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Context 1
... the analyses in this paper are analyzed using R software [17] [18] [19]. Figure 2 displays the time series plot for Figure 1. Time series plot of yearly inflation rates from 1965 to 2017. ...
Context 2
... means that a unit root exist and as a result the data are non-stationary. These results on non-stationarity of the inflation data are evident from Figure 2. ...
Context 3
... recall that Figure 2 shows that there is some level heteroskedasticity (changing variance over time) in the monthly rate of inflation series, and a formal test for heteroskedasticity was carried out to confirm the presence of heteroskedasticity (ARCH effect of heteroskedasticity (ARCH effects) although it has been reduced as compared to the case of the original monthly rate of inflation series. ...
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Citations
... Utilizing the ARIMA model, the research identifies the ARIMA (1, 1, 1) configuration as the optimal fit, highlighting a consistent depreciation trend of the Ghana Cedi relative to the US Dollar. Abdul-Karim [2] shifts the focus towards the volatility characteristics of Ghana's inflation rates spanning the years 2000 to 2018. Through the application of ARCH, GARCH, and EGARCH models, it was determined that the EGARCH (12, 1) model most accurately represents the data, demonstrating an upward trajectory in the overall price levels of goods and services. ...
Inflation remains a persistent challenge for many African countries. This research investigates the critical role of machine learning (ML) in understanding and managing inflation in Ghana, emphasizing its significance for the country's economic stability and growth. Utilizing a comprehensive dataset spanning from 2010 to 2022, the study aims to employ advanced ML models, particularly those adept in time series forecasting, to predict future inflation trends. The methodology is designed to provide accurate and reliable inflation forecasts, offering valuable insights for policymakers and advocating for a shift towards data-driven approaches in economic decision-making. This study aims to significantly advance the academic field of economic analysis by applying machine learning (ML) and offering practical guidance for integrating advanced technological tools into economic governance, ultimately demonstrating ML's potential to enhance Ghana's economic resilience and support sustainable development through effective inflation management.
... Regarding credit to the private sector, Katusiime (2018) indicates that private sector credit growth is positively linked to one-period lagged inflation volatility. Iddrisu et al. (2019) assessed the volatility of Ghana's inflation rates using autoregressive conditional heteroskedasticity (ARCH), generalized ARCH (GARCH) and exponential GARCH (EGARCH) models. The results indicate that higher-order models are required to explain Ghana's inflation volatility properly. ...
Purpose
The purpose of this paper is to emphasize the risks involved in modeling inflation volatility in the context of macroeconomic policy. For countries like Ghana that are always battling economic problems, accurate models are necessary in any modeling endeavor. We estimate volatility taking into account the heteroscedasticity of the model parameters.
Design/methodology/approach
The estimations considered the quasi-maximum likelihood-based GARCH, stochastic and Bayesian inference models in estimating the parameters of the inflation volatility.
Findings
A comparison of the stochastic volatility and Bayesian inference models reveals that the latter is better at tracking the evolution of month-on-month inflation volatility, thus following closely the data during the period under review.
Research limitations/implications
The paper looks at the effect of parameter uncertainty of inflation volatility alone while considering the effects of other key variables like interest and exchange rates that affect inflation.
Practical implications
Economists have battled with accurate modeling and tracking of inflation volatility in Ghana. Where the data is not well-behaved, for example, in developing economies, the stochastic nature of the parameter estimates should be incorporated in the model estimation.
Social implications
Estimating the parameters of inflation volatility models is not enough in a perpetually gyrating economy. The risks of these parameters are needed to completely describe the evolution of volatility especially in developing economies like Ghana.
Originality/value
This work is one of the first to draw the attention of policymakers in Ghana towards the nature of inflation data generated in the economy and the appropriate model for capturing the uncertainty of the model parameters.
... The continuously increasing inflation rate will have an impact on the economy of a region and even a country [33], [34]. This will have an impact on increasing prices for goods and services which are a long-term community need, depreciating currency values, and even increasing the poverty rate [35]- [37]. Therefore, it is important to take actions that can reduce inflation. ...
The Consumer Price Index is one of the indicators used to confirm financial success in inflation management. This study aims to help determine the CPI prediction value in Indonesia for the next twelve periods in a month using the ARIMA (Autoregressive Integrated Moving Average) method using the data from January 2015 to March 2022. The results obtained show that the best model that can be used for forecasting is the ARIMA model (2,1,2) with drift with Akaike's Information Criterion (AIC) values of 2190.84. The results of Indonesia's accurate CPI forecasting can be used to assess inflation management for policymaking in the context of controlling inflation.It can be concluded that Based on the analysis, the optimal ARIMA model for forecasting Indonesia's CPI is ARIMA (2,1,2) with drift, aiding in evaluating inflation management for policymaking
... A plethora of literature has studied volatility in some time series variable such as crude oil price (Suleiman et al., 2015Alhassan & Kilishi, 2016Muhammed & Faruk, 2018;Zhang et al., 2019;Nguyen & Walther, 2020;;Boitumelo et al., 2020), exchange rate (Kuhe & Agaigbe, 2018;Abdullah et al., 2017;Okoro & Osisiogu, 2017;Dritsaki, 2019), stock prices (Al Rahahleh & Kao, 2018;Iwada et al., 2018;Lin, 2018;Aliyev et al., 2020), inflation (Fwaga et al., 2017;Nyoni, 2018;Iddrisu et al., 2019) amongst others. Modeling PVCO by some authors has reported varying outcomes over the years. ...
Modeling price volatility of crude oil (PVCO) is pertinent because of the overbearing impact on any oil-producing economy. This study aimed at evaluating the performance of some volatility models in modeling and forecasting crude oil returns. Utilizing daily returns data from October 23, 2009, to March 23, 2020, this study attempted to capture the dynamics of crude oil price volatility in Nigeria using a symmetric and asymmetric GARCH models. In our research, we considered the generalized autoregressive conditional heteroscedastic model (GARCH), Exponential (E-GARCH), Glosten, Jagannathan and Runkle (GJR-GARCH) and Asymmetric Power (AP-ARCH) under six error innovations that include the skewed variant of the student-t, generalized error and normal distribution. From the results obtained, it was discovered that the AP-ARCH (1, 1) model performed better in the fitting and performance evaluation phase. The skew Student's t-distribution (SStD) was also reported to be the best performing error innovation in most of the models. Based upon these results, we conclude that the AP-ARCH (1, 1)-SStD model is the best model for capturing the dynamics of crude oil returns in Nigeria.
... We find a bidirectional relationship between stock market development and inflation. Similar results were found for African countries where inflation brought volatility to the capital market (Iddrisu et al., 2019), and European countries . Any economy with high inflation refers to a time of instability. ...
This paper examined the relationship between economic growth, inflation, stock market development and banking sector development for a panel of sixteen high-income countries for the period from 2001 to 2016, by using the mechanism impulse response functions and Granger causality tests derived from a panel vector autoregressive model. The evidence of bidirectional causality between all variables in the model was found. Overall, feedback and supply-leading theories have been confirmed in the literature. A plus sign in the relationship between the development of the banking sector and the stock market with economic growth was found. Therefore, stock market development and banking sector development stimulate the economy.
Türkiye ekonomisinde son on yılda yaşanan aşırı fiyat artışları, ülke ekonomisinde önemli bir refah kaybına sebebiyet vermiştir. Bu çalışmadaki temel amaç 2005M01-2024M04 zaman serisini baz alarak fiyat artışlarının kalıcı olup olmadığını ampirik analizlerle sorgulamaktır. Geleneksel birim kök testleriyle enflasyon serisinin durağanlığının incelendiği çalışmada, enflasyondaki şokların kalıcılığını araştırmak için otoregresif koşullu değişen varyans modellerinden yararlanılmıştır. Elde edilen bulgulara göre enflasyondaki volatilitenin kalıcı olduğu tespit edilmiş, sonuçlar değerlendirilerek politika önerisinde bulunulmuştur.
The main objective of this study was to predict inflation rates in Ghana using standard econometric methods such as linear multivariate OLS regression, and the machine learning algorithms of Artificial neural networks and support vector machines, and to ascertain which of these methods best predicts these variables. The study utilised an objectivist ontological paradigm, a positivist epistemological viewpoint, and sound axiological (ethical) principles in collecting the data used for the analysis. A computer-based experimental, quantitative approach employing time-series data was designed and implemented using Gretl, IBM SPSS Statistics, IBM SPSS Modeller, and MATLAB software. The data set was partitioned into 50% training, 25% testing, and 25% validation and applied the OLS regression, Artificial Neural Network, and support vector machine models for predicting inflation rates in Ghana. The time-series data employed in the analysis was secondary in nature, spanning the period 1960 to 2019, and was collected from the websites of the Ministry of Finance (MoF), Ghana Statistical Services (GSS), African Development Bank (AfDB), Bank of Ghana (BoG), and the World Bank Development Indicators (WDI) for the dependent macroeconomic variable of inflation rates, and all the independent variables employed in predicting of inflation rates in Ghana. Originality/Value The model training, testing, and validation experimental results from the Linear OLS, NARX ANN and SVM results reveal that current and past (one-year lagged values) of the output gap, rainfall, and money supply are the most important predictors of inflation rates in Ghana. Concerning the predictive ability of the models, the fine Gaussian SVM model performed better than the cubic SVM kernel, NARX ANN, and Linear OLS regression models in predicting inflation rates in Ghana in the training, testing, and validation data. The main value of this study is the identification of the determinants of inflation rates in Ghana. Additionally, the study brings to the bear the superior predictive ability of the fine Gaussian SVM model in forecasting Ghana's inflation rates better than the cubic SVM kernel, NARX ANN, and Linear OLS regression models in predicting inflation rates in Ghana in the training, testing, and validation data.
