Log-Linearization Technique in DSGE Modeling: A Quick Guide

Crossmark

Article Sidebar

Page Numbers: 278-310
Download PDF
Published: Jun 27, 2025
Digital Object Identifier: 10.58578/alsystech.v3i3.6201
Save this to:
Save to Mendeley Download RIS

Article Metrics:
Viewed: 611 times
Downloaded: 601 times
Article can trace at:

Author Fee:
Free Publication Fees for Foreign Researchers (USD 0.00)
Check for article on SINTA:

LYAS Publisher cordially invites qualified professionals to serve as Editors or Reviewers. Your expertise will make an important contribution to maintaining and strengthening the academic quality of our publications. Interested applicants are kindly requested to complete the application form at the following link: Editors & Reviewers

Connected Papers:
Connected Papers


Please feel free to contact us if you need any further information about the submission process or if you have any additional questions.


Authors:
El Baouchari Nabil1*, El Baroudi Youness2
Copyright :

Authors retain copyright and grant the journal right of first publication.

Explore Our Journals
Find the journal that best fits your research. If this journal does not match your manuscript’s scope, explore our portfolio below to select a more suitable publication venue.

Main Article Content

El Baouchari Nabil
Mohammed V University in Rabat, Morocco
El Baroudi Youness
Mohammed V University in Rabat, Morocco

Abstract

This paper presents a concise yet effective technique for simplifying complex nonlinear equations by converting them into a log-linear form, with particular application to Dynamic Stochastic General Equilibrium (DSGE) models. The method, known as log-linearization, leverages Taylor series approximation to express nonlinear functions as linear relationships in logarithmic deviations around a steady-state equilibrium. By doing so, the approach enhances analytical tractability and facilitates the interpretation and solution of DSGE models. The paper provides illustrative examples, including applications to Real Business Cycle (RBC) models, to demonstrate the practical implementation and benefits of the technique in macroeconomic modeling.

Keywords:
Share Article:

Citation Metrics:

Scopus

Downloads

Download data is not yet available.

Citation Metrics & Similar Scopus Articles

Data source Crossref
0
citations
Citation counts are source-specific and may differ because database coverage, reference matching, and update schedules are different. Counts are not added together. Crossref values represent citation links registered and matched by Crossref.
Check Secondary Documents in Scopus
Open this article in Scopus, then check the Secondary documents tab. Use Manual Citation Fallback only for counts you have verified manually.
Open in Scopus
Similar Scopus Articles
Scopus
  1. Mirzahosseini M. (2027)
    A Review of Constitutive Modeling of Unsaturated Soils
    Iranian Journal of Geophysics, 20(3), 81-128
  2. Al Hasan R. (2027)
    3D modeling of electro-facies using a geostatistical algorithm
    Iranian Journal of Geophysics, 20(3), 129-151
  3. Urazkeldiyeva D.A. (2027)
    Development of technology for obtaining high-purity sodium chloride with induced impurity removal and process modeling
    Kompleksnoe Ispolzovanie Mineralnogo Syra, 342(3), 56-64

Article Details

How to Cite
Nabil, E. B., & Youness, E. B. (2025). Log-Linearization Technique in DSGE Modeling: A Quick Guide. ALSYSTECH Journal of Education Technology, 3(3), 278-310. https://doi.org/10.58578/alsystech.v3i3.6201

Creative Commons License
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

References

Arthur, W. B. (2021). Foundations of complexity economics. Nature Reviews Physics, 3(2), Article 2. https://doi.org/10.1038/s42254-020-00273-3

Cooley, T. F., & Prescott, E. C. (1995). Economic growth and business cycles. Econometrica, 63(1), 147-165.

DeJong, D. N., & Dave, C. (2011). Structural macroeconometrics. Princeton University Press.

Fernández-Villaverde, J., Rubio-Ramírez, J. F., & Schorfheide, F. (2016). Solution and Estimation Methods for DSGE Models. In Handbook of Macroeconomics (Vol. 2, p. 527 724). Elsevier. https://doi.org/10.1016/bs.hesmac.2016.03.006

Hamilton, J. D. (1994). Time series analysis. Princeton University Press.

King, R. G., Plosser, C. I., & Rebelo, S. T. (1988). Production, growth and business cycles: I. The basic neoclassical model. Journal of Monetary Economics, 21(2-3), 195-211.

King, R. G., & Rebelo, S. T. (1999). Chapter 14 Resuscitating real business cycles. In Handbook of Macroeconomics (Vol. 1, p. 927 1007). Elsevier. https://doi.org/10.1016/S1574-0048(99)10022-3

Madeira, J. (2013). Simulation and estimation of macroeconomic models in Dynare. In N. Hashimzade & M. A. Thornton (Éds.), Handbook of Research Methods and Applications in Empirical Macroeconomics. Edward Elgar Publishing. https://doi.org/10.4337/9780857931023.00035

Schmitt-Grohé, S., & Uribe, M. (2004). Solving dynamic general equilibrium models using a second-order approximation to the policy function. Journal of Economic Dynamics and Control, 28(7), 755-775.

Sims, C. A. (2002). Solving linear rational expectations models. Computational Economics, 20(1-2), 1-20.

Sun, G. (2009). Two useful tricks for log-linearization. https://www.uio.no/studier/emner/sv/oekonomi/ECON4325/v09/undervisningsmateriale/log-linearize.pdf

StataCorp. 2023. Stata 18 Dynamic Stochastic General Equilibrium Models Reference Manual. College Station, TX: Stata Press.

Valdivia, D. (2015). Handbook on DSGE Models : Some Useful Tips in Modeling a DSGE Models. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2550303

Woodford, M. (2003). Interest and Prices : Foundations of a Theory of Monetary Policy (Illustrated edition). Princeton University Press.