Forecasting inflation in post-oil boom years: A case for regime switches?

In this study, we investigate the relative performance of various non-linear models against that of an autoregressive model in forecasting future inflation. We find that non-linear models have trivial forecast superiority over the univariate autoregressive model in terms of central forecast accuracy...

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Bibliographic Details
Published in:Journal of economics and finance 2018-04, Vol.42 (2), p.369-385
Main Authors: Ahmadov, Vugar, Huseynov, Salman, Adigozalov, Shaig, Mammadov, Fuad, Rahimov, Vugar
Format: Article
Language:English
Subjects:
Accuracy
Central banks
Economic forecasts
Economic models
Economics
Economics and Finance
Finance
Forecasting
GDP
Gross Domestic Product
Inflation
Macroeconomics/Monetary Economics//Financial Economics
Phillips curve
Probability
Random variables
Regression analysis
Studies
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Summary:In this study, we investigate the relative performance of various non-linear models against that of an autoregressive model in forecasting future inflation. We find that non-linear models have trivial forecast superiority over the univariate autoregressive model in terms of central forecast accuracy. They also perform poorly when their forecasts are measured against those of a VAR model. In addition, we also show that non-linear models cannot beat the random walk in terms of central forecast accuracy, which is in line with the previous literature on Azerbaijan during the post-oil boom years. However, we also demonstrate that non-linear models still have clear forecast advantages over both linear and random walk models in predicting forecast density.
ISSN:1055-0925
1938-9744
DOI:10.1007/s12197-017-9410-1