Inflation in Africa, 1960–2015

Franses, Philip Hans; Janssens, Eva

doi:10.1016/j.intfin.2018.09.005

Cited by 10 publications

(8 citation statements)

References 15 publications

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“…The result indicates that when prices grow the unemployment lower down as costs of operating businesses force investors to reduce jobs. The same result of Franses and Janssens (2018), corruption reduced the prices levels significantly. Termos et al (2013) focused on the GCC countries and found the drivers of inflation rates through the period 1972-2010.…”

Section: Literature Reviewmentioning

confidence: 68%

“…In this case, more production increases the competition between investors and reduce the prices effectively (this is consistent to the study of Ajmair, 2015). Franses and Janssens (2018) found the main drivers of inflation in 47 African countries Business and Economic Research ISSN 2162-4860 2020 during the period 1960-2015 using GARCH model (statistically). The main result of this study shows that the correlation between inflation and corruption is significant and negative.…”

Section: Literature Reviewmentioning

confidence: 96%

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What Drives Inflation in Gulf Cooperation Council (GCC) Countries

Alharthi

2019

BER

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The main reason of this study is to determine the main inflation determinants in Gulf Cooperation Council (GCC) countries over the period 1996-2016. The GCC area is exposing many economic challenges and risks like higher prices of basic products and services. Moreover, GCC countries impose value added tax in 2018 that increased the prices significantly. To reduce the prices rates, GCC governments decided to support the local manufacturing rather than depending on imports. However, controlling the inflation rates is showing the efficiency of economic administration for any country. In this study, the data was gathered through the United Nations Conference on Trade and Development (UNCTAD) International Monetary Fund (IMF) and World Bank (WB) databases. Statistically, the data were analysed by generalized method of moments (GMM) and generalized least square (GLS). The main results of this study show that the foreign direct investment (FDI) is one of main determinants that has an inverse and significant influence on inflation. Moreover, the corruption impacted the inflation positively and significantly. Finally, the oil prices are controlling the inflation as higher oil prices increase the inflation rates significantly.

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Section: Literature Reviewmentioning

confidence: 68%

Section: Literature Reviewmentioning

confidence: 96%

What Drives Inflation in Gulf Cooperation Council (GCC) Countries

Alharthi

2019

BER

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“…The first set of data concerns annual inflation rates for 41 African countries, ranging from 1960 to 2015. The data source is Franses and Janssens (). Table presents the estimates of α , μ ,

β σ_{ε}^{2}

, and

((), β σ_{ε}^{2}) / σ_{u}^{2}

, where this latter term will be used in Equation to create the adjusted forecast.…”

Section: Empirical Applicationmentioning

confidence: 99%

Model‐based forecast adjustment: With an illustration to inflation

Franses¹

2018

Journal of Forecasting

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This paper introduces the idea of adjusting forecasts from a linear time series model where the adjustment relies on the assumption that this linear model is an approximation of a nonlinear time series model. This way of creating forecasts could be convenient when inference for a nonlinear model is impossible, complicated or unreliable in small samples. The size of the forecast adjustment can be based on the estimation results for the linear model and on other data properties such as the first few moments or autocorrelations. An illustration is given for a first‐order diagonal bilinear time series model, which in certain properties can be approximated by a linear ARMA(1, 1) model. For this case, the forecast adjustment is easy to derive, which is convenient as the particular bilinear model is indeed cumbersome to analyze in practice. An application to a range of inflation series for low‐income countries shows that such adjustment can lead to some improved forecasts, although the gain is small for this particular bilinear time series model.

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“…Consider as an illustration the three annual inflation rates for France, Japan and the USA, see Franses and Janssens (2017) for data and graphs on these data and the others later. If we fit a first-order autoregression to each of these variables, the estimated autoregressive coefficients obtain values of 0.931, 0.776 and 0.823, respectively.…”

Section: Illustrationmentioning

confidence: 99%

“…No potential conflict of interest was reported by the authors. Model I: inflation t ¼ μ þ ρ inflation tÀ1 þ β PC North Africa;tÀ1 þ ε t Model II: inflation t ¼ μ þ ρ inflation tÀ1 þ β PC World; tÀ1 þ ε t The data are obtained from Franses and Janssens (2017). SEs are given within brackets.…”

Section: Disclosure Statementmentioning

confidence: 99%

Spurious principal components

Franses

Janssens

2018

Applied Economics Letters

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The principal component regression (PCR) is often used to forecast macroeconomic variables when there are many predictors. In this letter, we argue that it makes sense to pre-whiten the predictors before including these in a PCR. With simulation experiments, we show that without such pre-whitening, spurious principal components can appear and that these can become spuriously significant in a PCR. With an illustration to annual inflation rates for five African countries, we show that non-spurious principal components can be genuinely relevant in empirical forecasting models.

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Inflation in Africa, 1960–2015

Cited by 10 publications

References 15 publications

What Drives Inflation in Gulf Cooperation Council (GCC) Countries

What Drives Inflation in Gulf Cooperation Council (GCC) Countries

Model‐based forecast adjustment: With an illustration to inflation

Spurious principal components

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