Random Walk is not a typical method for forecasting exports and imports. Econometric Models, Time Series Analysis, and Qualitative Methods are widely used approaches.

A lagged variable is a variable whose past value is used to predict the current value of another variable. It is commonly employed in time series analysis for forecasting.

Natural disasters are not typically considered when forecasting exports, as they are unpredictable and have a limited impact on long-term export trends.

Econometric models are used for all of the purposes mentioned: identifying relationships, predicting future values, and evaluating policy impacts.

Expert Opinion Surveys are a qualitative method where experts in the field provide their insights and predictions about future exports and imports.

A structural break is a sudden and significant change in the underlying relationship between economic variables, which can impact the accuracy of forecasts.

Logistic Regression Model is not a common time series model for forecasting exports and imports. ARIMA, Exponential Smoothing, and VAR models are widely used.

Forecasting exports and imports aims to predict future values, identify influencing factors, and inform policy decisions.

Social Media Data is not a common source of data for forecasting exports and imports. Historical Trade Data, Economic Indicators, and Consumer Surveys are widely used.

Forecasting exports and imports is challenging due to the complexity of economic relationships, data availability issues, and the impact of unexpected events.

Correlation Coefficient is not a common measure for evaluating forecast accuracy in the context of exports and imports. MAE, RMSE, and Adjusted R-squared are widely used.

Qualitative methods are used to incorporate expert knowledge, handle complex relationships, and deal with missing or incomplete data.

Econometric Models are not a common approach for incorporating expert opinion. Delphi Method, Expert Opinion Surveys, and Focus Groups are widely used.

Time series models are advantageous for capturing historical trends and patterns in exports and imports.

Ignoring outliers in the data is not a common technique for improving forecast accuracy. Combining methods, adjusting for structural breaks, and using real-time data are widely used.