Forecasting the Fisheries Production in Pakistan for the Year 2017-2026, using Box-Jenkin’s Methodology

Mehmood, aisar; Sial, Maqbool H.; Sharif, Saira; Hussain, Abid; Riaz, Muhammad Waheed; Shaheen, N.

doi:10.17582/journal.pjar/2020/33.1.140.145

Cited by 16 publications

(19 citation statements)

References 14 publications

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“…Though, if researchers desire to create prediction intervals in forecasting, then the prediction breaks require forecast errors that would be uncorrelated and normally distributed with constant variance and mean zero. The data series should be transformed into stationary before applying MA, AR, and ARIMA or Box Jenkins approaches (Mehmood et al, 2020;Kitworawut & Rungreunganun, 2019). Since we used time-series data for this research, thus, Philips-Perron (1988), Augmented Dickey-Fuller (1979, 1981 for the stationarity, and Box Jenkins or ARIMA approach, and Auto-Regressive (AR) with seasonal dummies have been employed for the forecasting of import and export.…”

Section: Methodsmentioning

confidence: 99%

“…The inflation of FY2019 was -7 %, the reserves were $14.477 billion for the same fiscal year, however, the trade deficit was -11.9 % as a percent of GDP was recorded for the FY2019 1 . Foreign remittances are a significant part of Pakistan's GDP, and it had a 9.7 % growth in the fiscal year 2019 (Mehmood et al, 2020). The balance of payment and trade deficit has always been a burning question for Pakistan's economy and also hampered other macroeconomic indicators.…”

Section: Introductionmentioning

confidence: 99%

“…Though the imports have been continuously rising, the total imports for the FY2019 were $55,169 million. Thus, it is more than double the exports, which shows the vulnerability of trade deficit and balance of payment (Mehmood et al, 2020). According to Azgun and Servinc (2010), Chen (2009), Dokovic, Muncan, and Paunovic (2019), Kogid et al (2011), Alam, Uddin, and Taufique (2009), and Sharma (2019), the exports and imports have exerted a significant and sustainable influence on economic growth of different economies of the world.…”

Section: Introductionmentioning

confidence: 99%

“…In all cases, developing an alternative instrument for short-run export and import forecasting is justified by the need for providing better analysis and forming more accurate expectations about the economic development of the country. Bridging the gap between exports and imports is a challenge to the national trade policymakers of any country (Mehmood et al, 2020). Thus, the econometric time-series modeling is an essential tool to forecast the sustainable exports and imports of the country.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Forecasting Exports and Imports by using Autoregressive (AR) with Seasonal Dummies and Box-Jenkins Approaches: A Case of Pakistan

Ghauri

Ahmed

Štreimikienė

et al. 2020

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This research aims to evaluate two econometric models to forecast imports and exports for the financial year (FY) 2020. For this purpose, we used the annual exports and imports data of Pakistan from FY2002 to FY2019. Thus, in this regard, we employed, and compared the results of two econometrics models such as Box Jenkins or Autoregressive Integrated Moving Average (ARIMA), and Auto-Regressive (AR) with seasonal dummies. For examining the precision of forecasting, we employed mean absolute error and root mean square error approaches. The findings of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) reveal that the ARIMA or Box Jenkins approach provides better accuracy of the forecast for the exports as compared to the AR model with dummies. However, Auto-Regressive (AR) model has demonstrated more precision for the imports as compared to the Box Jenkins model. Hence, the projected forecasting for the growth of export is 1.87% for the FY2020 and projected forecasting for the import demonstrates a negative variation of -1.61% for the FY2020. The findings of the undertaken study recommend the policymakers of Pakistan to take corrective measures to increase exports and to prevent the country from the trade deficit. The policymakers of Pakistan should give more incentives to the exporters and decrease the cost of doing business to be more competitive than the regional economies such as India, Bangladesh, and China.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Forecasting Exports and Imports by using Autoregressive (AR) with Seasonal Dummies and Box-Jenkins Approaches: A Case of Pakistan

Ghauri

Ahmed

Štreimikienė

et al. 2020

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“…Compound growth rate (CGR) has been widely used to estimate growth trends in the fisheries sector (Jeyanthi and Nikita, 2012;Debroy et al, 2016 andSharma, 2017). Previously attempts have been made to forecast fish production using ARIMA models (Paul and Das 2010;Anuja et al, 2017;Kehinde and Joseph 2018;Mah et al, 2018;Mehmood et al, 2020). ARI-MA models were also used to obtain a seasonal forecast of fish production in Odisha (Raman et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Growth trends and forecasting of fish production in Assam, India using ARIMA model

Yadav

Das

et al. 2020

JANS

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Fish is an essential component of the diet of the most populace in Assam and fish farming has been one of the sources of livelihood in rural areas. Assam ranks first in fish production among North-eastern states of India. However, fish production is not sufficient to meet the demand despite having vast aquatic resources in the state. The present study was undertaken to determine the decadal growth of fish production in the state using the compound growth rate. The study also attempted modeling and forecasting of fish production in Assam using Auto-Regressive Integrated Moving Average (ARIMA) methodology. For the present study, time-series data on fish production in Assam from 1980-81 to 2018-19 was obtained from the Directorate of Fisheries, Government of Assam. Data for the period 1980-81 to 2014-15 was utilized to build an ARIMA model and validated through the remaining data from 2015-16 to 2018-19. The best suitable model for the state’s fish production was ARIMA (1,1,0) based on the values of the model selection criterion. The actual fish production and forecast values using a fitted model were in close agreement. The out-of-sample forecast values of fish production in the state for the subsequent years 2019-20 to 2022-23 showed an increasing trend from 336.97 to 358.21 thousand metric tonnes. Considering the vast aquatic resources in the state, the study calls for serious attention by policymakers, researchers, and developmental agencies for harnessing the potential of fisheries resources for making the North-east region self-sufficient in fish production as a whole and Assam in particular.

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Exploring association and forecasting of evapotranspiration based on meteorological factors over megacity Lahore (Pakistan) and central place of Indo-Gangetic Basin

Waris,

Sarif,

Batool

2023

Environ Dev Sustain

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Forecasting the Fisheries Production in Pakistan for the Year 2017-2026, using Box-Jenkin’s Methodology

Cited by 16 publications

References 14 publications

Forecasting Exports and Imports by using Autoregressive (AR) with Seasonal Dummies and Box-Jenkins Approaches: A Case of Pakistan

Forecasting Exports and Imports by using Autoregressive (AR) with Seasonal Dummies and Box-Jenkins Approaches: A Case of Pakistan

Growth trends and forecasting of fish production in Assam, India using ARIMA model

Exploring association and forecasting of evapotranspiration based on meteorological factors over megacity Lahore (Pakistan) and central place of Indo-Gangetic Basin

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