Investigating Predictors of Inflation in Nigeria: BMA and WALS Techniques

Tumala, Mohammed M and Olubusoye, Olusanya E and Yaaba, Baba N and Yaya, OlaOluwa S and Akanbi, Olawale B (2017): Investigating Predictors of Inflation in Nigeria: BMA and WALS Techniques. Published in: African Journal of Applied Statistics , Vol. 5, No. 1 (June 2018): pp. 301-321.

Abstract

The recent economic conundrum arising from the fall in the international oil price has threatened the maintenance of price stability, a key function of the central bank, therefore the need to investigate predictors of inflationary measures arises. The model averaging method considers uncertainty as part of the model selection, and include information from all candidate models. We analysed a wide spectrum of inflation predictors and all the possible models for Nigeria CPI inflation using the Bayesian Model Averaging and Weighted Average Least Squares. The study uses fifty-nine (59) predictor variables cutting across all sectors of the Nigerian economy and three (3) measures of inflation, namely; all items consumer price index, core consumer price index and food consumer price index. The results from both model averaging techniques showed that maximum lending rate, world food price index and Bureau de change exchange rate are the significant drivers of inflationary measures among focus variables, while foreign assets, credit to private sectors, net credit to government and real effective exchange rate are the drivers of inflationary measures, for the auxiliary variables, strongly supporting the monetarist and open economy views on inflation. The structuralist view is reported to be relatively weaker because government expenditure is only significant at 10.0 per cent..

• Item Type: MPRA Paper
• Language: English
• Keywords: Bayesian estimation; BMA; Frequentist approach; Inflation rate
• Subjects: C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C20 - General; C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics

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