Dynamic Model Implementation

This chapter introduces various dynamic econometric models and their relevance in analyzing and forecasting within dynamic business environments. It begins with the Two-Stage Least Squares (2SLS) estimation method, addressing endogeneity issues in simultaneous equation models. From there it moves to Auto Regressive (AR) models. AR models are foundational in time series analysis due to their ability to capture dependencies between successive observations. The chapter culminates with the implementation of the Auto Regressive Integrated Moving Average (ARIMA) model effective at identifying and leveraging underlying temporal patterns to make accurate forecasts. Additionally, the chapter introduces the concept of rolling forecast, a valuable method for continuously updating predictions as new data becomes available. This approach enhances adaptability and responsiveness, making it useful in rapidly changing business contexts. Through practical applications, the chapter demonstrates how dynamic models support informed decision-making and strategic planning.