Dynamic Modeling in Econometrics: Foundational Knowledge

Sarit Maitra ()
Additional contact information
Sarit Maitra: Alliance University

Chapter Chapter 3 in A Practical Guide to Static and Dynamic Econometric Modelling, 2025, pp 47-63 from Springer

Abstract: Abstract 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.

Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:conchp:978-3-031-86862-7_3

Ordering information: This item can be ordered from
http://www.springer.com/9783031868627

DOI: 10.1007/978-3-031-86862-7_3

Access Statistics for this chapter

More chapters in Contributions to Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().