Dickey-Fuller Research Articles

Nexus between Gross Fixed Capital Formation, Household Loans and Savings in Bulgaria

The goal of this article is to analyze the impact of household savings and household loans on gross fixed capital formation in Bulgaria. The research is carried out on the basis of quarterly data for the 2010-2023 period. The sources of the data are the National Statistical Institute (NSI) and the Bulgarian National Bank (BNB). The algorithm of the empirical research includes: checking for stationarity by means of the extended Dickey-Fuller test; determining the optimal lag of the model; Johansen cointegration test; specification of vector error correction model (VECM). As a result of the research, it has been established that there is a long-term relationship between the variables gross fixed capital formation, household savings and household loans. At the same time, about 39.2% of the disequilibrium between the studied variables in a certain quarter is compensated in the next one.

Evaluating the Effectiveness of Macroeconomic Determinants on the Performance of the Dhaka Stock Exchange: A Time Series Approach

The present study examines the causal relationships between selected macroeconomic factors and the Dhaka Stock Exchange Broad Index (DSEX) performance from January 2014 to December 2023. Time series econometric techniques include Dickey-Fuller (ADF) and Phillips-Perron (PP) tests to verify stationarity, Granger causality to investigate short-term dynamics, and Johansen's co-integration test to evaluate long-term relationships. The results show that while there is no long-term cointegration, there are significant short-term relationships between macroeconomic variables and the DSEX broad index. GDP growth and unemployment significantly impact stock performance, with two-way causality. While interest rates and the money supply have little impact on the index, inflation forecasts trends in inflation even if it doesn't directly influence it. Overall, the DSEX broad index shows how important it is to Bangladesh's macroeconomic dynamics by accurately predicting changes in GDP growth, unemployment, and inflation. The researcher provides unique insights into the mechanics of the Bangladeshi stock market, filling a vacuum in the literature on emerging countries. It analyzes how macroeconomic conditions affect the DSEX index over time, including the COVID-19 pandemic. The study investigates the influence of several macroeconomic variables on DSEX Index performance, providing significant information for Bangladeshi investors and policymakers looking to increase profits, inform policy choices, and promote economic stability. To have a better knowledge of stock market dynamics, future studies should include other macroeconomic aspects and regional markets.

Economic growth and unemployment nexus: empirical test of Okun’s law in Somalia

This paper aims to empirically examine the relationship between GDP and unemployment in Somalia from 2000 to 2021. The study also estimates Okun's coefficient. To evaluate the association between the unemployment rate and economic growth, we employ the Hodrick–Prescott (HP) filter detrending technique, the Augmented Dickey–Fuller (ADF) test, ordinary least squares (OLS), and fully modified OLS. The findings of this study demonstrate that the series is stationary at the level. However, the results confirm a statistically insignificant negative relationship between unemployment and economic growth. Consequently, our findings suggest that Okun's law does not apply in Somalia. For robustness, we employ fully modified ordinary least squares (FMOLS), canonical cointegrating regression (CCR), and dynamic ordinary least squares (DOLS). Nevertheless, the relationship between the GDP gap and unemployment is not strong enough to be considered statistically significant, and other factors may also influence unemployment. Therefore, policies aimed at reducing unemployment should take into account various factors, including education and training, labor market regulations, and social protection measures.

Prepivoted Augmented Dickey-Fuller Test with Bootstrap-Assisted Lag Length Selection

We investigate the application of prepivoting in conjunction with lag length selection to correct the size and power performance of the Augmented Dickey-Fuller test for a unit root. The bootstrap methodology used to perform the prepivoting is a residual based AR bootstrap that ensures that bootstrap replicate time series are created under the null irrespective of whether the originally observed series obeys the null hypothesis or not. Simulation studies wherein we examine the performance of our proposed method are given; we evaluate our method’s performance on ARMA(1,1) models with varying configurations for size and power performance. We also propose a novel data dependent lag selection technique that uses bootstrap data under the null to select an optimal lag length; the performance of our method is compared to existing lag length selection criteria.

The Impact of Real Exchange Rate and Gross Domestic Product on Trade Balance of Lao-Thailand

The objectives of this study are to analyze the effect of the real exchange rate of kip to baht and gross domestic product on the trade balance between Lao-Thai in both short-run and long-run effects. The time series data from 1990-2021 was usedand implied with the ARDL model. The stationary of each variable was tested by Augmented Dickey Fuller (ADF) method. The empirical analysis by the ARDL model show that there is a long-run relationship. In the long run, the real exchange rate and the gross domestic product of Thailand have a negative relationship with the trade balance at a statistical significance level of 0.01. Meanwhile, the gross domestic product of the Lao PDR has a positive relationship. In the short term, only the gross domestic product of Lao PDR has a negative relationship with the trade balance at a significance level of 0.05. The gross domestic product of Thailand has the most effect on the trade balance, followed by the gross domestic product of the Lao PDR and the real exchange rate of kip per baht has the least effect. The coefficient of speed in adjusting to the equilibrium point is equal to -0.784, which means that after shock, it will be able to adjust to the long run equilibrium by 78.4%.

COMPARATIVE ANALYSIS OF TIME SERIES FORECASTING MODELS USING ARIMA AND NEURAL NETWORK AUTOREGRESSION METHODS

Gold price fluctuations have a significant impact because gold is a haven asset. When financial markets are volatile, investors tend to turn to safer instruments such as gold, so gold price forecasting becomes important in economic uncertainty. The novelty of this research is the comparative analysis of time series forecasting models using ARIMA and the NNAR methods to predict gold price movements specifically applied to gold price data with non-stationary and non-linear characteristics. The aim is to identify the strengths and limitations of ARIMA and NNAR on such data. ARIMA can only be applied to time series data that are already stationary or have been converted to stationary form through differentiation. However, ARIMA may struggle to capture complex non-linear patterns in non-stationary data. Instead, NNAR can handle non-stationary data more effectively by modeling the complex non-linear relationships between input and output variables. In the NNAR model, the lag values of the time series are used as input variables for the neural network. The dataset used is the closing price of gold with 1449 periods from January 2, 2018, to October 5, 2023. The augmented Dickey-Fuller test dataset obtained a p-value = 0.6746, meaning the data is not stationary. The ARIMA(1, 1, 1) model was selected as the gold price forecasting model and outperformed other candidate ARIMA models based on parameter identification and model diagnosis tests. Model performance is evaluated based on the RMSE and MAE values. In this study, the ARIMA(1, 1, 1) model obtained RMSE = 16.20431 and MAE = 11.13958. The NNAR(1, 10) model produces RMSE = 16.10002 and MAE = 11.09360. Based on the RMSE and MAE values, the NNAR(1, 10) model produces better accuracy than the ARIMA(1, 1, 1) model.

Fault Detection in Industrial Equipment through Analysis of Time Series Stationarity

Predictive maintenance has gained importance due to industrialization. Harnessing advanced technologies like sensors and data analytics enables proactive interventions, preventing unplanned downtime, reducing costs, and enhancing workplace safety. They play a crucial role in optimizing industrial operations, ensuring the efficiency, reliability, and longevity of equipment, which have become increasingly vital in the context of industrialization. The analysis of time series’ stationarity is a powerful and agnostic approach to studying variations and trends that may indicate imminent failures in equipment, thus contributing to the effectiveness of predictive maintenance in industrial environments. The present paper explores the use of the Augmented Dickey–Fuller p-value temporal variation as a possible method for determining trends in sensor time series and thus anticipating possible failures of a wood chip pump in the paper industry.

Macroeconomic Drivers of Inflation in Ethiopia: Evidence from ARDL Modelling

Purpose: This study identifies the macroeconomic determinants of inflation in Ethiopia from 2012 to 2023, a period marked by reforms and liberalization efforts. Despite these measures, inflation remains a critical challenge impacting Ethiopia's economic stability.Method: Utilizing the ARDL model, this study examines the relationship between inflation and various macroeconomic indicators. Data are drawn from sources such as the National Bank of Ethiopia (NBE), Ethiopian Economic Association (EEA), International Monetary Fund (IMF), and World Bank (WB). The Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests are employed to assess data stationarity.Findings: Results indicate that money supply growth, import-to-GDP ratio, budget deficit, and public expenditure have a significant positive effect on inflation in both the long and short term. Conversely, lending interest rates exhibit inflationary effects in the short term but are deflationary in the long term. Additionally, external debt-to-GDP ratio, official exchange rate, and real GDP growth rate demonstrate negative long-term impacts on inflation.Novelty: This research uniquely integrates key macroeconomic variables, analyzing their divergent impacts on inflation across short- and long-term frameworks in Ethiopia's context.Implications: The findings underscore the importance of balanced fiscal and monetary policies to curb inflation, offering critical insights for Ethiopian policymakers aiming to foster economic stability.

Performance Analysis and Prediction of 5G Round-Trip Time Based on the VMD-LSTM Method

With the increasing level of industrial informatization, massive industrial data require real-time and high-fidelity wireless transmission. Although some industrial wireless network protocols have been designed over the last few decades, most of them have limited coverage and narrow bandwidth. They cannot always ensure the certainty of information transmission, making it especially difficult to meet the requirements of low latency in industrial manufacturing fields. The 5G technology is characterized by a high transmission rate and low latency; therefore, it has good prospects in industrial applications. To apply 5G technology to factory environments with low latency requirements for data transmission, in this study, we analyze the statistical performance of the round-trip time (RTT) in a 5G-R15 communication system. The results indicate that the average value of 5G RTT is about 11 ms, which is less than the 25 ms of WIA-FA. We then consider 5G RTT data as a group of time series, utilizing the augmented Dickey–Fuller (ADF) test method to analyze the stability of the RTT data. We conclude that the RTT data are non-stationary. Therefore, firstly, the original 5G RTT series are subjected to first-order differencing to obtain differential sequences with stronger stationarity. Then, a time series analysis-based variational mode decomposition–long short-term memory (VMD-LSTM) method is proposed to separately predict each differential sequence. Finally, the predicted results are subjected to inverse difference to obtain the predicted value of 5G RTT, and a predictive error of 4.481% indicates that the method performs better than LSTM and other methods. The prediction results could be used to evaluate network performance based on business requirements, reduce the impact of instruction packet loss, and improve the robustness of control algorithms. The proposed early warning accuracy metrics for control issues can also be used to indicate when to retrain the model and to indicate the setting of the control cycle. The field of industrial control, especially in the manufacturing industry, which requires low latency, will benefit from this analysis. It should be noted that the above analysis and prediction methods are also applicable to the R16 and R17 versions.

Forecasting Non-stationary Time Series Using Deep Learning in a Fuzzy Time Series Framework and its Application to Stock Markets

Non-stationary time series prediction is challenging due to its dynamic and complex nature. Fuzzy time series models offer a promising solution for forecasting such data, but a key challenge lies in partitioning the universe of discourse, which significantly impacts forecasting accuracy. Traditional fuzzy time series models often use equal-length interval partitioning, which is more suited for stationary data and limits their adaptability to non-stationary time series. This paper introduces a novel variable-length interval partitioning method designed specifically for non-stationary time series. The developed method combines a Long Short-Term Memory (LSTM) Autoencoder with K-means clustering, enabling dynamic, data-driven partitioning that adapts to the changing characteristics of the data. The LSTM Autoencoder encodes the time series, which is clustered using K-means, and intervals are defined based on cluster centers. Furthermore, the Variable Length Interval Partitioning-based Fuzzy Time Series model (VLIFTS) is developed by incorporating this partitioning method and the concepts of Markov chain and transition probability matrix. In this model, fuzzy sets are viewed as states of a Markov chain, and transition probabilities are used in the forecasting phase. The model is validated on stock market indices Nifty 50, NASDAQ, S&P 500, and Dow Jones. Stationarity and heteroscedasticity are tested using Augmented Dickey-Fuller (ADF) and Levene's tests respectively. Statistical forecast accuracy metrics Root Mean Squared Error (RMSE) and Mean Absolute Percent Error (MAPE) show that VLIFTS significantly improves forecasting accuracy over traditional models. This hybrid approach enhances fuzzy time series modelling and can be applied to various non-stationary time series forecasting problems.

HUMAN CAPITAL AND PROFITABILITY: CASE of TURKISH BANKING SECTOR

This study aims to examine the effect of human capital on profitability of Turkish banking sector by a panel data covering 2009.q1-2022.q4. Throughout this aim, a research model based on a dependent variable as return on equity is set up. The independent variable to be focused is the Human Capital Efficiency Coefficient -mostly considered as the most significant sub-coefficient of the Value-Added Intellectual Coefficient Methodology developed by Ante Pulic (2004)- to measure intellectual capital efficiency. Besides, three control variables are included in the model to proxy balance sheet structure, assets quality and liquidity. Levin-Lin-Chu (LLC), Im-Pesaran-Shin (IPS) and Augmented Dickey Fuller (ADF) Fisher panel unit root tests, and Pedroni (1999) panel co-integration test are conducted in the econometric analyses to test the stationarity and co-integration among the series. Long term co-integration coefficients are estimated by Fully Modified Ordinary Least Squares (FMOLS) methodology proposed by Pedroni (2000). The empirical findings indicate the existence of a statistically significant and positive relationship between human capital and profitability; and statistically significant and negative relationships among human capital and assets quality, and human capital and liquidity. Another finding is that there exists no statistically significant relationship between human capital and balance sheet structure.Levin-Lin-Chu (LLC), Im-Pesaran-Shin (IPS) and Augmented Dickey Fuller (ADF) Fisher panel unit root tests, and Pedroni (1999) panel co-integration test are conducted in the econometric analyses to test the stationarity and co-integration among the series. Long term co-integration coefficients are estimated by Fully Modified Ordinary Least Squares (FMOLS) methodology proposed by Pedroni (2000). The empirical findings indicate the existence of a statistically significant and positive relationship between human capital and profitability; and statistically significant and negative relationships among human capital and assets quality, and human capital and liquidity. Another finding is that there exists no statistically significant relationship between human capital and balance sheet structure.

The Relationship Between Sectoral Employment and Real Sector Economic Confidence: Fourier Causation Analysis

Purpose –In order to ensure economic growth in the short term and economic development in the long term, it is important to evaluate the events occurring in the political and economic order correctly and to meet the expectations. This study was created to evaluate the effectiveness of economic confidence on the relevant sectors on a sectoral basisDesign/Methodology/Approach –In this study, the relationship between the real sector economic confidence index and employment rates in industry, construction and services sectors will be analyzed. Turkey is selected as the sample and the data range subject to the analysis is determined as 2009:01-2023:08. In the study, the Fourier analysis method, which is among the new generation analysis methods, was applied, and Fractional Frequency Fourier Augmented Dickey Fuller Unit Root Test and Fourier Toda-Yamamoto Causality Test were applied.Findings –As a result of the application, a one-way causality relationship from the real sector economic confidence index to building and industry sector employment was determined. A recommendation should be made regarding the necessity of making political arrangements that will maintain the stability level of employment in the relevant sector.Discussion –In line with the data obtained in this study, the fact that real economic confidence has an effect on employment in the construction and industrial sectors, in addition to the fact that sectoral employment has no effect on the real economic confidence index, can be linked to the fact that sectoral leaders pursue a policy of contraction or expansion within the framework of expectations

FORECASTING CBOE OPENING VALUES: AN EFFECTIVE ARIMA APPROACH WITH TRANSFORMATION AND MODEL DIAGNOSTICS

This study investigates the use of an ARIMA model, coupled with Monte Carlo simulation, to forecast the opening value of a Volatility Index (VIX) time series. The data obtained from the Chicago Board Options Exchange (CBOE) for the years 1992–2019 have been transformed into stationary data using a detrend method and first-order difference. The Augmented Dickey-Fuller (ADF) test is used to ensure the data are adequately transformed. The autocorrelation function (ACF) and partial ACF (PACF) are then used to identify series with serial correlation and determine whether an autoregressive (AR) model is appropriate. Significant moving average (MA) lags are also determined for model identification. The Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) are used to find the best-fit ARIMA model. Among the evaluated ARIMA models, which ranged from a random walk ARIMA(0, 1, 0) to ARIMA(2, 1, 2), the ARIMA(2, 1, 2) model was found to be the most optimal, exhibiting the lowest AIC and BIC values. This model was then used to forecast the opening value for the year 2014, using 2013 data as the real data. The generated ARIMA(2, 1, 2) model demonstrates reasonable alignment with the actual 2014 data, suggesting its potential for forecasting in this context. Additionally, Monte Carlo simulations are employed to assess the model’s robustness by generating a range of potential outcomes, providing a more comprehensive understanding of the uncertainty associated with the forecasts.

IMPACT OF RENEWABLE AND NON-RENEWABLE ENERGY ON FOREIGN DIRECT INVESTMENT AND ECONOMIC GROWTH

This study examines the impact of renewable and non-renewable energy on foreign direct investment (FDI) and economic growth in Nigeria from 1980 to 2017. The objectives are to assess how renewable energy influences economic growth, FDI, and the interplay between renewable and non-renewable energy. Using techniques such as the Augmented Dickey-Fuller test, Co-integration, and the Auto Regressive Distributed Lag (ARDL) model, secondary data from the World Bank were analyzed, focusing on GDP, renewable energy, non-renewable energy, and FDI. The findings indicate that GDP positively influences renewable energy in the long run, while non-renewable energy has a negative effect. In the short run, non-renewable energy and GDP significantly impact renewable energy, while FDI shows no effect. Recommendations include fostering a stable political environment to attract FDI and providing incentives like subsidies and tax holidays to promote trade and investment in renewable energy. Effective macroeconomic management is essential for translating economic growth into increased renewable energy consumption in Nigeria. KEYWORDS: Renewable Energy, Non-Renewable Energy, Foreign Direct Investment and Economic Growth.

Long-term casual analysis of the energy-food price relationship

The energy price index is a key economic measure that tracks changes in the prices of energy commodities, such as petroleum, electricity, and gas. This study aims to explore how the energy price index influences the food price index, as both have significant impacts on the economy. The relationship between energy and food prices is complex and affected by various factors. The novelty of this research lies in identifying the time period during which increases in energy prices impact food prices due to inflation. A statistical approach is applied to investigate this effect, using data from Pakistan's energy and food price indices for the period between January 2019 and May 2023. The Augmented Dickey-Fuller (ADF) test is employed to assess whether the time series is stationary, followed by the Granger causality test to determine if the energy price index can be used to predict changes in the food price index. The Engle-Granger cointegration test is used to identify long-term relationships between non-stationary time series. Additionally, various lag tests are conducted to determine the minimum time period within which changes in energy prices influence food prices. This research has practical implications for policymakers. Government agencies can use the findings to predict potential changes in food prices, and the study may also be relevant to the United Nations' Sustainable Development Goals (SDGs), as shifts in food prices could directly or indirectly affect several SDGs.

RELATIONSHIP BETWEEN LOGISTICS SECTOR AND ECONOMIC GROWTH IN ASEAN COUNTRIES

In the modern economics, any manufacturing companies have increasingly relied on professional logistics firms, leading to rapid growth in the logistics sector as a service industry and its significant contribution to economic development. With increasing globalization and competition, countries have focused on enhancing their logistics systems to ensure stable economic growth and strengthen global trade competitiveness. In the ASEAN region, logistics plays a critical role, facilitating cross-border trade and regional connectivity. This study aims to investigate the relationship between the logistics sector, specifically air freight transportation, and economic growth in ASEAN countries, given the sector's central role in supporting supply chain management and trade activities. The primary objective of this study is to determine whether there is a causal relationship between air freight transportation (AFT), a key logistics activity, and gross domestic product (GDP) growth in six ASEAN countries. The study seeks to provide insights into how investment in logistics infrastructure, particularly in air freight, may influence economic growth in the region. The study uses panel data for six ASEAN countries from 1996 to 2021. Data on GDP growth and air freight transportation were sourced from the World Bank’s World Development Indicators database. A series of statistical tests, including the Cross-Sectional Augmented Dickey Fuller (CADF) test, Pesaran’s slope homogeneity test, and the Bootstrap Granger causality test, were performed to examine cross-sectional dependence and the causal relationship between GDP growth and air freight transportation. Descriptive statistics revealed significant disparities in air freight transportation capacity among the countries studied, with Indonesia and Thailand showing substantial air freight volumes compared to the Philippines. However, the results of the panel causality tests, including the Dumitrescu and Hurlin panel causality test, indicate that no causal relationship exists between air freight transportation and GDP growth in the six ASEAN countries.

Predicting NEPSE Index: An ARIMA-Based Model

Purpose: This research aims to employ the Auto Regressive Integrated Moving Average (ARIMA) approach to forecast the Nepal Stock Exchange (NEPSE) index.Methods: Daily univariate time series data were taken from the NEPSE website and other sources for 27 years for a total 6143 daily time series data starting in 1997. The presence of unit roots was identified through an Augmented Dickey-Fuller (ADF) test, which ensures stationarity in data. Thereafter, estimation and evaluation of AR (autoregressive), MA (moving average), and ARIMA models were carried out.Results: The study reveals that the ARIMA (2,1,1) model is well fitted in predicting the NEPSE index, indicating its ability to capture temporal dependence and trends that exist within the data.Conclusion: It concludes the temporal dynamics of series highlighting both the short-term impacts of past shocks and longer-term adjustment towards equilibrium. The research work supports the efficacy of model in capturing and predicting the behaviour of NEPSE index, thereby adding in informed decision making and future projections based on proposed statistical foundations.

Imports, Exports and Foreign Reserves in Nigeria: Exploring the Long and Short-Run Relationships from the Vector Error Correction Model (VECM) Approach

The imports of goods and services, a component of international trade, can boost economic progress when carried out on productive commodities. However, excessive imports of goods and services also stifle the growth of indigenous industries. Understanding the short- and long-run relationships between imports and other macroeconomic factors is necessary for planning by managers of the economy. This study examined the relationship between imports, exports, and foreign reserves in Nigeria for 10 years spanning from 2012 to 2021. Monthly time series data were tested for stationarity using the augmented Dickey-Fuller test, which showed that all the variables were integrated at order I (1). The Johansen co-integration test showed a stable long-run relationship between imports, exports, and foreign reserves. The regression estimates from the vector error correction model showed a long-run causal relationship (negative) between imports, exports, and foreign reserves. Furthermore, the foreign reserve has a significant short-run causal relationship with imports. Exports did not show any short-run causal relationship with imports. Both imports and foreign reserves do not have any causal relationship with exports in the short run. However, an insignificant causal relationship exists in the short run between exports and foreign reserves, while imports do not have any causal relationship with foreign reserves. The study recommends an optimum level of foreign reserve, which can be used to check the tendency of excessive importation, which is detrimental to the growth of local industries.

The Determinants of Industrial Output in Pakistan: A Time Series Analysis

This study investigates the determinants of industrial growth in Pakistan through an empirical analysis of quarterly data spanning from 1970 to 2022. Industrial growth is the dependent variable, while the inflation rate, rate of exchange, foreign direct investment, gross fixed capital formation, and terms of trade are independent variables. The Autoregressive Distributed Lag (ARDL) model was employed due to the mixed stationarity, as indicated by the augmented dickey-fuller test. The results reveal a positive long-run relationship between FDI and industrial growth, as well as between gross fixed capital formation and industrial growth. Conversely, a negative relationship is observed between the exchange rate and industrial growth, and between the interest rate and industrial growth. All variables to be statistically significant, except for terms of trade. Furthermore, the inflation rate and rate of exchange show a reducing impact on industrial growth, while FDI and gross fixed capital formation contribute positively to industrial growth.

Vertical price transmission in agricultural markets in Ghana

The purpose of this study was to investigate the extent of price transmission along the agri-food supply chain in selected regions in Ghana. The extent of transmission was used to assess the efficiency and effectiveness of the agricultural marketing systems in the area. Efficient marketing systems are expected to reduce the issue of regional price fluctuations, encourage fair income distribution throughout the supply chain, and improve food security. The preliminary time series stationarity/unit root and seasonality tests were performed using the Augmented Dickey-Fuller test (ADF)/Kwiatkowski, Phillips, Schmidt, and Shin (KPSS) and Canova-Hansen seasonal stability tests respectively. Following the detection of statistically significant threshold effects by the Supremum Likelihood Ratio (Sup-LR) tests, the extent of price transmission was investigated using two-region threshold vector error correction models. The initial tests found that the monthly wholesale, retail, and producer price series for maize, cowpea, and yam used in the study were first difference stationary rather than seasonally integrated. The findings revealed that price signals in the agri-food markets were not fully transmitted across the supply chain. Thus, the extent of price transmission was generally low, creating unexploited arbitrage opportunities along the chain. The study identified high transfer costs (proxied by the threshold parameter) and actor-specific market power (as revealed by the Granger causality test) as the primary obstacles to effective market functioning in the area. Minimizing transfer costs through infrastructural development such as improvements in road networks linking farming communities and markets, access to price information, and regular stakeholder engagements could help improve the functioning of the agri-food markets.