Autoregressive Modeling Technique Research Articles

A New Look at Cross-Country Aggregation in the Global VAR Approach: Theory and Monte Carlo Simulation

AbstractRequirements to understand and forecast the behavior of complex macroeconomic interactions mandate the use of high-dimensional macroeconometric models. The Global Vector Autoregressive (GVAR) modeling technique is very popular among them and it allows researchers and policymakers to take into account both the complex interdependencies that exist between various economic entities and the global economy through the world’s trade and financial channels. However, determining the cross-section unit size while using this approach is not a trivial task. In order to address this issue, we suggest an objective procedure for the detection of the size of the cross-country aggregation in GVAR models. While doing so, we depart from the Akaike Information Criterion (AIC) and propose an analytical modification to it, mainly employing an ad hoc approach without violating Akaike’s main principles. To supplement the theoretical results, small sample performances of those procedures are studied in Monte Carlo experiments as well as implementing our approach on real data. The numerical results suggest that our ad hoc modification of AIC can be used to determine the structure of the cross-section unit dimension in GVAR models, allowing the researchers and policymakers to build parsimonious models.

Anaysis of Interest Rate Spread, Financial Development and Foreign Capital Inflow Nexus in Nigeria

ABSTRACT: The world has gone through various economic crises in the past century, and their effects have had varying implications on the economies of countries all over the world. In a bid to help stabilise economies all over the world, various measures have been put in place. Some of these measures include regulations and policies by the Bretton Woods institutions (World Bank and International Monetary Fund) and their agencies. The idea behind these measures is to ensure sustainable growth and development among member states and other countries of the world. Some of these policies include the privatization and liberalization policies of the Bretton Woods institutions as well as other policies bordering on such economic and social benefits for people all over the world. The Nigerian economy in our contemporary time can be described as passing through a rather 'volatile' phase with respect to interest rate, financial development and foreign capital inflow. Thus, this study investigated the impact of interest rate spread and financial development on foreign capital inflow in Nigeria. Several studies have been carried out using different methodologies, such as; co-integration equation, multivariate vector auto regressive (VAR) model and vector error correction technique. Each methodology used was in line with the objective of the research in question. However, to achieve its objective, this paper adopted the Classical Linear Regression Model. The results of the study showed that financial development has a positive impact on foreign capital inflow, while interest rate spread on the other hand, was also found to have a positive impact on foreign capital inflow. Other control variables such as market capitalization and treasury bills rate were also found to positively affect foreign capital inflow, while the all share index had a negative impact on foreign capital inflow. Some economists have opined that the Nigerian economy is currently going through a phase of recession. In order for large scale investments to be made within the country, there is a need for foreign participants to be attracted to the Nigerian economy and pull their resources, both financial and non-financial into the country (but, not without regulation). However, for these investors to have confidence to make investments in the Nigerian economy, there has to be a high degree of economic stability in the country. This can be supported by enacting and implementing policies that can enhance the quality of interest rates spread and financial development within the country. The policy implication here involves enhancing financial development through sound policies and adopting competitive interest rates, could increase the rate of foreign inflow into the economy. In view of this, it is also recommended that government policies should target interest rate competitiveness, financial and economic development as well as sustainability.

Green financing role on climate change-supportive architectural design development: directions for green architectural designs.

The purpose of the study is to study the role of green financing in developing climate change supportive architectural design development to shift the modern world towards the idea of green architectural designs. Thus, the research estimated the nexus among green financing, green architectural development, and climate change mitigation by using the unit root analysis technique, co-integration analysis technique, bound-test estimates, auto-regressive distributive lag-error correction modeling (ARDL-ECM) technique to predict different short-run and long-run relationships, and robustness analysis technique. Following the previous study, modeling green financing index and green architectural design index are used to measure the variables. The findings of the study confirmed that green financing has significant role in supporting the climate change induction in architectural design development both in short run and long run. Moreover, green financing supports in promoting green architectural designs. By this, the viability of green financing in climate change that induces architecturally designed building is confirmed. Correspondingly, empirical results have shown that green financing contributes in climate change with 0.66, green infrastructure development with 0.72, and economic development with 0.31. While in long-run, green financing role in changing inside of climate of the architectural design is 0.74, supports in green infrastructure development with 0.67, and holds the 0.29 percent potential of contributing in economic development. These findings are robust with the 0.74 value of F-statistics, 1.89 value of t-statistics, and 110 value of Narayan standard estimate. In last, the study suggested way forward for stakeholders to promote green architectural designs to achieve SDG 8, SDG 11, and SDG 13.

GTAR: a new ensemble evolutionary autoregressive approach to model dissolved organic carbon

AbstractThis article explores the forecasting capabilities of three classic linear and nonlinear autoregressive modeling techniques and proposes a new ensemble evolutionary time series approach to model and forecast daily dynamics in stream dissolved organic carbon (DOC). The model used data from the Oulankajoki River basin, a boreal catchment in Northern Finland. The models that were evolved used both accuracy and parsimony measures including autoregressive (AR), vector autoregressive (VAR), and self-exciting threshold autoregressive (SETAR). The new method, called genetic-based SETAR (GTAR), evolved through the integration of state-of-the-art genetic programming with SETAR. To develop the models, high-resolution DOC concentration and daily streamflow (as the external input for VAR) were measured at the same gauging station throughout the ice free season. The results showed that all the models characterize the DOC dynamics with an acceptable 1-day-ahead forecasting accuracy. Use of the streamflow time series as an exogenous variable did not increase the predictive accuracy of AR models. Moreover, the hybrid GTAR provided the best accuracy for the holdout testing data and proved to be a suitable approach for predicting DOC in boreal conditions.

Uncovering Asymmetries in Monetary Policy Transmission in the Economic Community of West African States: Dynamic Heterogeneous Panel Analysis

ABSTRACT As the Economic Community of West African States (ECOWAS) gravitates toward establishing a monetary union, characterized by a common central bank and a single currency, the eco, there is need for deeper knowledge of the monetary policy transmission mechanism to ensure the effective conduct of a common monetary policy in the region. This study investigates the existence of potential asymmetries in the transmission of monetary policy impulses and the implications for monetary policy effectiveness under a unified framework. It employs the dynamic heterogeneous structural panel vector autoregressive (VAR) modeling technique developed by Pedroni (2013), which allows for heterogeneous country-specific dynamics and accommodates cross-sectional dependencies. Based on quarterly data covering the period 1994–2016, the findings reveal statistically insignificant responses of real gross domestic product (GDP) and inflation in member countries to a common monetary policy shock, though the country-specific responses exhibit some asymmetries. The panel estimates for the region also do not produce statistically significant responses to the unanticipated monetary policy shock. In addition, the results indicate that the two plausible monetary transmission channels—the bank lending channel and the exchange rate channel—are either weak or inoperable at the level of ECOWAS and that could undermine the effective conduct of a common monetary policy within the proposed monetary union. Policy should, therefore, be directed at enhancing the transmission of monetary policy impulses in the region, including through far-reaching financial sector reforms, modernization of monetary policy frameworks, and the comprehensive harmonization of institutional and operational mechanisms of monetary policy across member countries.

Modeling leaf area index using time-series remote sensing and topographic data in pure Anatolian black pine stands.

We aimed to map and analyze LAI by using Landsat 8 and Sentinel-2 time series and the corresponding ground measurements collected in pure Anatolian black pine [Pinus nigra J.F. Arnold ssp. pallasiana (Lamb.) Holmboe] stands within seven-month (from June to December) period. A total of 30 sample plots were selected and seven-month changes of LAI values were determined through hemispherical photography for each sample plot. Remote sensing (reflectance values and vegetation indices obtained from Landsat-8 and Sentinel-2) and topographic (elevation, aspect, and slope) data were used to model the LAI for each month using multiple linear regression (MLR) method. Additionally, the data for all months were combined and modeled. In this case, autoregressive modeling techniques were used to solve the temporal autocorrelation problem. Our study indicated that the models developed from Sentinel-2 give more successful results than Landsat 8 on monthly LAI models. The most successful models were obtained in June by using the reflectance values ({R}_{mathrm{adj}}^{2} = 0.39, RMSE = 0.3138 m2 m−2), reflectance values–topographic data ({R}_{mathrm{adj}}^{2} = 0.59, RMSE = 0.3174 m2 m−2), vegetation indices–topographic data ({R}_{mathrm{adj}}^{2} = 0.82, RMSE = 0.2126 m2 m−2), and reflectance values–vegetation indices–topographic data ({R}_{mathrm{adj}}^{2} = 0.93, RMSE = 0.1060 m2 m−2). Among the autoregressive modeling techniques, the highest success was obtained with the Landsat 8 OLI using the moving average (2) procedure (R2 = 0.56). This study is significant that it is the first to analyze the monthly effect on LAI modeling and mapping in pure Anatolian black pine stands using both reflectance values, vegetation indices, and topographic data.

Education, Human Capital, and Endogenous Growth Nexus: Time Series Evidence from Pakistan

The concept of endogenous growth states that economic growth is caused by variables within the economy rather than external ones. This study's major goal is to demonstrate the link between education, human capital, and endogenous growth in Pakistan. For the period 1990–2021, time series data is used. The stationary is confirmed using the augmented dickey fuller (ADF) method. In this research, the non linear autoregressive distributed lag model (NARDL) technique is employed for the empirical analysis. Gross domestic product (GDP), education enrolment, human capital, inflation, trade, and gross fixed capital formation are the factors examined in this study. In the current research, gross domestic product, education enrollment, and human capital are dependent variables, while inflation, trade, gross fixed capital formation and education enrollment are independent variables. Inflation shows an inverse relationship with the gross domestic product, while trade and education enrollment show a positive relationship with the gross domestic product. Gross fixed capital formation shows an inverse relationship with human capital, while education enrollment and inflation also show a negative relationship with human capital. Gross fixed capital formation and trade show a positive impact on education enrollment, while inflation shows a negative impact on education enrollment. It is recommended that the government should decrease inflation to increase gross domestic product (GDP). This study suggests that education enrollment (EDUENROLL), trade (TRADE), and gross fixed capital formation (GFCF) also increase.

Karuk ecological fire management practices promote elk habitat in northern California

Abstract After a century of fire suppression and accumulating fuel loads in North American forests, prescribed burns are increasingly used to prevent conditions leading to catastrophic megafire. There is widespread evidence that prescribed fire was used by Indigenous communities to manage natural and cultural resources for thousands of years. Wildlife habitat is an example of an ecological response that was actively managed with prescribed burns by Indigenous American peoples and is an important factor in western US forest management planning, restoration and climate resilience efforts. We analysed the effects of modern prescribed burns informed by traditional ecological knowledge (TEK) on the predicted change in elk winter habitat in Karuk aboriginal territory in Northern California between 2013 and 2018 using species distribution and simultaneous autoregressive modelling techniques. Burn types most closely resembling Karuk traditional practices, specifically those incorporating multiple‐year broadcast burns, had significant positive effects on elk winter habitat suitability. Conversely, concentrated burns focused solely on reducing fuel loads had significant negative effects on elk winter habitat suitability. However, areas where these fuel‐reduction burns were combined with multiple years of broadcast burns featured the highest increases in habitat. Synthesis and applications. Our results suggest that transitioning to prescribed burns that more closely follow Karuk traditional ecological knowledge will promote elk habitat in the region. This would be best achieved through continuing to work closely with Indigenous representatives to plan and implement cultural fire prescriptions on a landscape scale, a trend we posit would benefit environmental management efforts across the globe.

Multivariate autoregressive model estimation for high-dimensional intracranial electrophysiological data

Fundamental to elucidating the functional organization of the brain is the assessment of causal interactions between different brain regions. Multivariate autoregressive (MVAR) modeling techniques applied to multisite electrophysiological recordings are a promising avenue for identifying such causal links. They estimate the degree to which past activity in one or more brain regions is predictive of another region's present activity, while simultaneously accounting for the mediating effects of other regions. Including as many mediating variables as possible in the model has the benefit of drastically reducing the odds of detecting spurious causal connectivity. However, effective bounds on the number of MVAR model coefficients that can be estimated reliably from limited data make exploiting the potential of MVAR models challenging for even modest numbers of recording sites. Here, we utilize well-established dimensionality-reduction techniques to fit MVAR models to human intracranial data from ∼100 – 200 recording sites spanning dozens of anatomically and functionally distinct cortical regions. First, we show that high-dimensional MVAR models can be successfully estimated from long segments of data and yield plausible connectivity profiles. Next, we use these models to generate synthetic data with known ground-truth connectivity to explore the utility of applying principal component analysis and group least absolute shrinkage and selection operator (gLASSO) to reduce the number of parameters (connections) during model fitting to shorter data segments. We show that gLASSO is highly effective for recovering ground-truth connectivity in the limited data regime, capturing important features of connectivity for high-dimensional models with as little as 10 seconds of data. The methods presented here have broad applicability to the analysis of high-dimensional time series data in neuroscience, facilitating the elucidation of the neural basis of sensation, cognition, and arousal.

The role of energy efficiency and energy diversity in reducing carbon emissions: empirical evidence on the long-run trade-off or synergy in emerging economies.

Energy efficiency is often argued as a low-cost carbon emission mitigation strategy than energy diversity for emerging economies. Thus, deviations from carbon emission reduction goals due to diminished energy diversity may partially be compensated by energy efficiency improvements. This urgently requires an empirical analysis of carbon emission reduction impact of energy efficiency and energy diversity along with an investigation of any spillover effects. This study, therefore, investigates the long-run implications of energy efficiency and energy diversity on carbon emissions for emerging economies, covering the period 1990-2017. The long-run effect of energy efficiency on energy diversity and vice-versa is also examined to uncover any trade-off or synergy. The sample panel consists of the biggest seven emerging economies including, Brazil, China, India, Indonesia, Mexico, Russia, and Turkey. The empirical analysis is based on an augmented environmental Kuznets curve model, estimated using the panel autoregressive distributed lag modeling technique. The results corroborate the argument that energy efficiency is an effective low-carbon strategy than energy diversity in the long run. Specifically, a 1% increase in energy efficiency and energy diversity reduces carbon emissions by 1.2% and 0.5%, respectively. Moreover, it is also revealed that the long-run impact of energy diversity on energy efficiency is negative, suggesting that a 1% increase in energy diversity reduces energy efficiency by 0.3%. This evidence uncovers a trade-off between these two strategies in the long-run. However, the long-run impact of energy efficiency on energy diversity is insignificant. These findings suggest that both low-carbon strategies must be promoted in tandem by considering the long-run trade-off between energy diversity and energy efficiency in emerging economies.

Autoregressive parametric modeling combined ANOVA approach for label-free-based cancerous and normal cells discrimination

Label free based methods received huge interest in the field of bio cell characterizations because they do not cause any cell damage nor contribute any change in its compositions. This work takes a close outlook of cancerous cells discrimination from normal cells utilizing parametric modeling approach. Autoregressive (AR) modeling technique is used to fit the measured optical transmittance profiles of both cancer and normal cells. The transmitted light intensity, when passes through the cells, gets affected by their intercellular compositions and membrane properties. In this study, four types of cells: lung-cancerous and normal, liver-cancerous and normal, were suspended in their corresponding medium and their transmission characteristics were collected and processed. The AR coefficients of each type of the cell were analyzed with the statistical technique called Analysis of variance (ANOVA), which provided the significant coefficients. The poles extracted from the significant coefficients resulted in an improved demarcation for normal and cancer cells. These outcomes can be further utilized for cell classification using statistical tools.

Autoregressive model‐enhanced variational mode decomposition for mechanical fault detection

Mechanical fault detection is an important research direction attracted a great attention. Variational mode decomposition (VMD) has been widely used due to the ability to generate in highly localised time-frequency (TF) representation. However, the method usually fails to analyse the non-stationary signal with the unknown low-frequency interferences. Combined with autoregressive (AR) model technique, an enhancement version of VMD method is proposed. First, VMD is employed to decompose the raw non-stationary vibration signal into a set of intrinsic mode functions (IMFs), which contain the faulty information, and the interference of high-frequency noises can be almost eliminated. Second, AR model is introduced to further purify each IMF through decreasing the interferences of unknown low-frequency components. Finally, Hilbert envelope analysis is applied to demodulate the purified signal and the fault can be determined by comparing the demodulation frequency with the theoretical faulty feature frequency. Numerical simulations and experimental investigations are conducted to validate the performance of the proposed method. The comparisons with two combinations, AR model and empirical mode decomposition (EMD), AR model and local mean decomposition (LMD) are given, and the comparative results verify the proposed method is superior to other methods for mechanical fault detection.

R-Peak Detection in ECG Signal Using Yule–Walker and Principal Component Analysis

ABSTRACT Proper diagnosis of clinical Electrocardiogram (ECG) is still a challenge. The minor variations in the attributes of ECG signal cannot be examined properly by simple visualization, rather an efficient technique is required to increase the chances of early prediction of the diseases. R-peak detection is one such important attribute. It plays an important role in the detection of Arrhythmias (heart diseases). Proper detection of Arrhythmias using R-peak requires two things: long time recording of the ECG and noise reduction. But, long time recording of ECG requires proper modeling for extracting features from long data records. In this paper, noise reduction is accomplished using a digital bandpass filter (DBPF), since its filtering characteristics are invariant with drift and temperature, and the features are extracted using Yule–Walker (YW) autoregressive modeling technique which is most appropriate for modeling non-stationary signals recorded for long times. So, databases of AHA (American Heart Association), Ventricular Tachyarrhythmia and MIT-BIH Arrhythmia have been investigated. A total of 18 ECG records were made for implementing the proposed methodology using MATLAB R2008b. The feature extraction step is performed by finding AR coefficients on the basis of the selected model order. YW method is applied for finding AR coefficients and Principal Component Analysis (PCA) is used for R-peak detection. In this paper, normal and abnormal signals have been considered during the detection process. The PCA without YW yields a sensitivity of 99.73%, a specificity of 99.80%, a detection rate (DR) of 99.73%, and an accuracy (ACC) of 99.66%, whereas the proposed PCA with YW (PCA + YW) yields a sensitivity (SE) of 99.88%, a specificity (SP) of 99.92%, a detection rate (DR) of 99.90%, and an accuracy (ACC) of 99.81%. Suitable comparisons of the results obtained using the proposed method have been carried out with those obtained using existing methods for illustration.

Comparison of ARIMA and NNAR Models for Forecasting Water Treatment Plant’s Influent Characteristics

A reliable forecasting model for each Water Treatment Plant (WTP) influent characteristics is useful for controlling the plant’s operation. In this paper Auto-Regressive Integrated Moving Average (ARIMA) and Neural Network Auto-Regressive (NNAR) modeling techniques were applied on a WTP’s influent water characteristics time series to make some models for short-term period (to seven days ahead) forecasting. The ARIMA and NNAR models both provided acceptable generalization capability with R2s ranged from 0.44 to 0.91 and 0.45 to 0.92, respectively, for chloride and temperature. Although a more prediction performance was observed for NNAR in comparison with ARIMA for all studied series, the forecasting performance of models was further examined using Time Series Cross-Validation (TSCV) and Diebold-Mariano test. The results showed ARIMA is more accurate than NNAR for forecasting the horizon-daily values for CO2, Cl and Ca time-series. Therefore, despite of the good predictive performance of NNAR, ARIMA may still stands as better alternative for forecasting task of aforementioned series. Thus, as a general rule, not only the predictive performance using R2 statistic but also the forecasting performance of a model using TSCV, are need to be examined and compared for selecting an appropriate forecasting model for WTP’s influent characteristics.

A sensor fault detection strategy for structural health monitoring systems

Structural health monitoring has drawn great attention in the field of civil engineering in past two decades. These structural health monitoring methods evaluate structural integrity through high-quality sensor measurements of structures. Due to electronic deterioration or aging problems, sensors may yield biased signals. Therefore, the objective of this study is to develop a fault detection method that identifies malfunctioning sensors in a sensor network. This method exploits the autoregressive modeling technique to generate a bank of Kalman estimators, and the faulty sensors are then recognized by comparing the measurements with these estimated signals. Three types of faults are considered in this study including the additive, multiplicative, and slowly drifting faults. To assess the effectiveness of detecting faulty sensors, a numerical example is provided, while an experimental investigation with faults added artificially is studied. As a result, the proposed method is capable of determining the faulty occurrences and types.

Enhanced cycle slip detection method for dual-frequency BeiDou GEO carrier phase observations

Cycle slip detection is one of the essential steps for high-precision GNSS data processing when carrier phase observations are involved, such as in precise point positioning (PPP) and precise orbit determination (POD). A number of algorithms have been developed since the 1980s and are effective for processing dual-frequency GPS. However, the emerging BeiDou navigation satellite system in China brings some new challenges for these existing algorithms, especially when small cycle slips occur more frequently. In this study, a large number of 1-cycle slips have been found in low-elevation BeiDou GEO carrier phase observations, which are collected by receivers of the IGS multi-GNSS experiment. Such small cycle slips should be identified and repaired, if possible, before PPP and POD processing. We propose an enhanced cycle slip detection method based on the series of dual-frequency phase geometry-free combinations. A robust polynomial fit algorithm and a general autoregressive conditional heteroskedastic modeling technique is employed to provide an adaptive detection threshold, which allows identification of such small cycle slips with high reliability. Simulated and real data tests reveal that the proposed method has both high sensitivity and low false-alarm rate even in the case of ionospheric scintillation.

Natural Gas Pipelines and the Value of Nearby Homes: A Spatial Analysis

We examine the effect of a natural gas pipeline on the sales prices of nearby homes in Hillsborough County, Florida. We use an ordinary least squares regression model in the hedonic format to regress the natural logarithm of sale price on a vector of housing, neighborhood, transactional, and environmental characteristics. We apply spatial autocorrelation using two spatially autoregressive modeling techniques: the spatial lag model and the spatial error model. The results yield statistically insignificant coefficients for each distance band, which indicates that it is highly unlikely that there is an association between the sale price of a home and its proximity to a natural gas pipeline.

ARDUINO UNO BASED OBSTRUCTIVE SLEEP APNEA DETECTION USING RESPIRATORY SIGNAL

The monitoring of breathing dynamics is an essential diagnostic tool in various clinical environments, such as sleep analysis, intensive care and central nervous and physiological disorder analysis. This paper introduces a mathematical representation of respiratory pattern in frequency domain .Sleep apnea is defined as cessation of airflow to the lungs during sleep for 10 sec. It normally results from either lack in neural input from the central nervous system (Central Sleep Apnea) or Upper airway collapse (Obstructive sleep apnea).Microcontroller based sleep apnea monitor consists of a piezoelectric sensor attached to rib cage of patient. The amplified signal obtained from the patient is applied to the microcontroller. The method mentioned in the paper is based on extraction of four enhanced main energy features of respiratory signal from 30 second respiratory data through auto regressive modeling and other techniques. The four features extracted are Signal power, Respiration frequency, Dominant frequency in power spectrum, Maximum power in specturm . These features are compared with their threshold values and introduced to a series of condition for each epoch.

Design of Universal Wireless Channel Generator Accounting for the 3-D Scatter Distribution and Hardware Output

Hardware to generate the wireless channel is indispensable to the tests of wireless equipment. Previous literature always focused on the pure-hardware design, which is difficult or expensive for the small- and medium-sized enterprises. Hence, this paper presents a simple strategy to develop a flexible and low-cost channel generator, where the theoretical derivation and hardware realization are integrated together to save the test-cost and reduce the realization difficulty. In the proposed method, the fading channel is first modeled in the Agilent advanced design systems (ADS) software, and then a conventional signal generator hardware is connected and controlled to output the physical fading signal to wireless equipment waiting for measurements. Meanwhile, to cover a widerange of fading scenarios, we exploit 3-D geometry-based stochastic channel model as well as the autoregressive (AR) modeling technique. In general, for a given nonisotropic 3-D scattering environment, the angle of arrival, autocorrelation function, and level crossing rate are derived first, and then the AR coefficients are obtained by solving the Yule-Walker equation. Finally, the AR coefficients are fed to the ADS software to control the hardware generating channels. In addition, the channel model and the radio frequency parameters can be controlled by the ADS software, yielding a great flexibility. We verify the proposed method via both the computer simulation and the hardware measurement, and the simulation agrees with the theoretical result well. In addition, the physical measurements also demonstrate that the physical fading channels are consistent with the design specifications.

Should the West African countries form a currency union?

This paper examines whether or not the Economic Community of West African States (ECOWAS), comprising fifteen countries, constitutes an Optimum Currency Area (OCA). The paper uses secondary data obtained from the International Financial Statistics Bulletin, covering the period 1986 to 2003. The Vector Autoregressive (VAR) modelling technique was used to investigate the optimality of the community as a currency area. The study found that shocks to the output growth rate and inflation rates aligned symmetrically. Except for Nigeria and Sierra Leone, shocks to the real exchange rates also aligned symmetrically across countries. However, the degree of openness variable showed asymmetrical disturbance across countries. This paper thus concludes that a low trade link exists among member countries of ECOWAS, traced principally to the fact that these countries’ exports were competitive rather than complementary. The asymmetric disturbance of real exchange rate shocks and the low degree of openness across the countries implied that the sub-region could not effectively form a successful Optimum Currency Area (OCA). Nevertheless, the sub-region exhibits some potential for forming an optimum currency area in the future.