ESTIMATING INFLATION AND INFLATION EXPECTATIONS BASED ON A MARKOV-SWITCHING VECTOR AUTOREGRESSION APPROACH: CASE OF UKRAINE

This article examines the behaviour of inflation and inflation expectations in Ukraine under conditions of macroeconomic instability caused by the full-scale Russian-Ukrainian war. Using a Markov Switching Vector Autoregressive (MS-VAR) model, the study investigates regime-switching dynamics between high and low volatility periods from 2021 to 2024. The findings reveal a significantly longer duration of high-volatility regimes (averaging 4 months) compared to low-volatility regimes (1.4 months), reflecting the prolonged instability in the macroeconomic environment.The results highlight that inflation expectations and consumer price index (CPI) dynamics differ markedly between regimes. Under low volatility, changes in the key policy rate have a stronger and more predictable impact on domestic prices, aligning with theoretical expectations. Conversely, during high volatility, both inflation expectations and the CPI are predominantly driven by their own fluctuations, limiting the central bank’s control. During periods of destabilization, the influence of monetary policy on the macroeconomy tends to weaken, as heightened volatility diminishes the effectiveness of policy transmission mechanisms.The study also demonstrates the utility of the MS-VAR model in capturing regime-dependent dynamics and the influence of monetary policy during crises. The model successfully passed the Stability Condition Check, ensuring its reliability for analysis. Insights from Ukraine’s experience in conducting monetary policy under conflict conditions offer valuable lessons for other nations facing similar economic challenges. The findings emphasize the critical role of adaptive monetary strategies in maintaining economic resilience amid crises.This research provides valuable insights into the interplay between macroeconomic indicators during periods of instability, such as crises or wartime, and offers a framework for policymakers to navigate the complexities of managing inflation and expectations under volatile conditions. The findings are particularly relevant for countries facing similar economic challenges, as they underscore the importance of understanding regime shifts and the nonlinear dynamics of inflation and economic policies during times of crisis or conflict.

Authors

Institutions

Publication Details

Journal
Financial and credit activity problems of theory and practice
Published
2025-06-30
DOI
https://doi.org/10.55643/fcaptp.3.62.2025.4724
Primary Topic
Monetary Policy and Economic Impact
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

ESTIMATING INFLATION AND INFLATION EXPECTATIONS BASED ON A MARKOV-SWITCHING VECTOR AUTOREGRESSION APPROACH: CASE OF UKRAINE

Iryna Lukianenko, Taras Tokarchuk, Mariia Nasachenko
1 links
Financial and credit activity problems of theory and practice
Monetary Policy and Economic Impact
article

ESTIMATING INFLATION AND INFLATION EXPECTATIONS BASED ON A MARKOV-SWITCHING VECTOR AUTOREGRESSION APPROACH: CASE OF UKRAINE

Iryna Lukianenko, Taras Tokarchuk, Mariia Nasachenko
article en

Abstract

This article examines the behaviour of inflation and inflation expectations in Ukraine under conditions of macroeconomic instability caused by the full-scale Russian-Ukrainian war. Using a Markov Switching Vector Autoregressive (MS-VAR) model, the study investigates regime-switching dynamics between high and low volatility periods from 2021 to 2024. The findings reveal a significantly longer duration of high-volatility regimes (averaging 4 months) compared to low-volatility regimes (1.4 months), reflecting the prolonged instability in the macroeconomic environment.The results highlight that inflation expectations and consumer price index (CPI) dynamics differ markedly between regimes. Under low volatility, changes in the key policy rate have a stronger and more predictable impact on domestic prices, aligning with theoretical expectations. Conversely, during high volatility, both inflation expectations and the CPI are predominantly driven by their own fluctuations, limiting the central bank’s control. During periods of destabilization, the influence of monetary policy on the macroeconomy tends to weaken, as heightened volatility diminishes the effectiveness of policy transmission mechanisms.The study also demonstrates the utility of the MS-VAR model in capturing regime-dependent dynamics and the influence of monetary policy during crises. The model successfully passed the Stability Condition Check, ensuring its reliability for analysis. Insights from Ukraine’s experience in conducting monetary policy under conflict conditions offer valuable lessons for other nations facing similar economic challenges. The findings emphasize the critical role of adaptive monetary strategies in maintaining economic resilience amid crises.This research provides valuable insights into the interplay between macroeconomic indicators during periods of instability, such as crises or wartime, and offers a framework for policymakers to navigate the complexities of managing inflation and expectations under volatile conditions. The findings are particularly relevant for countries facing similar economic challenges, as they underscore the importance of understanding regime shifts and the nonlinear dynamics of inflation and economic policies during times of crisis or conflict.

Financial and credit activity problems of theory and practiceVol. 3(62)
National University of Kyiv-Mohyla Academy (UA)
Openalex Percentile: Top 75%
Monetary Policy and Economic Impact
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.