scholarly journals Forecasting of Unemployment and Economic Growth for Turkey: ARIMA Model Application

2021 ◽  
Author(s):  
Uğur AYIK ◽  
Gökhan ERKAL
Keyword(s):  
Economic Growth ◽  
Arima Model ◽  
Model Application

scholarly journals ECONOMIC GROWTH PROJECTION FOR EUROPEAN UNION BY 2030

2019 ◽  
pp. 61-69
Author(s):  
Pawel Mlodkowski
Keyword(s):  
Economic Growth ◽  
Production Function ◽  
Capital Investment ◽  
Historical Perspective ◽  
Arima Model ◽  
Private Capital ◽  
Real Output ◽  
Estimated Parameters ◽  
Growth Projection ◽  
The Eu

The paper contributes to a discussion on developments in output for the EU-27 group over the next 11 years, up to year 2030. It departs from a discussion on arguments of the production function, with focus on sudden changes to population in Europe, its growth rate and composition. A brief study of population-decreasing events in the European historical perspective may represent an inspiring part. Reasons for inconsistency in estimated parameters of production function for European countries seems to be well-explained this way. The projection for the next 11 years, up to 2030 has employed the production function framework. Parameters have been estimated on the period 2004 – 2016 that matches most closely conditions that one may expect over the projection horizon. Feeding the estimated production function for the 2018-2030 forecast has employed projected population by Eurostat, while private capital investment has been generated by an ARIMA model. Projection is offered in two forms: (1) aggregated real output for the whole EU, and (2) the same category for each of the EU-27 countries.

scholarly journals Projections of Regional Macroeconomic Conditions using the Univariate Forecasting Method

2020 ◽  
Vol 8 (3) ◽  
pp. 329-342
Author(s):  
Inayati Nuraini Dwiputri ◽  
Muhammad Syam Kusufi ◽  
Albertus Girik Allo
Keyword(s):  
Economic Growth ◽  
Income Distribution ◽  
Unemployment Rate ◽  
Arima Model ◽  
Exponential Smoothing ◽  
Jel Classification ◽  
Macroeconomic Conditions ◽  
Macroeconomic Indicators ◽  
The Government ◽  
Regional Product

The prediction of future macroeconomic conditions is needed by the government to carry out the planning and budgeting. This study predicts macro indicators in Hulu Sungai Utara Regency in the period 2017-2022. The method used is univariateforecasting, which includes the ARIMA model, exponential smoothing, and exponential smoothing with trend adjustment. The macroeconomic indicators used in this study are real Gross Domestic Regional Product (GDRP), economic growth, unemployment rate, and income distribution. The results of the analysis show that Brown's forecasting model is predicted that the real GDRP value tends to increase, forecasting results using a simple model on economic growth and the ARIMA (0.0,0) model on the unemployment rate, had predicted tends to be constant. And, the prediction of income distribution with the Holt model tends to increase. Keywords: macroeconomic, univariate, forecasting, ARIMA, exponential smoothing JEL Classification: E0, O1, C0

scholarly journals A Forecasting Model for Economic Growth and CO2 Emission Based on Industry 4.0 Political Policy under the Government Power: Adapting a Second-Order Autoregressive-SEM

2019 ◽  
Vol 5 (3) ◽  
pp. 69 ◽  
Author(s):  
Sutthichaimethee ◽  
Chatchorfa ◽  
Suyaprom
Keyword(s):  
Economic Growth ◽  
Carbon Dioxide Emissions ◽  
Industry 4.0 ◽  
Goodness Of Fit ◽  
Arima Model ◽  
Second Order ◽  
Forecasting Model ◽  
Political Policy ◽  
The Government

This research aims to forecast future economic and environmental growth for the next 16 years (2020–2035) according to the government’s strategic framework by applying the second order autoregressive-structural equation model (second order autoregressive-SEM). The model is validated by various measures, fits with the best model standards, meets all criteria of the goodness of fit, and is absent from any issues of heteroskedasticity, multicollinearity, autocorrelation, and non-normality. The proposed model is very distinct from other alternatives in that it produces the optimal outcome. Its mean absolute percentage error (MAPE) is 1.02% while the root mean square error (RMSE) is 1.51%. A comparison of the above results is carried out to compare the same values from other models, namely the regression linear model (ML model), back propagation neural network (BP model), artificial neural natural model (ANN model), gray model, and the autoregressive integrated moving average model (ARIMA model). The second order autoregressive-SEM is a model that is appropriate for long-term forecasting (2020–2035), and accounts for the specifics of the Thai government strategy set under the Industry 4.0 policy framework. The results of the long-term analysis indicate that the current political policy will result in continuous economic growth, where the gross national product (GNP) growth rate will climb up to 6.45% per annum by 2035, while the environment is being negatively affected. The study predicts that CO2 emissions will rise up to 97.52 Mt CO2 Eq. (2035). The forecasting model also reflects that the economy factor has an adjustment ability to equilibrium stronger than that of the environment factor; further, it shows that the relationship between the factors is causal. In addition, the political policy , economy , and environment factors are found to have both direct and indirect effects. As to the results, this study illustrates that the Industry 4.0 policy is still inefficient, as the carbon dioxide emissions are projected to be higher than the threshold for environment hazards and disasters which set to the limit of 80 Mt CO2 Eq. by 2035. The effect of such policy will put the environment at risk, and the government must take immediate action to respond to this urgency. Thus, the second order autoregressive-SEM model remains a significant model embedded with the adjustment ability to equilibrium and the applicability for various contexts in different sectors. This introduced model is a vital tool for assisting the national government to create policy that is effective and sustainable, and lead to positive development of the nation. This second order autoregressive-SEM model can be used as a resource for the management of both public policy and private enterprise.

scholarly journals Pendekatan Univariate Time Series Modelling untuk Prediksi Kuartalan Pertumbuhan Ekonomi Indonesia Pasca Vaksinasi COVID-19

2022 ◽  
Vol 4 (1) ◽  
pp. 86-103
Author(s):  
Asrirawan Asrirawan ◽  
Sri Utami Permata ◽  
Muhammad Ilham Fauzan
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Linear Models ◽  
Arima Model ◽  
Training Data ◽  
Series Data ◽  
Growth Data ◽  
Dynamic Linear Models ◽  
Univariate Time Series ◽  
Time Series Modelling

The development of COVID-19 has had a significant negative impact on Indonesia’s economic growth based on the indicator of the value of the quarterly year of year data in 2020 and 2021. Economic growth is still experiencing a recession per first quarter with a percentage of - 2.19 percent at the beginning of 2021. The government has to take vaccination measures for the community gradually with the aim of reducing the number of sufferers of these cases. The purpose of this study is to predict economic growth quarterly after vaccination using 3 (three) univariate time series models, namely ARIMA, Holt-Winters and Dynamic Linear models for policymaking. Holt-Winters and Dynamic Linear models make it possible to handle time-series data containing trends and seasonality. The data is divided into training data and test data obtained from the ministry of finance and the Indonesian Central Statistics Agency (BPS). The goodness of the model uses MSE, MAE and U-Theil criteria. Based on the results of the analysis using the R library, the results show that the best modelling for economic growth data is the ARIMA model with the lowest MSE, MAE and U-Theil values with the difference between the models being 0.000242. The ARIMA model looks better than other models because the economic growth data only contains trends and assumes a seasonal element in the data. In addition, the Holt-Winters and Dynamic Linear models produce a forecast for Indonesia’s economic growth to still experience a recession (negative growth) in the next four quarterly data, while the ARIMA model produces a positive growth forecast in the fourth quarter.

Empirical Analysis of Environmental Innovation and Economic Growth in Western Region

2011 ◽  
Vol 71-78 ◽  
pp. 1741-1744
Author(s):  
Jing Feng Zhao
Keyword(s):  
Economic Growth ◽  
Granger Causality ◽  
Development Strategy ◽  
Empirical Analysis ◽  
Arima Model ◽  
Unit Root Test ◽  
Spillover Effects ◽  
Western Region ◽  
Environmental Innovation ◽  
Western Area

This article chooses the evaluation index from The Report of Sustainable Development Strategy of China and quantifies the environmental innovation level. We exploit the data of western region from the year 1995 to 2010 and forecast the regional environmental innovation level in 2011 and 2012 through ARIMA model. The result of prediction indicates the tendency of stable increase of this level. We then use unit root test, co-integration analysis and Granger Causality to test the relationship between the environmental innovation and economic growth. The result shows the long-term equilibrium relationship between them; environmental innovation has positive external spillover effects on economic growth and they follow the Granger Causality. The results of empirical analysis comply with the situation of societal and economic development of western area. Finally, the relevant policy recommendations are proposed.

scholarly journals THE ECONOMIC GROWTH FORECAST AND A REVIEW OF THE IMPACTS OF COVID-19 ON THE GHANAIAN ECONOMY: APPLICATION OF TIME SERIES ANALYSIS AND MONTE-CARLO SIMULATION

2020 ◽  
pp. 83-95
Author(s):  
Professor Li Fang Lin ◽  
Blessed Kwasi Adjei ◽  
Felix Kwame Nyarko
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Time Series Analysis ◽  
Arima Model ◽  
Information Criterion ◽  
Growth Trend ◽  
Series Analysis ◽  
The Government

This manuscript explores the effects of Covid-19 pandemic on the economic activities of Ghana by first modelling the Economic growth figures of Ghana; discuss the current covid-19 situation and its economic impact on the nation and to wrap things up by suggesting remedial measures necessary to salvage the situation at hand. To model and forecast the Economic growth trend, the times series analysis and the Monte Carlo simulation (Laplace distribution) techniques were employed. The success of the ARIMA model was monitored through Akaike information Criterion (AIC) where irrefutably the absolute number shows the success of the model - the lower the number, the better the model. The research results showed that in spite of promising economic forecasts, with the force of the pandemic soaring universally, there is no doubt that the economic prosperity of Ghana will be disrupted and major revenue margins shrinked this year. However, due to some solid and harsh measures set out by the government we are optimistic that situations will be well contained and managed. The scientific contribution of the research lies in the fact that it will offer a new way of perceiving risks and uncertainties when policy makers are drafting budgets and economic policies going forward. In that capacity, they will not only adapt to practical and analytical methods to forecast but additionally consider some unforeseen circumstances beyond the control of humanity that may have tormenting impact on economic outputs. KEYWORDS: Time series analysis, Covid-19, Monte-Carlo simulation, GDP per Capita, Modelling, Economic Growth.

scholarly journals Predicting Index Price Based on the COVID-19 Cases and Deaths

2021 ◽  
Vol 50 (9) ◽  
pp. 2833-2846
Author(s):  
Nur Sabrina Razali ◽  
R. Nur-Firyal
Keyword(s):  
Economic Growth ◽  
Financial Market ◽  
Descriptive Analysis ◽  
Arima Model ◽  
World Health ◽  
Price Prediction ◽  
Short Period ◽  
Huge Impact ◽  
Health Organization ◽  
The Impact

COVID-19 pandemic has impacted global financial market. In this paper, we study the impact of COVID-19 pandemic on four countries indexes which are United Kingdom, United States, Japan and Malaysia to see the effect of the spread of the virus on economy. Based on descriptive analysis, most index market suffer for a short period of time after the World Health Organization (WHO) declared COVID-19 as a pandemic on 11 March 2020. However, most markets manage to get back on track after a few months. We want to see the effect of number of COVID-19 cases and deaths on the index price because we believe that they will impact the economic growth of most countries. This will indirectly impact the countries index market as most businesses could not operate in full scale. Moreover, an increase in number of cases, most countries had to implement a partial or total lockdown which then impact the economic growth. Based on our studies, we conclude that the number of COVID-19 cases and deaths did have an impact on the four countries index price. Prediction analysis shows that the time series linear model can predict index price better than ARIMA model that relies on historical data. As of right now, COVID-19 does have a huge impact on the countries financial market and economic growth.

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