Determining Harmonic Fluctuations in Food Inflation

Yılmaz Akdi, Kamil Demirberk Ünlü, Cem Baş and Yunus Emre Karamanoğlu

Chapter 3 in Modeling and Advanced Techniques in Modern Economics, 2022, pp 47-66 from World Scientific Publishing Co. Pte. Ltd.

Abstract: In this study, we start with a brief expression of consumer price index of Turkey. In the next step, we give the theoretical essentials of periodogram-based unit root and harmonic regression model. Periodogram-based unit root test is used to identify both the stationarity of data and periodicities. Periodicity is beyond seasonality; it is the hidden cycles in the data. Thus, it is harder to detect them compared to seasonal cycles. Harmonic-regression-type trigonometric regression models are useful in modeling data which have hidden periodicity. Afterward, the stationarity properties of monthly inflation and monthly food inflation of Turkey for the period between 2004 and 2020 are investigated. Standard augmented Dickey–Fuller unit root test shows that both series are integrated of order one. However, the periodogram-based unit root test shows that monthly inflation has unit root but monthly food inflation does not. After examining the unit root, the hidden cycles in the food inflation are revealed. The cycles in food inflation are important because they may trigger a headline inflation. The main contribution of this study is the identification of the hidden cycles in food inflation. It has cycles of approximately two, four, six and eight years. These cycles, in short, correspond to cycles of two years of consecutive periods.

Keywords: Harmonic Regression; Periodograms; Consumer Price Index; Food Inflation; Turkey; Gaussian Distribution; Europe Union; GDP; Panel Data; Spatial Regression; Measurement Errors; Nonlinear Time Series; Chaotic Time Series; Weibull Distribution; Location Parameters; Fiducial Approach; Hypothesis Testing; Green Swan; Financial Stability; Annex II Countries; Financial Time Series; Kernels; Stock Index; Machine Learning; Statistical Learning; Optimization; WSAR Algorithm; Deep Neural Networks; Phyton; Parameter Estimation; COVID-19; Clustering Analyses; Artificial Neural Networks; Performance Criteria; Time Series Forecasting; Statistical Inference (search for similar items in EconPapers)
JEL-codes: C1 C4 C5 C6 C63 (search for similar items in EconPapers)
Date: 2022
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