ABDULLAH MOHAMMAD GHAZI AL KHATIB
Verified @hotmail.com
Damascus University- Faculty of Economics- Department of Banking and Insurance
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Pradeep Mishra, Abdullah Mohammad Ghazi Al Khatib, Shikha Yadav, Soumik Ray, Achal Lama, Binita Kumari, Divya Sharma, and Ramesh Yadav
Springer Science and Business Media LLC
Shikha Yadav, Abdullah Mohammad Ghazi Al khatib, Bayan Mohamad Alshaib, Sushmita Ranjan, Binita Kumari, Naief Alabed Alkader, Pradeep Mishra, and Promil Kapoor
Springer Science and Business Media LLC
Pradeep Mishra, Abdullah Mohammad Ghazi Al khatib, Bayan Mohamad Alshaib, Binita Kuamri, Shiwani Tiwari, Aditya Pratap Singh, Shikha Yadav, Divya Sharma, and Prity Kumari
Springer Science and Business Media LLC
Abul Abrar Masrur Ahmed, Nadjem Bailek, Laith Abualigah, Kada Bouchouicha, Alban Kuriqi, Alireza Sharifi, Pooya Sareh, Abdullah Mohammad Ghazi Al khatib, Pradeep Mishra, Ilhami Colak,et al.
Elsevier BV
Bayan Mohamad Alshaib, Abdullah Mohammad Ghazi Al khatib, Alina Cristina Nuta, Mohamad Hamra, Pradeep Mishra, Rajani Gautam, Sarfraz Hussain, and Cristina Gabriela Zamfir
SAGE Publications
This study examines the association between fiscal sustainability indicators and Egypt’s economic growth from 1980 to 2018. Fiscal sustainability refers to a government’s ability to generate sufficient revenue to cover its costs and debt obligations in the long run without excessive borrowing or money creation. Egypt’s economic growth has slowed, raising questions about fiscal sustainability. This study aimed to analyze the dynamic relationship between fiscal sustainability indicators (government revenue, expenditure, external debt) and economic growth in Egypt. The autoregressive distributed lag (ARDL) bounds testing approach and unrestricted error correction model were applied to annual data from 1980 to 2018. A dynamic link was found between fiscal sustainability indicators and economic growth. Government expenditure and external debt significantly impacted economic expansion in the long term, while government revenue did not. Fiscal sustainability, measured by growth in total government expenses, external debt obligations, and revenue, significantly influences Egypt’s economic growth. Prudent fiscal management is crucial for sustained economic development. Policymakers should focus on controlling government spending, limiting external debt, and improving revenue generation to promote long-term economic growth in Egypt. Fiscal sustainability must balance critical investments in public services. Carefully managing fiscal deficits is key to unleashing Egypt’s economic potential. This study provides valuable insights into the connection between fiscal policy and economic growth in Egypt, informing policymakers’ decisions.
Abdullah Mohammad Ghazi Al khatib, Bayan Mohamad Alshaib, and Ali Mohamad Kanaan
SAGE Publications
This study examines the complex interaction between Financial Development (FD) and Economic Growth (EG) in Algeria from 1980 to 2020 using nonlinear modeling techniques. We apply the Non-Linear Causality test with Transfer Entropy, a novel method in this literature, to confirm the nonlinear causality from FD to EG. We also use the Nonlinear Autoregressive Distributed Lag (NARDL) model and the Cumulative Dynamic Multiplier (CDM) to establish the long-run equilibrium and the short-run dynamic relationship between FD and EG. The NARDL results support the Transfer Entropy findings and show that FD has a symmetric impact on EG in both the short and long term. Positive and negative shocks on FD affect EG similarly and the imbalance is corrected in about 6 years. This study provides useful information for policymakers and stakeholders to design policies that promote (EG) and development in Algeria by enhancing FD and mitigating shocks. JEL Codes: C45, C32, E44, O16, O53
Abdullah Mohammad Ghazi Al khatib
Elsevier BV
Pradeep Mishra, Abdullah Mohammad Ghazi Al Khatib, Priyanka Lal, Ayesha Anwar, Korakot Nganvongpanit, Mostafa Abotaleb, Soumik Ray, and Veerasak Punyapornwithaya
Springer Science and Business Media LLC
Soumik Ray, Pradeep Mishra, Hicham Ayad, Prity Kumari, Rajnee Sharma, Binita Kumari, Abdullah Mohammad Ghazi Al Khatib, Anant Tamang, and Tufleuddin Biswas
Walter de Gruyter GmbH
Abstract Forecasting is valuable to countries because it enables them to make informed business decisions and develop data-driven strategies. Fruit production offers promising economic opportunities to reduce rural poverty and unemployment in developing countries and is a crucial component of farm diversification strategies. After vegetables, fruits are the most affordable source of essential vitamins and minerals for human health. India's fruit production strategies should be developed based on accurate predictions and the best forecasting models. This study focused on the forecasting behavior of production of apples, bananas, grapes, mangoes, guavas, and pineapples in India using data from 1961 to 2015 (modelling set) and 2016–2020 (predicting set). Two unit root tests were used, the Ng–Perron (2001) test, and the Dickey–Fuller test with bootstrapping critical values depending on the Park (2003) technique. The results show that all variables are stationary at first differences. Autoregressive integrated moving average (ARIMA) and exponential smoothing (ETS) models were used and compared based on goodness of fit. The results indicated that the ETS model was the best in all the cases, as the predictions using ETS had the smallest errors and deviations between forecasting and actual values. This result was confirmed using three tests: Diebold–Mariano, Giacomini–White, and Clark–West. According to the best models, forecasts for production during 2021–2027 were obtained. In terms of production, an increase is expected for apples, bananas, grapes, mangoes, mangosteens, guavas, and pineapples in India during this period. The current outcomes of the forecasts could enable policymakers to create an enabling environment for farmers, exporters, and other stakeholders, leading to stable markets and enhanced economic growth. Policymakers can use the insights from forecasting to design strategies that ensure a diverse and nutritious fruit supply for the population. This can include initiatives like promoting small-scale farming, improving postharvest storage and processing facilities, and establishing effective distribution networks to reach vulnerable communities.
Abdullah Mohammad Ghazi Al khatib
The Society of Economics and Development
This study investigated the relationship between financial development and inflation in Egypt from 1980-2018. The study employed various econometric techniques, such as Johansen's test for co-integration, error correction model, Granger's causality test, Toda-Yamamoto causality test, and dynamic ordinary least squares model. The findings indicated that financial development had not significantly reduced inflation in Egypt over the studied period. Additionally, inflation was an obstacle to the country's financial development. The results also suggested a long-term equilibrium relationship between financial development and inflation, which is unidirectional and runs from inflation to financial development. The results revealed that the inflation rate, economic growth, exchange rate, and trade openness jointly impact financial development in the short and long term. It implied that the past values of these variables could be used to predict the current and future values of financial development. However, it was not possible to use the financial development index to predict the inflation rate in Egypt in the short and long term.
Pradeep Mishra, Abdullah Mohammad Ghazi Al khatib, Bayan Mohamad Alshaib, Binita Kuamri, Shiwani Tiwari, Aditya Pratap Singh, Shikha Yadav, Divya Sharma, and Prity Kumari
Springer Science and Business Media LLC
Pradeep Mishra, Khder Mohammed Alakkari, Achal Lama, Soumik Ray, Monika Singh, Claris Shoko, Mostafa Abotaleb, Abdullah Mohammad Ghazi Al khatib, and Kadir Karakaya
King Mongkut's Institute of Technology Ladkrabang
Sugarcane industry is of crucial importance to the South Asian countries. These countries depend heavily on agriculture and the sugarcane industry has immense potential to contribute towards its economic development. Hence, the precise and timely forecast of sugarcane production is of concern for farmers, policy makers and other stakeholders. In this manuscript, we strived to forecast the production and growth rate of this important commodity using standard statistical approaches. The ARIMA (Auto Regressive Integrated Moving Average) and ETS (Exponential Smoothing) models were applied and compared on the basis of their forecasting efficiency for South Asia countries. This study also investigated the trends in sugarcane production in the region and studies the causes of the decline in production of sugarcane in Sri Lanka and Bangladesh. Furthermore, the expected production for following 7 years was computed using both models. In addition, we also calculated the projected growth rates of sugarcane production of South Asian countries over the years 2020-2027.
Pradeep Mishra
Agricultural Economics and Social Science Research Association (AESSRA)
There is a link between economic progress and Financial Development. In order to analyze the potential for influencing Economic Growth, this study will look at the underlying elements that drive the development of Syria’s Financial Sector. The research team is also speculating on how much Economic Growth these effects will bring. A Dynamic Linear Model that takes into account Financial Reforms and changes on the Legal System was used to analyze the Impact of Financial Development on Economic Growth between 1980 and 2018. We were able to measure many dimensions of Financial Development with the use of a new IMF Financial Development Indicator, overcoming the limits of single traditional variables that have been widely used. The ARDL Bounds Test approach, which is based on unit root tests, was used. The Error Correction Model was also applied. The country’s Financial Development had a favorable and statistically significant effect on Economic Growth in Syria in the short and long terms. A lot of factors influence Economic Growth, including the Legal System, overall Government Expenditure, and the Exchange Rate. The Supply Leading Hypothesis of Patrick (1966) was realized in Syria,hence Financial Development leads to Economic Growth, consistent with the proposal of “more Finance, more Growth” (Levine, 2003). Financial Development is a necessary condition and prerequisite for Economic Growth in Syria, which is consistent with the (Finance Lead Growth Theory). The model could be very useful in decision-making, especially those related to reform policies to promote the SDGs or to modify current policies in response to a possible global financial crisis or shock.
Abdullah Mohammad Ghazi Al khatib
Pakistan Agricultural Scientists Forum
Sourakanti Sarkar
Agricultural Economics and Social Science Research Association (AESSRA)
An attempt has been made to study the behavioural pattern of prices and market arrivals for rice by generating a monthly seasonal index of market arrivals and prices for the crop in five selected markets, namely Bardhaman, Siliguri, Contai, Chakdah, and Bishnupur of West Bengal from 2013–14 to 2018–19. It establishes that the month of February has the greatest average monthly arrival, followed by the months of March and May. It has a declining pattern up until October, after which it climbs upward and reaches its peak in February. In case of prices, long term movement over time has a significant increasing trend pattern expect Contai and Siliguri markets. The variability associated with arrival is very high may be due to the uncertain prices and seasonal nature of agricultural production system. But variability related with prices are comparatively less. All five markets have relatively little intra-year price variance. The relationship between market arrival and current price is positive for all markets except Siliguri and Bishnupur where one year lagged price is positively correlated with arrival only for Contai and Bishnupur markets.
Monika Ray, K.C. Sahoo, Mostafa Abotaleb, Soumik Ray, P.K. Sahu, Pradeep Mishra, AbdullahMohammad GhaziAlKhatib, Soumitra SankarDas, Vikas Jain, and Ritisha Balloo
India Meteorological Department
Weather factors such as temperature and humidity are indispensable for good agriculture. The best-suitable products can be selected according to the optimal of these weather factors. In this study, data on maximum temperature, minimum temperature, morning relative humidity and evening relative humidity were analyzed from 31st January 1921 to 31st December 2020 in India. The BATS and TBAT models are conducted for forecasting procedures. According to some selection criteria, the best models are specified for all weather factors. Extensive tables and graphics are presented in the study. Using the results and forecasts obtained in this study, India may consider agriculture according to the weather conditions suitable for agriculture.
Yashpal Singh Raghav, Pradeep Mishra, Khder Mohammed Alakkari, Monika Singh, Abdullah Mohammad Ghazi Al Khatib, and Ritisha Balloo
Agricultural Research Communication Center
Background: The goal of this study was to forecast pulse production in six countries: Afghanistan, Bangladesh, China, India, Nepal and Pakistan (2020-2027). In this study, time series forecasting was used. Methods: The data series were divided into training set from 1961 to 2015 for model building, testing set from 2016 to 2019 for validation and finally, after selecting the best model, forecast was used from 2020 to 2027, the models were compared. The best-fit model was chosen based on the minimum ME, RMSE, MAE, MPE, MAPE, MASE, ACF1 values on the training data set and the minimum MAPE values on the testing data set. Result: The best fitted model for India was NNAR (1,1). Similar to Afghanistan, the best fit model for forecasting was NNAR (3,2). The best fit model for forecasting in China was ARIMA (0,1,1). The best fit model for forecasting in Nepal was ARIMA (1,1,0). The best fit model for forecasting in Pakistan was ETS (A, N, N) (M, N, N). With a 15.73 per cent growth rate from 2020 to 2027, the best models predict that the production of pulses in (Afghanistan, China, India) will increase until 2027. India will continue to be the largest producer of pulses among the six countries, with production expected to reach 1088.778 thousand tons in 2027. Afghanistan and China have extreme growth rates of 25.19% and 11.95%, respectively, while the rest of the countries have relatively stable production volumes. These results may be crucial for developing an effective agriculture production policy, whether by providing forecasted production values or evaluating such policies.
Pradeep Mishra, Abdullah Mohammad Ghazi Al Khatib, Iqra Sardar, Jamal Mohammed, Kadir Karakaya, Abhiram Dash, Monika Ray, Lakshmi Narsimhaiah, and Anurag Dubey
Springer Science and Business Media LLC
Sugarcane plays an essential role in the economy of the India. During 2018, 79.9% of total sugarcane production of India was used in the manufacture of white sugar, 11.29% was used for jaggery production, and 8.80% was used as seed and feed materials. 840.16 Mt sugarcane was exported in the year 2019. Prediction of production level is basic to effective decision-making for policymakers. The objective of this study is thus to find the suitable models of forecasting for sugarcane production. India and major sugarcane producing states, namely Andhra Pradesh, Karnataka, Maharashtra, Tamil Nadu and Uttar Pradesh were selected. Sugarcane production data from 1950 to 2015 were used for training and 2016 to 2018 was used to test the model. ARIMA method was used to model the production process. Order selection was done using AIC. RMSE, MAPE and Theils’ U statistic were used to test the accuracy of the models fitted to the data. ARCH process was found for Karnataka, Tamil Nadu and Uttar Pradesh. Autocorrelation was not present in all the data series analyzed. Forecast accuracy on MAPE criteria ranged from 0.046 to 0.197 percent.
Soumik Ray, Soumitra Sankar Das, Pradeep Mishra, and Abdullah Mohammad Ghazi Al Khatib
Springer Science and Business Media LLC
This study attempted to examine the future behaviour of monthly average rainfall and temperature of South Asian countries by using the Seasonal Autoregressive Moving Average model. Mann–Kendall trend test with Sen's Slope Estimator, to find the trending behaviour of all data series. The study has also been attempted to compare the above methods with the help of actual data. The monthly average rainfall and temperature of South Asian countries except Afghanistan and Maldives viz. Bangladesh, Bhutan, India, Nepal, Pakistan, Sri Lanka data from January, 1961 to December, 2016 have been collected from World Bank Group, Climate change knowledge portal. For estimating the trending behaviour, a non-parametric model such as the Mann–Kendall test was used with Sen's slope estimation to determine the magnitude of the trend. Box–Jenkins methodology was also used to develop the model and estimate the forecasting behaviour of rainfall and temperature in South Asian countries. Forecasting is carried out for both monthly rainfall and the average temperature of all the countries using best fitted models based on the data series. The monthly data from January, 1961 to December, 2010 are considered for validation of the model can be regarded as in-sample forecast and the data from January, 2011 to December, 2016 are used as out-sample forecast. The forecasting values with 95% confidence limit from January, 2011 to December, 2021 using best-fitted models for both rainfall and temperature. We conclude that climate change occurs for both rainfall and temperature in South Asian countries from the study period. The selected model can be used for forecasting both rainfall and temperature of respective countries from January, 2011 to December, 2021. As the climatic data analysis is valuable to understand the variation of global climatic change, this study may help for future research work on rainfall and temperature data.