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Inflation forecasts with ARIMA, vector autoregressive and error correction models in Nigeria
Abstract
This study examines the relative predictive power of ARIMA, VAR and ECM models in forecasting inflation in Nigeria. In doing this, a domestic Consumer Price Index(CPI) was lumped into headline(All Item). This is because decomposing Consumer Price Index will generate difficult task for monetary authority, since different factors determine inflation(CPI) under different types of CPI. Annual data that spanned from 1970-2010 were used. Comparatively, the study examines the performance of the forecasting ability of the models, and how well the simulated series track the actual data. In doing this, historical simulation of the models were carried out. Thus, it was observed that, different models performed well in different periods. While ARIMA is good as a benchmark model, VAR for short term forecasting and ECM is suitable for long run forecasting. The study shows that significant relationship exist between domestic CPI and exchange rate, US-CPI(foreign price) and government expenditure in predicting inflation movements in Nigeria. Hence, in transiting into inflation targeting framework by CBN, these variables must be critically monitored, examined and put into consideration before resorting to any policy option.
... Forecasting inflation is a crucial component of economic policy. The central bank, the authority on policy, needs to make predictions to make decisions [1]. The successful implementation of policy under the inflation target framework depends on the effectiveness of monetary policy, whether it is an interest rate adjustment or purchasing bonds from the private sector or government. ...
... In the past, inflation forecasts employed time series models such as vector autoregressive (VAR), autoregressive integrated moving average (ARIMA), and error correction models (ECM) [1]. They are a model capable of capturing patterns and trends in historical inflation data. ...
... Uko [1] assessed the comparative forecasting efficacy of ARIMA (despite being a univariate model), VAR, and ECM models in predicting inflation. In the study conducted by Uko [1], a substantial correlation was observed between the Domestic Consumer Price Index, the US dollar exchange rate, and government expenditures. ...
... Various models have been used to forecast inflation, such as ARIMA, VAR, and ECM, which are appropriately used in certain contexts (Uko & Nkoro, 2012). However, the BOL currently relies mainly on a macroeconomic model to target inflation, which does not adequately reflect the current situation. ...
... 110 วารสารบริ หารธุ รกิ จ Forecasting Inflation in Lao PDR: A Comparison of ARIMA and VAR Models Support for forecasting secondary data in Bangladesh with both ARIMA and VAR models by defining that the forecast can be compared, which is in line with the study of Khan and Khan (2020) found that the VAR model is the best compared to the ARIMA model because the forecast in the model comparison can be read according to the Root Mean absolute Error (RMSE) and Mean Absolute Percent Error (MAPE) with the smallest value that can explain the error and accuracy of the model Well, Sargolzaie, and Shahrami (2022) study the effect of oil conversion on the Inflation rate and the price of gold in Ehan than Gathingi (2014) found that the amount of money, crude price, and exchange rate all affect the exchange rate and also found that it is an essential variable in the economy in Kenya and can explain that the VAR model is the best compared to the ARIMA model. However, it differs from the studies of Erkekoglu et al. (2020) and Uko and Nkoro (2012), which tested the ARIMA model, Exponential Smoothing, VAR, and ECM and found that the results of the regression equation analysis of both of them are that the ARIMA model is more effective than the VAR model. Also, there are independent variables that are different from this research, namely GDP and GNI variables and different study cases. ...
This study aims to use a univariate time series in the form of an Autoregressive Integrated Moving Average (ARIMA) model developed by Box and Jenkins and a multivariate time series model in the form of a Vector Autoregressive model (VAR) to forecast inflation for Lao PDR. Our focus is to use change in quarterly inflation data obtained from the Bank of Lao PDR over the period 2005: Q1 to 2023: Q3 to analyze the forecast performance of the two models using measures of accuracy such as RMSE and MAPE statistics. So, the best forecasting model for predicting inflation in Lao PDR will be selected based on different diagnostic and evaluation criteria. ARIMA model (1,1,3) was the best model, and the VAR model used a vector error correction model. The Impulse Response Function and Variance Decomposition analyses reveal consistent results, indicating that the variables experience sudden changes or shocks. However, the Var model had the least minimum square error and is the closest approximate to current inflation at 26.4 percent in Q3:2023 in Lao PDR. The study forecasted core inflation using VAR for the quarterly of 2023: Q3 to 2024: Q4 to be 28.8 percent.
... Similarly, Datta [34] investigated the use of the ARIMA model for forecasting inflation in the Bangladesh economy, concluding that the ARIMA (1,0,1) model provided a satisfactory fit for the inflation data. Uko [35] conducted a comparative analysis of ARIMA, VAR (Vector Autoregressive), and ECM (Error Correction Models) to forecast inflation in Nigeria, with ARIMA emerging as a reliable predictor. Mondal et al. [36] applied ARIMA to forecast the next month's data of 56 stocks from seven sectors of the National Stock Exchange (NSE) of India, achieving prediction accuracy of more than 85%. ...
... Upon initial examination, it seems that the models used in this study can accurately predict the ranges in which the real data of the VN30 index will fall. This finding is consistent with the research of Ariyo et al. [37], Datta [34], Uko [35], and Nguyen TC et al. [39], all of whom have found that ARIMA is a suitable model for forecasting, particularly in the short-term. The comparison of forecast intervals shows that the 95% forecast interval is wider than the 80% interval. ...
This research paper examines the potential of forecasting the VN30 index, a prominent benchmark in the Vietnamese stock market, using the Auto Regressive Integrated Moving Average (ARIMA) model. Given the rapid evolution of the Vietnamese stock market since its inception in 2000, it presents unique challenges and opportunities for investors and policymakers. The increasing trading activity and the market’s relatively low efficiency make accurate forecasting essential for informed decision-making. The purpose of this study is to determine if the ARIMA model can effectively predict the future values of the VN30 index, providing insights into market trends and assisting stakeholders in navigating the complexities of the Vietnamese stock market. The methodology employed involves a comprehensive approach based on the theory of change and the steps outlined by Hyndman [1], including data visualization, variance stabilization, model selection, and residual analysis. Data used for this study consist of monthly and daily observations of the VN30 index over various periods, allowing for a robust analysis of market behavior. Findings indicate that the ARIMA model can be a valuable tool for forecasting in emerging markets like Vietnam, although challenges related to information transparency and corporate disclosure persist. The results suggest that the VN30 index’s future values generally align with ARIMA’s prediction interval, offering a degree of confidence for investors and market analysts. However, the study also highlights the need for ongoing improvements in market efficiency and transparency to enhance forecasting accuracy. This paper contributes to the existing literature by demonstrating the applicability of ARIMA in a developing market context and providing practical guidance for investors and policymakers.
Keywords:Forecast; ARIMA; Stock market; Vietnam; VN30 index
... The outcome demonstrates that the ARIMA (2, 0 and 2) model is the best ARIMA model for the banking sector with a 95% confidence range. Uko et al. (2012) he has a compared the relative predictive abilities of ARIMA, VAR, and ECM models. In order to anticipate inflation in Nigeria, the outcome demonstrates that ARIMA is an effective inflation forecasting model and a strong predictor of inflation in Nigeria. ...
The stock market is unpredictable and continuously changing. Fast information flow and quick capital
inflow will cause stock prices to fluctuate, which will then have an impact on the market. This is a
mutually influencing and conducive process. Since its inception, the rising market of India's stock
market has been incredibly unpredictable, frequently experiencing sharp ups and downs. The Nifty50
Index of the NSE in India is the index that this study chooses to analyze empirically and carve out the
characteristics of from an econometric standpoint. Additionally, it makes certain recommendations
based on the current level of Nifty50 Index volatility. Relevant and required data has taken from
secondary sources like articles, journals, and NSE websites. Were the 5 years data collected by daily
closing price of nifty50 index of NSE in India from 2nd of Jun 2018 to 30th of May 2023. The study
has investigated and analyzing based on descriptive methodology using of time series data. Were the
forecast market volatility of nifty50 of NSE by using of various methods as Unit root test, ARCH (1),
ARCH (2) Models to found that market the volatility.
... In econometrics, the determination of the dynamic influence of a variable on other variables require the use of multiple distributed lag models. However, the ARDL linear model addresses the problem of distributed lag more efficiently (Uko and Nkoro, 2012), because it is crucial in analyzing time lag effects of changes in the economy. ...
The Nigerian economy has been frontally constrained by unsteady GDP growth rates with episodes of recession, and other unimpressive socio-political and economic indices over the past three decades. The situation has become aggravated by the identifiable cases of fiscal and monetary distortions, macroeconomic shocks and gaps in our budgetary provisions that are evident in recurring deficit budgets, fiscal crises, and unsustainable debt profile. The persistence of these problems has, unarguably, impacted negatively on the overall economic performance of Nigeria, and this begs the question of the potency of the use of monetary policy and fiscal policy in addressing our targeted macroeconomic problems. Spurred by the need to finding policy solutions to these problems, this study empirically examines the relative impact of fiscal policy and monetary policy in stimulating gross domestic product in Nigeria. The achieve the study objectives, annual time series secondary data spanning from 1983 to 2021 were empirically analyzed using Autoregressive Distributed Lag (ARDL) estimation after the unit root text revealed a mixed order of integration. The result of the long and short run dynamics revealed that Total Government Expenditure (TOGE) has a positive and statistically significant relationship with GDP in Nigeria. Also, Broad Money Supply (MPMS) and Open Market Operations (MPOM) have positive and statistically significant relationships with GDP within the study period. The study concludes that in the short and long run, monetary policy and fiscal policy play significant roles in stimulating GDP growth in Nigeria. However, in relative terms, fiscal policy is more potent than monetary policy in Nigeria within the review period. The study, therefore, recommends that for the attainment of macroeconomic goals and sustainable economic development effective coordination of monetary and fiscal policy tools should be encouraged for consolidated socio-political and economic gains.
... Infation rate (IR) values are estimated as percentages [5]. A negative IR value indicates a reduction in the cost of goods and services paid for by consumers, whereas a positive value indicates an increase in the cost of goods and services paid for by consumers [1]. ...
Background. In economic theory, a steady consumer price index (CPI) and its associated low inflation rate (IR) are very much preferred to a volatile one. CPI is considered a major variable in measuring the IR of a country. These indices are those of price changes and have major significance in monetary policy decisions. In this study, different conventional and machine learning methodologies have been applied to model and forecast the CPI of Pakistan. Methods. Pakistan's yearly CPI data from 1960 to 2021 were modelled using seasonal autoregressive moving average (SARIMA), neural network autoregressive (NNAR), and multilayer perceptron (MLP) models. Several forms of the models were compared by employing the root mean square error (RMSE), mean square error (MSE), and mean absolute percentage error (MAPE) as the key performance indicators (KPIs). Results. The 20-hidden-layered MLP model appeared as the best-performing model for CPI forecasting based on the KPIs. Forecasted values of Pakistan's CPI from 2022 to 2031 showed an astronomical increase in value which is unpleasant to consumers and economic management. Conclusion. The increasing CPI trend observed if not addressed will trigger a rising purchasing power, thereby causing higher commodity prices. It is recommended that the government put vibrant policies in place to address this alarming situation.
... Comparing the three-model criterion, it suggests ARMA as the best model to forecast inflation in Nigeria for the data from 1960 -2016, forecast period ending 2021. Another study [17] is employed three models VAR, ECM and ARIMA to measure the predictive power of inflation. It concludes that ARIMA can be used as a benchmark model, VAR for short run and ECM for long run inflation forecasting model. ...
Elevated inflation again has been a key macroeconomic problem that impacts negatively on economic activities in recent times. Inflation is widely used as a short run monetary policy tool that has impact on redistribution of resources through price transmission mechanism in economies. Forecasting is also a challenging task with high volatilities of the price index that use to measure inflation. However, inflation forecasts are essential in setting monetary policy targets and the decision-making process in the short run. In Sri Lanka inflation recorded at its highest level ever in the year 2022 reversing its single digit inflation maintained in a decade. The aim of this paper is to estimate an inflation forecasting model using Automatic ARIMA technique in Sri Lanka employing data from 2014M01 to 2023M01 towards forecasting end point to end 2024, using secondary sourced monthly data. Accordingly, Colombo Consumer Price Index shows a further upward trend forecasting range given in CCPIC index point from 224 to 260 during the period for inflation measured using year on year base is in declining trend, below 10 per cent, but not par equal to the mid-single level as per the data, CCPI 2013=100. Given the demand full inflation factors, policies to encourage supply and production are recommended in the medium term.
... Where is the first difference of , while and are the intercept term and time trend respectively, while is the lag value. The Augmented Dickey-Fuller test is taken as superior to the Dickey Fuller because the Dickey Fuller test does not take account of possible autocorrelation in the error process (Uko & Nkoro, 2016). ...
Empirical studies have shown contradictory and/or inconclusive findings between international trade and economic growth in Nigeria and this necessitated this study. The study empirically examined the relationship between international trade and economic growth in Nigeria from 1986 to 2021 and used the Autoregressive Distributed Lag Model. Augmented Dickey-Fuller unit root test result established that, at levels and first difference, some variables were stationary while others were not. The Bounds test of co-integration showed a long run equilibrium relationship among the variables. The result further revealed that trade openness had negative and insignificant relationship with economic growth in Nigeria, because of the country’s narrow production and export base dominated by low value products such as primary commodities. Foreign direct investment was positively and significantly related to economic growth, implying that foreign direct investment was a major determinant of economic growth in Nigeria. Exchange rate was positively insignificant to economic growth in Nigeria, due to exchange rate volatility. The study recommended that, government should strengthen trade openness by dismantling trade barriers, add value to their exports and provide a level playing field for trading partners to achieve the desired gains from international trade vis-à-vis a sustainable economic growth in Nigeria.
... In order to project inflation in Nigeria, this study compares the performance of various neural network models. This is due to the fact that deconstructing the CPI will be a challenging work for the monetary authority, as various causes influence inflation under various types of CPI [15]. ...
... et la comparaison des modèles ARIMA, VAR et ECM(Uko et Nkoro, 2012). ...
This paper attempts to highlight the role of new short-term forecasting methods. It leads to the fact that artificial neural networks (LSTM) are more efficient than classical methods (ARIMA and HOLT-WINTERS) in forecasting the HICP of Côte d'Ivoire. The data are from the "Direction des Prévisions, des Politiques et des Statistiques Economiques (DPPSE)" and cover the period from January 2012 to May 2022. The root mean square error of the long-term memory recurrent neural network (LSTM) is the lowest compared to the other two techniques. Thus, one can assert that the LSTM method improves the prediction by more than 90%, ARIMA by 68%, and Holt-Winters by 61%. These results make machine learning techniques (LSTM) excellent forecasting tools.
... According to the results, the ARIMA (2, 0, 2) model is the best at 95 percent confidence interval for the banking sector. Uko and Nkoro (2012) examined the ARIMA, VAR, and ECM models in predicting Nigerian inflation. according to the findings, among the other models, ARIMA is a better to forecast inflation in Nigeria and can be used as a benchmark model for forecasting inflation. ...
... In the literature special attention is given to comparisons of more advanced prognostic models, such as comparisons of ARMA, ARIMA and GARCH models (Nyoni, 2018), comparison of VAR and ARIMA models in HICP prognosis in Austria (Fritzer, Moser & Scharler, 2002), comparison of ARIMA, VAR and ECM models (Uko & Nkoro, 2012). Suhartono (2005) compares the prognostic performances of the Neural Networks, ARIMA and ARIMAX models in inflation forecasting in Indonesia, where it is concluded that the neural networks model gives a more accurate inflation forecast compared to traditional econometric time series models. ...
The purpose of this paper is to compare the accuracy of the three types of models:
Autoregressive Integrated Moving Average (ARIMA) models, Holt-Winters models and
Neural Network Auto-Regressive (NNAR) models in forcasting the Harmonized Index of
Consumer Prices (HICP) for the countries of European Union and the Western Balkans
(Montenegro, Serbia and Northern Macedonia). The models are compared based on the
values of ME, RMSE, MAE, MPE, MAPE, MASE and Theil's U for the out-of-sample
forecast. The key finding of this paper is that NNAR models give the most accurate forecast
for the Western Balkans countries while ARIMA model gives the most accurate forecast of
twelve-month inflation in EU countries. The Holt-Winters (additive and multiplicative)
method proved to be the second best method in case of both group of countries. The obtained
results correspond to the fact that the European Union has been implementing a policy of
strict inflation targeting for a long time, so the ARIMA models give the most accurate
forecast of inflation future values. In the countries of the Western Balkans the targeting policy
is not implemented in the same way and the NNAR models are better for inflation forecasting
... In predicting inflation in Nigeria, Uko et al scrutinized the comparative extrapolative influence of VAR, ARIMA & ECM models. Their result illustrates that ARIMA is the best forecaster of inflation in Nigeria and function as a yard stick model in estimating inflation [11]. ...
... Other studies applied ARIMA to analyze and forecast unemployment and CPI, inflation, the exports of industrial goods from Punjab for the ensuing decade until 2020. The savings and credit to private sector impacted on economic growth, domestic Consumer Price Index(CPI), the rates of inflation etc. (Charline et al., 2016;Aham, 2012;Muhammad et al., 2012;Fuat, 2011;Gulshan & Sanjeev, 2010;Muhammad et al., 2016). There are many models used in forecasting but each model has its own advantages and limitations. ...
Credit is an economic category and is also a product of the commodity economy, which exists through many socio-economic forms to promote economic growth. Therefore, the objective of this paper is to analyst, compare and forecast domestic credit growth in Vietnam's and China's economy. Thus, the paper is applied by a method of an autoregressive integrated moving average (ARIMA) model. This model is fitted to time series data both to better understand the data and to forecast future points in the series. Hereby, the methodology is selected by Vietnam's bestfit model ARIMA (2, 3, 1) and China's best-fit model ARIMA (2, 3, 5). Analytical data are collected from 1996 to 2017, the sample fitted the model and is statistically significant. The result show the forecast result for next years. The Vietnamese and Chinese economy are the open economies and have domestic credit greatly contributed to economic growth. These results are the basis for policymakers to have a general view and define the impact of domestic credit growth on GDP between the two countries.
... There are several evidences in the literature supporting the forecasting strength of ARIMA model approach using Box-Jenkins procedure in forecasting [12][13][14][15]. Although, recent studies in Nigeria, have shown the VAR modeling and forecasting approach to be highly useful in predicting short run forecast [16][17], there is however a need to revisit the modeling and forecasting ability of ARIMA model in Nigeria using more recent data. ...
This study employs a univariate Autoregressive Integrated Moving Average (ARIMA) homoskedastic model in conjunction with Box and Jenkins modeling procedure to model and forecast annual Consumer Price Index (CPI) data in Nigeria from 1950 to 2014. The annual data on Consumer Price Index is obtained as secondary data from Penn World Table, the National Bureau of Statistics and the Central bank of Nigeria over the period 1950 to 2014. We examine the graphical, statistical, unit root and stationarity properties of the series using time plots, ACF, PACF, Phillips-Perron as well as Dickey-Fuller Generalized Least Squares unit root tests. The results show that the CPI data in Nigeria is non-stationary in level but stationary in logged first difference and thus integrated of order one, I(1). We then applied Box-Jenkins modeling methodology to search for an optimal model and found that ARIMA (3, 1, 0) was the best fitting model to describe CPI data series in Nigeria. The model was validated and found to be adequate and good. Based on this model, we forecast the future annual CPI in Nigeria for a period of 6 years from 2015 to 2020. The forecasts show a steady increase in the annual values of CPI in Nigeria. The study predicts that inflation will increase in Nigeria from 2015 since the confidence intervals of the forecast suggest a consistent increase in annual CPI during the forecasted period of 2015 to 2020.
... Wrathful to say It was rarely to find in literatures studies that used ARIMA model to forecast future earning based on time data series of earning per share (EPS), most research work focused on forecasting stock market indices, like Junior et al (2014) who assessed the performance of ARIMA model for time series forecasting of IBOVESPA, they concluded that the ARIMA model can be used for financial time series data forecasting. A study by Uko and Nkoro (2012) that analytically compared the influence of ECM, VAR, and ARIMA models in forecasting inflation of Nigeria, it revealed that ARIMA is a superior forecaster of inflation at Nigeria. ...
The study aims to estimate and forecast earnings of the firms listed in Amman Stock exchange (ASE) using a time series data of earning per share (EPS) for the period from 1978 till 2016. The data has been extracted from firms' annual reports. A wavelet Transform (WT) decomposes the data and detects the fluctuations and outlay values. The parameters p, d, and q are estimated using the ARIMA model, the results show that the ARIMA models accuracy criteria MASE and RSME have the lowest values (0.7089 and 0.0709) respectively, thus the forecasting accuracy is high. It is concluded that firms' earnings show slow increasing trend for the upcoming 38 financial years.
... The difficulty of controlling inflation and the time lag of monetary policy suggest the need to maintain stable inflation. Most studies that tried to forecast inflation in Nigeria either used ARIMA (Adebiyi et al., 2010;Olajide et al, 2012;Uko & Nkoro 2012;Etuk et al, 2012;Okafor & Shaibu 2013;Kelikume & Salami 2014;Mustapha & Kubalu 2016;Popoola et al., 2017), SARIMA (Doguwa & Alade, 2013) or a combination of both ( Otu et al., 2014;John & Patrick, 2016). ...
... Wrathful to say It was rarely to find in literatures studies that used ARIMA model to forecast future earning based on time data series of earning per share (EPS), most research work focused on forecasting stock market indices, like Junior et al (2014) who assessed the performance of ARIMA model for time series forecasting of IBOVESPA, they concluded that the ARIMA model can be used for financial time series data forecasting. A study by Uko and Nkoro (2012) that analytically compared the influence of ECM, VAR, and ARIMA models in forecasting inflation of Nigeria, it revealed that ARIMA is a superior forecaster of inflation at Nigeria. ...
The study aims to estimate and forecast earnings of the firms listed in Amman Stock exchange (ASE) using a time series data of earning per share (EPS) for the period from 1978 till 2016. The data has been extracted from firms' annual reports. A wavelet Transform (WT) decomposes the data and detects the fluctuations and outlay values. The parameters p, d, and q are estimated using the ARIMA model, the results show that the ARIMA models accuracy criteria MASE and RSME have the lowest values (0.7089 and 0.0709) respectively, thus the forecasting accuracy is high. It is concluded that firms' earnings show slow increasing trend for the upcoming 38 financial years.
... The result showed that best ARIMA models at 95% confidence interval for banks sector is ARIMA (2,0,2) model. Uko et al. (2012) examined the relative predictive power of ARIMA, VAR and ECM model in forecasting inflation in Nigeria. The result shows that ARIMA is good predictor of inflation in Nigeria and serves as a benchmark model in inflation forecasting. ...
Forecasting financial time series such as stock market has drawn considerable attention among applied researchers because of the vital role which stock market play on the economy of any nation. To date, autoregressive integrated moving average (ARIMA) model has remained the mostly widely used time series model for forecasting stock market series the problem of selecting the best ARIMA model for stock market prediction has attracted a huge literature in empirical analysis in view of its implication for national economics. In this paper, we consider the problem of selecting best ARIMA models for stock market prediction for Botswana and Nigeria. Using the standard model selection criteria such as AIC, BIC, HQC, RMSE and MAE we evaluate the forecasting performance of various candidate ARIMA models with a view to determining the best ARIMA model for predicting stock market in each country under investigation. The outcome of the Forecasting Stock Market Series with ARIMA Model empirical analysis indicated that ARIMA (3,1,1) and ARIMA (1,1,4) models are the best forecast models for Botswana and Nigeria stock market respectively.
... Uko et al. [6] finds the comparative analytical influence of ECM, VAR, and ARIMA models in predicting inflation of Nigeria. The study reveals that ARIMA is a superior forecaster of inflation at Nigeria and provide a standard model for inflation prediction. ...
The Term Market Capitalization represents the combined value of a company’s share.
Market capitalization has been calculated by multiplying the present share market price to the
numbers of outstanding share in the market. Companies use predicting technique to make
tactical decisions about market capitalization, productions, sales, etc. The precedent data are
used by companies to forecast, anticipating regarding to the future. Time series analysis can
be used to predict the future. This study is an effort to examine the best ARIMA model for
predicting market capitalization. Quarterly Market Capitalization data series of 21
Infrastructural Sector Companies listed in S&P BSE-200 Index have been used for this study.
The finest ARIMA models have been selected on the basis of criteria namely SC, AIC, DW
Statistic, RMSE, MAPE and Adjusted R-Square, etc. Best model has been considered on the
basis of smallest values of criteria. ARIMA investigation is applied for 21companies secluded
the firms into three categories i.e. companies on an upward trend, linear trend and downward
trend. This study also focused on the financial statement of the companies to reveal the
rationale for the trend of market capitalization during the study and forecasted period
This study examines the impact of trade openness and human capital on economic growth in China. The Autoregressive Distributed Lag cointegration model (ARDL) and CUSUM-of-squares test are used for data duration 1990-2023. Main results of the study show that trade openness and human capital are positively correlated with economic growth. Human capital is considered one of the most profitable assets in the country's economy, While, the country’s economy does not directly affect by trade openness. To enhance economic growth, the following suggestions can be made 1) reduce Trade Barriers like Lower tariffs, quotas, and other trade restrictions to facilitate international trade. 2) Increase funding for primary, secondary, and tertiary education to raise the average years of schooling and literacy rates. 3) Implement policies that improve ease of doing business, such as reducing bureaucratic red tape and enhancing legal protections for investors.
This research utilizes annual time series data on HICP to identify the most suitable econometric model for forecasting the inflation rate in Poland. In this study, we have compared and applied the Autoregressive Moving Average (ARMA) model and the Vector Autoregressive (VAR) model to predict the annual inflation rate using data from January 2002 to December 2020. Various methods are employed to determine the optimal model specifications, followed by the generation of forecasts within a rolling estimation window. The results consistently indicate that the ARMA (2, 0) model outperforms other specifications in forecasting Polish inflation. These findings suggest that the inflation rate is expected to continue its downward trend in the coming years. Consequently, this study offers valuable insights for guiding future actions and policymaking in response to prospective inflation scenarios.
Stock market plays a vital role on the providence of any country. This study attempts to select an appropriate ARIMA (Autoregressive Integrated Moving Average) model for forecasting the future behavior of four pharmaceuticals company; ACI Pharmaceuticals Limited (ACI), Beximco Pharmaceuticals Limited (BPL), RENETA and Square Pharmaceuticals Limited (SPL). Daily stock price data has been collected from January 2011 to December 2018. There are three parameters p, d and q in ARIMA model namely ARIMA (p,d,q).Box-Ljung (1978) diagnostic test was performed to select the p, d, q parameter that best fit to the data. At first checking the stationarity by Augmented Dickey Fuller test where the alternative hypothesis is the data is stationary. After taking first difference almost all the variable is stationary. Based on different selection criterion such as AIC, BIC, HQC, RMSE and MAE we can select the best forecasting ARIMA model for predicting stock market in each company under investigation. The outcome of the analysis indicated that ARIMA (0,1,1) model for ACI, ARIMA (0,1,1) model for BPL, ARIMA (0,1,1) for RENETA and ARIMA (0,1,2) for SPL are the finest forecast models respectively.
Many investors in order to predict stock prices use various techniques like fundamental analysis and technical analysis and sometimes rely on the discussions provided by
various stock market analysts. ARIMA is a part of time-series analysis under prediction algorithms, and this paper attempts to predict the share prices of selected pharmaceutical companies in India, listed under NIFTY100, using the ARIMA model. A
sample size of 782 time-series observations from January 1, 2017, to December 31, 2019
for each selected pharmaceutical firm has been considered to frame the ARIMA model.
ADF test is used to verify whether the data are stationary or not. For ARIMA model
estimation, significant spikes in the correlogram of ACF and PACF have been observed,
and many models have been framed taking different AR and MA terms for each selected company. After that, 5 best models have been selected, and necessary inculcation of various AR and MA terms has been made to adjust the models and choose the
best adjusted ARIMA model for each firm based on Volatility, adjusted R-squared, and
Akaike Information Criterion. The results could be used to analyze the stock prices and
their prediction in-depth in future research efforts.
Gross Domestic Product (GDP) is the market value of the all goods and services that are produced within the country's national borders in a year, Our study aims to estimate and predict Jordanian's GDP using a time series data for the period from 1978 till 2017, the data has been taken from Jordanian's department of statistics, Minitab and Matlab statistical software's are used , we deploy a wavelet transform (WT) model to decomposes the time series data then detecting the fluctuations and outliers values, also an ARIMA (autoregressive integrated moving average) is established, the fitted ARIMA (2, 2, 1) time series model is the best for modeling the Jordanian's GDP according to the recognition rules and stationary test of time series. The results show that the predicted values are within the range of 5%, and the prediction capability of this model is relatively adequate and efficient in modeling the annual GDP for the next 20 years, Thus, the prediction accuracy is considered high. It is concluded that Jordanian's GDP is in upward trend for upcoming 20 fascial years, Furthermore Jordanian's government has to follow more comprehensive economic policies and should implement key growth-enhancing reforms to strength its economy,also it has to stimulate job-creating growth and creates conditions to increase private investment and improve country's competitiveness.
The change of number delivery goods through PT. Pos Regional VII East Java Indonesia indicates that the trend of increasing and decreasing the delivery of documents and non-documents in PT. Pos Regional VII East Java Indonesia is strongly influenced by conditions outside of PT. Pos Regional VII East Java Indonesia so that the prediction the number of document and non-documents requires a model that can accommodate it. Based on the time series plot monthly data fluctuations occur from 2013-2016 then the model is done using ARIMA or seasonal ARIMA and selected the best model based on the smallest AIC value. The results of data analysis about the number of shipments on each product sent through the Sub-Regional Postal Office VII East Java indicates that there are 5 post offices of 26 post offices entering the territory. The largest number of shipments is available on the PPB (Paket Pos Biasa is regular package shipment/non-document ) and SKH (Surat Kilat Khusus is Special Express Mail/document) products. The time series model generated is largely a Random walk model meaning that the number of shipment in the future is influenced by random effects that are difficult to predict. Some are AR and MA models, except for Express shipment products with Malang post office destination which has seasonal ARIMA model on lag 6 and 12. This means that the number of items in the following month is affected by the number of items in the previous 6 months.
This paper surveys three topics: vector autoregressive (VAR) models with integrated regressors, cointegration, and structural VAR modeling. The paper begins by developing methods to study potential "unit root" problems in multivariate models, and then presents a simple set of rules designed to help applied researchers conduct inference in VARs. A large number of examples are studied, including tests for Granger causality, tests for VAR lag length, spurious regressions and OLS estimators of cointegrating vectors. The survey of cointegration begins with four alternative representations of cointegrated systems: the vector error correction model (VECM), and the moving average, common trends and triangular representations. A variety of tests for cointegration and efficient estimators for cointegrating vectors are developed and compared. Finally, structural VAR modeling is surveyed, with an emphasis on interpretation, econometric identification and construction of efficient estimators. Each section of this survey is largely self-contained. Inference in VARs with integrated regressors is covered in Section 2, cointegration is surveyed in Section 3, and structural VAR modeling is the subject of Section 4.
The monetarist model of inflation is tested with quarterly data
over the period 1960-1983 from six African countries: Egypt, Ghana,
Morocco, Nigeria, Sudan and Tunisia. The role of foreign factors in the
inflationary process is examined in the model, the lag length is
determined, the issue of causality is addressed and structural stability
is tested. The empirical results are, in general, consistent with the
monetarist explanation of inflation.
In view of the explicit link between inflation and the rate of monetary expansion that is invariably incorporated into many macroeconomic adjustment programmes in Africa, this paper, using both cross‐section and time series econometric analysis, provides some new empirical evidence on the relationship between money and inflation in Africa. It is shown that although, broadly speaking, the simple monetarist inflation model appears to hold when tested in cross‐section equations covering several countries and averaged over several years, the same is generally not true for individual countries in time series analysis or in cross‐section studies covering shorter time periods. In fact, the result of the analysis strongly suggest that factors other than the rate of monetary expansion have played an important role in determining short‐run inflation trends in Africa. With respect to the implications of these findings for the conduct of current macroeconomic policy and in particular, given the apparently lesser role that ought to be assigned to monetary factors over the short run, the paper urges greater flexibility in deploying policy instruments towards inflation targets in individual African countries and cautions against the application of rules based on regional results in favour of those derived from country‐specific findings.
Résumé:
Compte tenu du lien explicite entre inflation et taux d'expansion monetaire, auquel il est invariablement fait reference dans de nombreux pro‐grammes d'ajustements macro‐economique en Afrique, cet article, s'appuyant sur une analyse economique de donnees transversales et chronologiques fournit de nouvelles preuves empiriques des relations qui existent entre la monnaie et l'inflation en Afrique. On montre que, de facon generale, mkme si le simple modele monetariste d'inflation parait ktre valide lorsqu'on effectue des tests sur des equations transversales couvrant plusieurs pays et qu'on calcule sa moyenne sur plusieurs annees, il n'en est pas de mCme, pour les pays consideres isolement, dans l'analyse de series chronologiques ou dans les etudes transver‐sales couvrant des periodes plus courtes. De fait, les resultats de l'analyse donnent fortement a penser que des facteurs autres que le taux d'expension monetaire ont joue un role important dansla determination des tendances inflationnistes a court terme en Afrique. En ce qui concerne les consequences de
ces conclusions pour la conduite de la politique macro‐economique actuelle et, en particulier, vu le role moins important, a ce qui semble, qu'il faudrait
accorder court terme aux facteurs monetaires, l'article preconise une plus grande souplesse dans le deploiement de la panoplie des instruments de poli‐
tique economique pour atteindre les objectifs retenus pour le taux d'inflation dam les differents pays africains et met en garde contre l'application de regles fondees sur les resultats regionaux en faveur de celles elaborees a partir de conclusions propres aux pays.
Various criteria for estimating the order of a vector autoregressive process are compared in a simulation study. For the considered processes Schwarz's BIC criterion chooses the correct autoregressive order most often and leads to the smallest mean squared forecasting error in samples of the size usually available in practice.