This chapter provides a foundational knowledge on the dynamic linear models. Dynamism is an essential component in Econometrics for capturing the temporal interdependence and evolution of economic variables. Dynamic models help to understand how current economic outcomes are influenced by past behaviors, decisions, or shocks which are important feature in real-world scenarios where time dependencies are common. Economic variables such as inflation, GDP growth, and stock prices often depend on their own historical values. The importance concepts of ARIMA (Auto Regressive Integrated Moving Averages) and Vector Auto Regression (VAR) are introduced in this chapter to explicitly account for the lagged time-variant relationships. This approach helps for better time-series analysis, forecasting, and policy evaluation based on historical trends.