Is forecasting inflation easier under inflation targeting?

Özkan, Harun; Yazgan, M. Ege

doi:10.1007/s00181-013-0793-3

Cited by 4 publications

(1 citation statement)

References 22 publications

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“…Previous studies clearly indicates that performance of inflation forecasting depends on the type of model in use, monetary policy regime, the sample period, the variables included in the model, transformations applied to the data for stationarity and structural breaks as well as the length of the forecasting horizon (Lee, 2012, Ozkan & Yazgan, 2015. Buelens (2012) and Stock and Watson (2008) stated that the accuracy of a forecasting model depends on the sample period in which they are estimated and evaluated.…”

Section: Introductionmentioning

confidence: 99%

Comparing Multivariate Models’ Forecasts of Inflation for Brics and Opec Countries

Ajayi

2019

Business, Management and Education

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Purpose – This study identifies the most appropriately selected multivariate model for forecasting inflation in different economic environments. In specifying the multivariate models, the study test for the orders of integration of variables and for those that are nonstationary. For non-stationary variables, this study examines whether they are cointegrated. Engle and Granger (1987) establish that a cointegrating equation can be represented as an error correction model that incorporates both changes and levels of variables such that all of the elements are stationary. However, VARs estimated with cointegrated data will be misspecified if all of the data are differenced because long-run information will be omitted, and will have omitted stationarity inducing constraints if all the data are used in levels. Further, including variables in both levels and differences should sat-isfy stationarity requirements. However, they will omit cointegrating restrictions that may improve the model. Of course, these constraints will be satisfied asymptotically; but efficiency gains and improved multi-step forecasts may be achieved by imposing the constraints (Engle and Granger 1987, p. 259). Therefore, this study test for order of integration and compare inflation forecasting performance of different multivariate models for BRICS and OPEC countries. Research methodology – The following approaches were considered; the first approach is to construct a VAR model in differences (stationary form) to forecast inflation. The second approach is to construct a VECM without imposing cointegrating restrictions. The third approach is to construct a VEC that imposes cointegrating restrictions on the VECM. This will help to understand whether imposing cointegrating restrictions via a VEC improves long-run forecasts. Research limitation – The proposed multivariate models focused on differencing and cointegrating restrictions to ensure the stationarity of the data, the available variables were combined and specified based on their level of integration to forecast inflation. For instance, a VAR model is estimated based on differenced variables I(0); the same holds true for VECM and VEC models, where differenced variables and linear combinations of I(I) covariates are stationary. In future, multivariate models guided by economic theory rather than the order of integration of variables are suggested. Findings – The result shows that the forecast performance of inflation depends on the nature of the economy and whether the country experiencing higher inflation or low inflation. For instance, the model that includes long-run information in the form of a specified cointegrated equation generally improves the inflation forecasting performance for BRICS countries and one OPEC country (Saudi Arabia) that has a history of low inflation. Practical implications – This research will improve the policy makers decision on how to select appropriate model to forecast inflation over different economic environment. Originality/Value – These methods have not been used to forecast inflation for many emerging economies such as OPEC and BRICS countries despite the importance of many of these countries to the global economy. This study fills this gap by evaluating the forecasting performance of inflation using multivariate VAR and cointegrating models for OPEC and BRICS economies.

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Section: Introductionmentioning

confidence: 99%

Comparing Multivariate Models’ Forecasts of Inflation for Brics and Opec Countries

Ajayi

2019

Business, Management and Education

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Performance of Electricity Price Forecasting Models: Evidence from Turkey

Uğurlu

Taş

Gündüz

2018

Emerging Markets Finance and Trade

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ARMA-Based Inflation Forecasting Under the Inflation Targeting Framework in Sri Lanka

Liyanage

2024

IJEBO

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Inflation targeting has been introduced as one of the monetary policy framework since the end of the 1990s. Forecasting inflation under the inflation-targeting monetary policy framework is also expected to improve with superior performance by constantly feeding updated information into the forecasting process, considering the domestic and global market conditions and developments. Literature evidence that inflation forecasting under inflation targeting is a challenging task with risk and uncertainty that are not easy to capture even in the latest modeling techniques. Inflation-targeting monetary policy was adopted by the Central Bank of Sri Lanka after the enactment of the Central Bank of Sri Lanka Act with effect from 15.09.2023. This study examines inflation forecasting under the inflation-targeting monetary policy in Sri Lanka implemented with a clear mandate assigned in the agreement for a flexible inflation targeting framework to guide the market and inflation expectations. Automatic ARIMA forecasting techniques are employed to forecast inflation using two series of price indices with different base years from 2016M01 to 2026M12, and 2022M01 to 2026 M12 including 30 months as forecasting length. According to the findings, inflation forecasting is in line with the 5 percent targeted level with a 3 percent margin to fluctuate for both sides subject to the upside and downside risk factors. Accordingly, Inflation forecasting is within the inflation projections indicated under the inflation fan chart. It further suggests that inflation has been gradually stable in SL compared to the recently elevated inflation experience. Therefore, inflation targeting and projections can be used to shape and guide market expectations to facilitate decision-making in forward looking policies toward achieving and maintaining domestic price stability, which is the primary objective of the Central Bank of Sri Lanka.

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Is forecasting inflation easier under inflation targeting?

Cited by 4 publications

References 22 publications

Comparing Multivariate Models’ Forecasts of Inflation for Brics and Opec Countries

Comparing Multivariate Models’ Forecasts of Inflation for Brics and Opec Countries

Performance of Electricity Price Forecasting Models: Evidence from Turkey

ARMA-Based Inflation Forecasting Under the Inflation Targeting Framework in Sri Lanka

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