditional forecasting. On the other hand, unconditional forecasting does not consider any external factors or independent variables when making predictions. Instead, it only focuses on the trend of the variable over time. This approach may lead to biased predictions, as it tends to overestimate the value of the variable being forecasted. The bias in unconditional forecasting is positive, meaning that the forecasts are consistently higher than the actual values. In summary, conditional forecasting takes into account external factors and independent variables to make predictions, resulting in potentially more accurate forecasts. Unconditional forecasting, on the other hand, relies solely on the historical trend of the variable and may lead to overestimation due to its lack of consideration for external influences.
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