scholarly journals Multivariate Time Series Modelling of Nigerian Gross Domestic Product (GDP) and Some Macroeconomic Variables

2021 ◽  
Vol 4 (3) ◽  
pp. 12-31
Author(s):  
Anthony U. ◽  
Emediong U.
Keyword(s):  
Time Series ◽  
Gross Domestic Product ◽  
Crude Oil ◽  
Multivariate Time Series ◽  
Domestic Product ◽  
Economic Sectors ◽  
Vector Autoregressive ◽  
Macroeconomic Variables ◽  
Distributed Lag ◽  
Time Series Modelling

This paper focused on modelling Nigeria’s Gross Domestic Product and some macroeconomic variables, which include, Agriculture, Crude Oil/Mineral Gas and Telecommunication using different classes of multivariate time series models. Multi-Dependent Linear Regression Model (MLRM), Vector Autoregressive Model (VARM) and Multivariate Autoregressive Distributed Lag Models (MARDLM) have been fitted to the multivariate time series. The basic statistics of the estimates and errors reveal the competitiveness of VARM and MARDLM. This was also evidently using the model selection criteria. But the mean square error of forecast places VARM on a higher comparative advantage than MARDLM. The results of the Granger causality tests showed that Crude Oil/Mineral Gas granger causes Gross Domestic Product and also granger causes Agriculture, but not vice versa in each case. This paper establishes the fact that Crude Oil/Mineral Gas is a good predictor of Gross Domestic Product and Agriculture as a major contributor to the nation’s economic development. The need to consistently juxtapose causal relationships between major economic sectors and Gross Domestic Product is vehemently advocated for proper evaluation of sectorial contributions and formulation of economic driven policy in the country.

scholarly journals VECTOR AUTOREGRESSIVE APPROACH AFTER FIRST DIFFERENCING: A TIME SERIES ANALYSIS OF INFLATION AND ITS DETERMINANTS IN TANZANIA

2021 ◽  
pp. 43-56
Author(s):  
Rachel R. Cheti ◽  
Bahati Ilembo
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Gross Domestic Product ◽  
Money Supply ◽  
Inflation Rate ◽  
Negative Impact ◽  
Causal Effect ◽  
Domestic Product ◽  
Series Data ◽  
Vector Autoregressive

The objective of the study was to examine the trend of inflation and its key determinants in Tanzania. We used secondary time series data observed annually from January 1970 to 2020 which are inflation rate, GDP, Exchange rate and money supply. The vector autoregressive (VAR) model was employed for modeling. Augmented Dickey-Fuller test (ADF) found that inflation rate, Gross Domestic Product (GDP), exchange rate and Money supply (M3) were initially non-stationary but they became stationary after first differencing so as to proceed with the analysis. Preliminary tests before obtaining vector auto regressive model were carried out before determining the relationship between the variables. Diagnostic test such as serial correlation, heteroscedasticity, stability and normality were also important to evaluate the model assumptions and investigate whether or not there are observations with a large, undue influence on the analysis. We used Granger causality test (GCT) to determine causal- effect relationship between the variables. The results show that, there is a long run relationship between the variables, also the results showed that exchange rate and money supply (M3) both have a positive impact on inflation rate while gross domestic product (GDP) revealed a negative impact on inflation rate. Finally, the forecast of inflation rate for 15 years ahead was performed. The study recommends that the government should pursue both contractionary monetary policy and fiscal policy in order to control inflation in the country.

scholarly journals The Economy between Fluctuations in Oil Revenues and Diversifying Sources of Income in Arab Countries

2021 ◽  
pp. 96-110
Author(s):  
Yun D Starchenko
Keyword(s):  
Gross Domestic Product ◽  
Crude Oil ◽  
Research Problem ◽  
Arab Countries ◽  
Domestic Product ◽  
Economic Sectors ◽  
Macroeconomic Policies ◽  
Economic Trends ◽  
Oil Revenues ◽  
The Impact

The Economic Monitor offers an overview of key economic trends and policies over the preceding six months and discusses conclusions from recent World Bank work on Iraq, putting them in a longer-term and global sense and evaluating the impact of these developments and other policy adjustments Iraq's outlook. The macroeconomy, capital markets, and indices of human health and growth are all included. It is aimed at a broad range of people, like policymakers, industry executives, stock market players, and Iraq's analysts and practitioners. The research problem was represented by the fact that the Iraqi economy is single-source due to its dependence on the crude oil sector, which constitutes more than (60%) of the gross domestic product. Crude oil revenues constitute more than (90%) and neglect other economic sectors such as agriculture, industry, and tourism, whose percentage did not exceed (30%) of the gross domestic product. The weakness of non-oil exports in the foreign trade sector is the failure of macroeconomic policies to diversify the Iraqi economy. The research aims to achieve many goals, the most important of which are: identifying the concept and indicators of diversification. As well as an analysis of the Iraqi economic structure during the period (2008-2019). A forward-looking vision for economic diversification in Iraq.

scholarly journals An analysis of the relationship of inflation and unemployment to the gross domestic product (gdp) in zimbabwe

2014 ◽  
Vol 1 (3) ◽  
pp. 156-162
Author(s):  
Tendai Makoni
Keyword(s):  
Time Series ◽  
Gross Domestic Product ◽  
Cointegration Test ◽  
Domestic Product ◽  
Long Run ◽  
Causality Tests ◽  
Granger Causality Tests ◽  
Relationship Of ◽  
The Relationship ◽  
Yearly Data

The time series yearly data for Gross Domestic Product (GDP), inflation and unemployment from 1980 to 2012 was used in the study. First difference of the logged data became stationary as suggested by the time series plots. Johansen Maximum Likelihood Cointegration test indicated a long-run relationship among the variables. Granger Causality tests suggested unidirectional causality between inflation and GDP, implying that GDP is Granger caused by inflation in Zimbabwe. Another unidirectional causality was noted between unemployment and inflation. The causality between unemployment and inflation imply that unemployment do affect GDP indirectly since unemployment influences inflation which in turn positively affect GDP.

scholarly journals Impact of Crude Prices on Retail Prices – A Time Series Analysis

2019 ◽  
Vol 8 (2S7) ◽  
pp. 606-612
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Crude Oil ◽  
Energy Demand ◽  
Multivariate Time Series ◽  
Secondary Data ◽  
Series Analysis ◽  
Long Run ◽  
The Uk ◽  
The Impact

The UK has emerged as one of the largest producers of petroleum in the world. A significant amount of petroleum is used for fulfilling the energy demand within the country. However, the country witnessed a different trend from 2015. This is mainly due to the increase in imports of petroleum in order to meet domestic needs. To this, there is a need to identify the impact of changes exist in petrol and crude oil prices in the UK. In this context, the researcher has undertaken primary research to derive conclusions which are case specific and can comply with the research aim. The study used secondary data for the year 2015-2018 and conducted multivariate time series analysis. A series of tests including unit root, ARIMA, and co-integration tests were used to derive the results. The study found that there was an asymmetric relationship between the movements of prices of crude oil with respect to retail fuel prices in the long run. However, the study is not without limitations which are represented at the end of the study following with its future scope

scholarly journals Dampak Pembiayaan BUS dan UUS Terhadap Produk Domestik Bruto Indonesia Berdasarkan Lapangan Usaha

2021 ◽  
Vol 1 (3) ◽  
pp. 555-571
Author(s):  
Aida Azmi Nabila ◽  
Endang Hatma Juniwati ◽  
Fifi Afiyanti Tripuspitorini
Keyword(s):  
Economic Growth ◽  
Economic Development ◽  
Gross Domestic Product ◽  
Model Test ◽  
Domestic Product ◽  
Islamic Banking ◽  
Economic Sectors

Islamic banking has a role to encourage economic development and enhance economic growth. One way to do this is by allocating Islamic banking financing funds to all economic sectors or industrials in Indonesia. There is a mismatch between the growth statistics of financing distribution to Gross Domestic Product based on industrials consisting of seven industrial. This istudy iaims ito idetermine iwhether ior inot ithere iis ia  relationship, iconstribution, and the effect iof ifinancing ichanneled on Indonesia's Gross Domestic Product. The isample iin ithis istudy was determined using ipurposive isampling. iThis iresearch imethod iis ia idescriptive imethod iwith ia iquantitative iapproach. iThe iresults iof  the model test of the effect of BUS and UUS financing on Indonesia’s Gross Dometic Product based on the industrial in 2012-2019 show that not all financing has a relationship, constribution, and the effect to Indonesia’s Gross Domestic Product based on the industrial.

Multiscaled Neural Autoregressive Distributed Lag: A New Empirical Mode Decomposition Model for Nonlinear Time Series Forecasting

2020 ◽  
Vol 30 (08) ◽  
pp. 2050039 ◽  
Author(s):  
Foued Saâdaoui ◽  
Othman Ben Messaoud
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Empirical Mode Decomposition ◽  
Multivariate Time Series ◽  
Seasonal Effects ◽  
Real World Data ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Mode Decomposition ◽  
Nonlinear Trends

Forecasting has always been the cornerstone of machine learning and statistics. Despite the great evolution of the time series theory, forecasters are still in the hunt for better models to make more accurate decisions. The huge advances in neural networks over the last years has led to the emergence of a new generation of effective models replacing classic econometric models. It is in this direction that we propose, in this paper, a new multiscaled Feedforward Neural Network (FNN), with the aim of forecasting multivariate time series. This new model, called Empirical Mode Decomposition (EMD)-based Neural ARDL, is inspired from the well-known Autoregressive Distributed Lag (ARDL) model being our proposal founded upon the concepts of nonlinearity, EMD-multiresolution and neural networks. These features give the model the ability to effectively capture many nonlinear patterns like the ones often present in econophysical time series, such as nonlinear trends, seasonal effects, long-range dependency, etc. The proposed algorithm can be summarized into the following four basic tasks: (i) EMD breaking-down multivariate time series into different resolution levels, (ii) feeding EMD components from the same levels into a number of feedforward neural ARDL models, (iii) from one level to the next, extrapolating the component corresponding to the response variable (scalar output) a number of steps ahead, and finally, (iv) recombining level-by-level forecasts into a single output. An optimal learning scheme is rigorously designed for efficiently training the new proposed architecture. The approach is finally tested and compared to a number of powerful benchmark models, where experiments are conducted on real-world data.

scholarly journals Agricultural exports and the growth of agriculture in India

2013 ◽  
Vol 59 (No. 5) ◽  
pp. 211-218 ◽  
Author(s):  
O. Ramphul
Keyword(s):  
Gross Domestic Product ◽  
Causal Link ◽  
Domestic Product ◽  
Bounds Testing ◽  
Causality Test ◽  
Vector Error Correction ◽  
Agricultural Exports ◽  
Distributed Lag ◽  
Long Run ◽  
Equilibrium Relationship

The study empirically investigates the causality between agricultural exports and gross domestic product (GDP) agriculture in India using the Granger causality test via Vector Error-Correction Model over the period 1970–1971 to 2009–2010. The results of unit-root tests suggest that the series of India’s GDP agriculture and farm exports are integrated of order one. The results of the Auto Regressive Distributed Lag bounds testing approach to co-integration show that there is a positive and stable long-run equilibrium relationship between India’s agricultural exports and GDP of agriculture. We find a unidirectional causal link running from farm exports to gross domestic product of agriculture. It indicates that in India, agricultural products export Granger causes the growth in GDP of agriculture, which supports the export-led growth hypothesis. It is suggested that in order to accelerate the agricultural growth rate in India, there is a need to implement the policies encouraging the agricultural exports.  

The relationship between energy consumption and gross domestic product in Hong Kong (1992–2015): Evidence from sectoral analysis and implications on future energy policy

2019 ◽  
Vol 31 (2) ◽  
pp. 215-236
Author(s):  
Ruixiaoxiao Zhang ◽  
Geoffrey QP Shen ◽  
Meng Ni ◽  
Johnny Wong
Keyword(s):  
Time Series ◽  
Energy Consumption ◽  
Gross Domestic Product ◽  
Causal Relationship ◽  
Granger Causality ◽  
Value Added ◽  
Granger Causality Test ◽  
Domestic Product ◽  
Causality Test ◽  
Per Capita

The causal relationship between energy consumption and gross domestic product in Hong Kong from 1992 to 2015 is investigated in this study. Different from the previous studies focusing on the causal relationship between total energy consumption and total gross domestic product per capita, this study further investigates the causal relationship from sectoral perspective, including residential, commercial, industrial and transportation sectors. For each sector, the time series data of sectoral energy consumption and sectoral per capita value added are collected. To conduct the Granger causality test, the unit root test is first applied to analyse the stationarity of time series. The cointegration test is then employed to examine whether causal relationship exists in long-term. Finally, based on the aforementioned tests, both vector error correction model and vector autoregression model can be selected to determine the Granger causality between time series. It is interesting to find that the sectoral energy consumption and corresponding sectoral per capita value-added exhibit quite different causal relationships. For both residential sector and commercial sectors, a unidirectional causal relationship is found running from the sectoral per capita value added to sectoral energy consumption. Oppositely, for industrial sector and transportation sector, a unidirectional causal relationship is found running from sectoral energy consumption to sectoral per capita value added. Regarding the Granger causality test results, the indicative suggestions on energy conservation policies, energy efficiency policies and greenhouse gas emission reduction policies are discussed based on the background of Hong Kong’s economic structure and fuel types.

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