scholarly journals Climatic Changes Effects on Agricultural Production: An Assessment in Case of Pakistan

2020 ◽  
Vol V (III) ◽  
pp. 11-21
Author(s):  
Muhammad Shahid ◽  
M. Niamatullah ◽  
Bibi Aisha Sadiqa
Keyword(s):  
Agricultural Production ◽  
Time Series Data ◽  
Residential Building ◽  
Agricultural Economics ◽  
Series Data ◽  
Climatic Variations ◽  
Lag Model ◽  
Auto Regressive ◽  
Gas Fuel ◽  
Different Sources

The emission of carbon from electricity, gas fuel consumption, residential building and commercial buildings has raised the temperature of the region, affecting agricultural production. The agaric sector is sensitive to change in temperature. This paper examines the climatic variations due to CO2 emission from different sources is inclined to the yield of production by taking time series data over the period from 1984 -2015. The Auto-Regressive Distributive lag model was applied to estimate the results. Moreover, diagnostic test, stability test and error correction mechanism were operated on the dataset, reveals the rising temperature, pollution and emission from residential affects the agaric production negatively I case of Pakistan. I have reviewed the literature on this topic, but a very limited portion of the literature discusses this issue. Hence this paper will contribute a lot in agricultural economics fields.

scholarly journals Consequences of Capital formation, Trade Liberalization on the Economic Growth: Pakistan’s Experience

2015 ◽  
Vol 2 (1) ◽  
pp. 1-4
Author(s):  
Nadia Bukhari ◽  
Anjum Iqbal
Keyword(s):  
Economic Growth ◽  
Time Series Data ◽  
Trade Openness ◽  
Capital Formation ◽  
Vital Role ◽  
Series Data ◽  
Distributed Lag ◽  
Long Run ◽  
Auto Regressive ◽  
Adf Test

This study considers the long run relationship between the liberalization of trade, capital formation and the economic growth of Pakistan by using the time series data from 1975-2013. The main aim of this study is to examine that how much liberalization of trade and capital formation affects the economic growth of Pakistan in long run. The approach that has been used for empirical analysis is Auto Regressive Distributed Lag (ARDL) model. Under the ADF test capital formation (CF) is stationary at its first level but the trade openness (TO) and GDP is stationary at its first difference. Moreover, the granger casualty test is evident that there become a casual relationship between the trade openness and GDP. The result of this study shows that both the trade openness and the capital formation determined the economic growth in long run and they both have statistically significant effect on the GDP. Furthermore it has has been depicted from the study that the trade has a vital role to influence the economic growth.

Trend and Pattern of Cereal Crops: Production, Productivity and Area Coverage in Nepal

Patan Pragya ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. 188-195
Author(s):  
Bhaba Datta Sapkota
Keyword(s):  
Agricultural Production ◽  
Food Production ◽  
Time Series Data ◽  
Area Coverage ◽  
Series Data ◽  
Cereal Crops ◽  
Sources Of Information ◽  
Secondary Sources ◽  
Rugged Topography ◽  
Future Plans

This study conducted to examine growth trends and pattern in area, production and productivity of major cereal crops over the six fiscal years. Time series data of cereal crops coverage of annual yield were used for the study. This study was based on descriptive nature and used secondary sources of information on production, productivity and area coverage of major cereals (Paddy, Maize, Wheat, Millet, Buckwheat and Barley) covering the six fiscal years (2011/12-20116/17) data. Trends of crop's productivity were analyzed using graphical methods. Ratio and percentage were used to measure productivity (yield) growth rate of the selected crops of study. The yielding trend of cereal production is not satisfactory in Nepalese economy. Pattern of agricultural production in Nepal is affected by multiple factors including rugged topography, monsoon, insignificant investment in infrastructure, and research and development. Production and productivity would be helpful to develop the future plans and take the appropriate decisions to uphold the situation for the sustainability in food production.

scholarly journals Sectoral Investment and Employment Generation in Pakistan: An Econometric Analysis

2018 ◽  
Vol 4 (2) ◽  
pp. 261-270
Author(s):  
Furrukh Bashir ◽  
Hafeez ur Rehman ◽  
Rashid Ahmad ◽  
Ismat Nasim
Keyword(s):  
Time Series Data ◽  
Trade Openness ◽  
Percent Level ◽  
Series Data ◽  
Distributed Lag ◽  
Distributed Lag Model ◽  
Long Run ◽  
Augmented Dickey Fuller ◽  
Lag Model ◽  
Employment Generation

This study is projected at investigating the influence of Sectoral Investment on Employment Generation. For this purpose, time series data is collected from Pakistan over the period from 1972 to 2017. Augmented Dickey fuller test reveals that few variables considered in the study are stationary at level and few at first difference. So, econometric results are estimated using autoregressive and distributed lag model for long run elasticities. Long run co-integrating relationship is established at 2.5 percent level using ARDL bound testing approach. ARDL long run results concludes that Agricultural Investment, Industrial Investment, Services Sector Investment and Trade openness are increasing employment while inflation and tax revenue are seemed to be negatively related with employment of Pakistan in the long run.

scholarly journals PENDUGAAN PARAMETER MODEl DISTRIBUTED LAG POLA POLINOMIAL MENGGUNAKAN METODE ALMON

2021 ◽  
Vol 15 (4) ◽  
pp. 761-772
Author(s):  
Fitria Virgantari ◽  
Wilda Rahayu
Keyword(s):  
Regression Model ◽  
Time Series Data ◽  
Operating Cost ◽  
Series Data ◽  
Operating Income ◽  
Distributed Lag ◽  
Distributed Lag Model ◽  
Lag Model ◽  
Forecast Time ◽  
The Impact

The distributed lag model is a regression  model that describes the relationship between the dependent variable of a given period and the independent variables of a certain or previous periods. The model can be used to determine the impact of the independent variable to dependent variables over time and forecast time series data for the next periods. There are two forms of distributed lag model that have been widely proposed in the estimation of distributed lag regression model. The first form  is proposed by Koyck and the second form by Almon. This paper aims to apply the Almon model to examine the effect of  the ratio of BOPO (Operating Cost and Operating Income) to the ROA (Return on Asset) of a government bank based on quarterly data, to estimate its parameters, to examine the feasibility of the model, and to predict the next quarter.  Results shows that distributed lag model is  = 10.110 - 0.078  + 0.015  + 0.026  – 0.045  with Yt is ROA, and Xt is the ratio BOPO  on the 1st quarter until the previous 3 quarters. The model is quite good according to the determination coefficient that is 0.75, no autocorrelation in the model, t test and F test are also significant. Based on the model, the value of ROA ratio next quarter predicted 4.63%. The decrease in profitability ROA ratio is due to an increase in interest expense while interest income can not compensate

scholarly journals Climate Change Driven Food Inflation in Ethiopia?

2018 ◽  
Vol 8 (1) ◽  
pp. 13-22
Author(s):  
Berhe Gebregewergs Hagos
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Average Price ◽  
Ceteris Paribus ◽  
Distributed Lag ◽  
Distributed Lag Model ◽  
Lag Model ◽  
Food Inflation

The research dealt with the relationships between temperature variability and price of food stuffs in Tigrai using 84 months collected time series data thereby applied a Univariate econometric tool and finite Distributed Lag Model in defining the variables and outcome of the study. As a result, the econometric regression analysis witnessed that a 1oC temperature rise contributed the average price of food stuffs such as barley price rose up by 80 percent, maize 186 percent, sorghum close to 275 percent, wheat 60 percent, and 170 percent in white Teff over the years, ceteris paribus.

scholarly journals Does government spending reduce poverty in Nigeria? Evidence from Auto-Regressive Distributed Lag Specification.

2020 ◽  
Vol 7 (2) ◽  
pp. 86
Author(s):  
Olufemi Samuel Adegboyo
Keyword(s):  
Government Spending ◽  
Time Series Data ◽  
Capital Expenditure ◽  
Transportation Cost ◽  
Series Data ◽  
Distributed Lag ◽  
Auto Regressive ◽  
The Government ◽  
Transfer Capital ◽  
The Impact

This paper analyses the impact of government spending on poverty reducing in Nigeria for the period 1981 to 2017 making use of annual time series data. The study employs the Auto-Regressive Distributed Lag (ARDL) approach. The result of the study revealed that economic service recurrent expenditure (ESRX), social and community recurrent expenditure (SCSRX), Transfer recurrent expenditure (TRX) reduces poverty while transfer capital expenditure (TCX) and administrative recurrent expenditure (ADRX) escalate poverty. Consequently, the study recommends that Government should embark on provision of food subsidies, subsidies farm input for farmers, subsidies transportation cost. Furthermore, government should endeavor to pay pensioners all their entitlements including gratuities as at when due without any delay, government should also be giving stipend to the unemployed and disabled, more poverty alleviating programs should be organize Also, the huge cost of maintaining the government should be reduced by reducing the numbers of political appointees to a reasonable size.

scholarly journals Time series data analysis and ARIMA modeling to forecast the short-term trajectory of the acceleration of fatalities in Brazil caused by the corona virus (COVID-19)

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11748
Author(s):  
Akini James ◽  
Vrijesh Tripathi
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Explanatory Variable ◽  
Arima Model ◽  
Series Data ◽  
Arima Models ◽  
Composite Function ◽  
Short Run ◽  
Lag Model ◽  
Time Series Data Analysis

Objective This paper incorporates the concept of acceleration to fatalities caused by the coronavirus in Brazil from time series data beginning on 17th March 2020 (the day of the first death) to 3rd February 2021 to explain the trajectory of the fatalities for the next six months using confirmed infections as the explanatory variable. Methods Acceleration of the cases of confirmed infection and fatalities were calculated by using the concept of derivatives. Acceleration of fatality function was then determined from multivariate linear function and calculus chain rule for composite function with confirmed infections as an explanatory variable. Different ARIMA models were fitted for each acceleration of fatality function: the de-seasonalized Auto ARIMA Model, the adjusted lag model, and the auto ARIMA model with seasonality. The ARIMA models were validated. The most realistic models were selected for each function for forecasting. Finally, the short run six-month forecast was conducted on the trajectory of the acceleration of fatalities for all the selected best ARIMA models. Results It was found that the best ARIMA model for the acceleration functions were the seasonalized models. All functions suggest a general decrease in fatalities and the pace at which this change occurs will eventually slow down over the next six months. Conclusion The decreasing fatalities over the next six-month period takes into consideration the direct impact of the confirmed infections. There is an early increase in acceleration for the forecast period, which suggests an increase in daily fatalities. The acceleration eventually reduces over the six-month period which shows that fatalities will eventually decrease. This gives health officials an idea on how the fatalities will be affected in the future as the trajectory of confirmed COVID-19 infections change.

scholarly journals External Debt and Agricultural Production in Nigeria

2019 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Wilson Ebhotemhen ◽  
David Umoru
Keyword(s):  
Agricultural Production ◽  
Time Series Data ◽  
Negative Impact ◽  
External Debt ◽  
Debt Management ◽  
Series Data ◽  
Inverse Association ◽  
Yield Increase ◽  
Test Instrument ◽  
Foreign Loans

Over time, Nigeria has experienced upsurge in external debt stock and several studies have estimated negative impact of growing external debt on economic growth. On sectoral basis therefore, our study proceeds to evaluate such impact of external debt on growth of agricultural production in Nigeria using time series data for the period of study 1980 to 2017. For this purpose, we utilized co-integration being test instrument and ECM to investigate link among variables used. The empirical results reveal that external debt failed to yield increase in output returns in agricultural productivity by its inverse association with agricultural output. This indicates that acquired external loans for agriculture within period of study were not optimally utilized for same resolve. Hence, we advise that Nigerian government should demonstrate sound commitment to effective debt management in order to ensure foreign loans are suitably channeled and healthily utilized for purpose acquired for as this would equally guarantee that the output returns would be sufficient for debt service obligation and balance to promote growth in other sectors of Nigerian economy.

scholarly journals White Noise Tests on the LSTM Model Trained with Double Pendulum

2021 ◽  
Vol 2137 (1) ◽  
pp. 012032
Author(s):  
Xisen Wang
Keyword(s):  
Time Series ◽  
White Noise ◽  
Time Series Data ◽  
Initial Conditions ◽  
Correlation Coefficients ◽  
Series Data ◽  
Double Pendulum ◽  
Auto Correlation ◽  
State Dependent ◽  
Auto Regressive

Abstract This paper describes the intrinsic qualities of a simple double pendulum (DP), with a visual representation, a rigorous deduction of the Lagrangian equation, and a concrete factor analysis. LSTM model was utilized to simulate the double pendulum’s periodic and chaotic behaviors and evaluates the effectiveness of the model. The auto-correlation coefficients was calculated. Meanwhile, Box-Pierce test and Ljung-Box tests for various state-dependent time series were conducted to give various initial conditions to explore the DP system’s random characteristics. The research results are as follows: 1) Chaos did not lead to direct randomness; 2) seasonality could coexist with chaos; 3) the highly auto-regressive nature of DP’s time series data are found. Therefore, it can be concluded that the chaos in a double pendulum has particular patterns (such as the positive relationship with the likelihood of being a random white noise series) that could be further explored.

scholarly journals PENGARUH INFLASI DAN EKSPOR MINYAK KELAPA SAWIT TERHADAP PERTUMBUHAN EKONOMI DI INDONESIA MENGGUNAKAN MODEL DINAMIS REGRESSION

2019 ◽  
Vol 8 (1) ◽  
pp. 8
Author(s):  
Isfihani Isfihani ◽  
Devi Andriyani
Keyword(s):  
Economic Growth ◽  
Palm Oil ◽  
Time Series Data ◽  
Series Data ◽  
Analysis Tool ◽  
Ardl Approach ◽  
Auto Regressive ◽  
The Stability ◽  
Term Analysis

This study aims to determine the effect of inflation and export of palm oil on the economic growth in Indonesia in the short and long term. The data used is the time series data from 1988 to 2016. The data analysis method used is the Auto Regressive Distributed Lagged (ARDL) approach and the analysis tool with the help of Eviews 9. The results of the study show that all stationary variables at the level of first difference and have long-term cointegration. Partially, in the short term analysis shows that inflation has a negative and significant effect on the economic growth, and exports of palm oil have a positive and insignificant effect on the value of economic growth in Indonesia. In the long-term analysis of inflation and export of palm oil has a negative and significant effect on the economic growth in Indonesia. The results of the stability model test show that the model used is stable. The result of the determination coefficient R 2 is 87.40 percent

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