- Email: [email protected]
Economic growth rate management by soft computing approach
Physica A xx (xxxx) xxx–xxx
Contents lists available at ScienceDirect
Physica A journal homepage: www.elsevier.com/locate/physa
Q1
Q2
Economic growth rate management by soft computing approach Goran Maksimović a , Srđan Jović b,∗ , Radomir Jovanović a a
University of Priština, Faculty of Agriculture, Kopaonička, 38219 Lešak, Serbia
b
University of Priština, Faculty of Technical Sciences, 38220 Kosovska Mitrovica, Kneza Milosa 7, Serbia
highlights • • • •
Economic growth may be developed on the basis of combination of different factors. The influence of agriculture, manufacturing, industry and services. Gross domestic product (GDP) was used as economic growth indicator. Neuro fuzzy inference system was applied to the data.
article
info
Article history: Received 14 July 2016 Received in revised form 8 August 2016 Available online xxxx Keywords: Neuro-fuzzy Forecasting Gross domestic product GDP
abstract Economic growth rate management is very important process in order to improve the economic stability of any country. The main goal of the study was to manage the impact of agriculture, manufacturing, industry and services on the economic growth rate prediction. Soft computing methodology was used in order to select the inputs influence on the economic growth rate prediction. It is known that the economic growth may be developed on the basis of combination of different factors. Gross domestic product (GDP) was used as economic growth indicator. It was found services have the highest impact on the GDP growth rate. On the contrary, the manufacturing has the smallest impact on the GDP growth rate. © 2016 Elsevier B.V. All rights reserved.
1. Introduction Prediction of economic activity is very important for decision makers in many financial and nonfinancial firms. The growth of Gross Domestic Product (GDP) is considered an essential ingredient of a healthy economy. There are many factors, which have different influence on the economic growth. The GDP growth is influences based on the synthesis of various known or unknown and certain or uncertain factors. Mapping of the stimuli effects and the input and output estimates of many algorithms can be obtained via combinations of nonlinear functions. There is need for more advances algorithm for GDP growth rate forecasting and analysis. In paper [1] was analyzed the endogenous determination of sectors in a growing economy where it was shown that for the equilibrium to be stable and well behaved, it is required that the modern and traditional sectors should be substitutes and not complements. The impact of production complexity and its adaptability on the level of output and on its rate of economic growth was analyzed in Ref. [2] where it was confirmed that increased complexity has an ambiguous effect on the level of output, but positively impacts economic growth by enhancing human capital formation. Based on the results in
∗
Corresponding author. E-mail address: [email protected] (S. Jović).
http://dx.doi.org/10.1016/j.physa.2016.08.063 0378-4371/© 2016 Elsevier B.V. All rights reserved.
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G. Maksimović et al. / Physica A xx (xxxx) xxx–xxx
Fig. 1. Flowchart of the selection procedure.
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investigation [3] with artificial neutral network (ANN) method, a forecasting system of economic growth was proposed and software with related applications was developed. The ANN method achieved better results in performance and efficiency compared to conventional methods. In paper [4] was analyzed the performance of GDP growth and inflation forecasts for 25 transition countries between 1994 and 2007 and the empirical results were shown that there is a positive correlation between the number of forecasters covering a given country and the forecast accuracy. The GDP forecast accuracy improves with progress in transition as well as with the expansion in the information domain [5]. The relative performance of several factor models to forecast GDP growth using a large monthly dataset was analyzed in article [6]. It was found that factor models outperform significantly the univariate autoregressive model for nowcasting and one-quarter ahead forecasting while for longer forecast horizons the gains are substantially reduced. The growth of GDP is considered as a natural-growth process amenable to description by the logistic-growth equation [7]. Although the mathematical modeling of the GDP growth analyzing has been proposed in several investigations [8–10], the main aim in this study was to determine which parameters have the highest influence on the GDP growth rate prediction. For such a purpose soft computing method was used since this method does not require knowledge of internal system and could provide compact solutions. In this study neuro-fuzzy methodology [11] was applied to manage the most influential parameters for the GDP growth forecasting based on the data in different countries. As the input parameter, agriculture, manufacturing, industry and services were used. Neuro-fuzzy shows very good learning and forecasting capabilities, which makes it an efficient tool to deal with encountered uncertainties in any system [12–15].
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2. Methodology
20
2.1. Neuro-fuzzy selection procedure
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
21 22 23 24 25 26 27
In the beginning input–output samples were collected and used for the training procedure of the neuro-fuzzy technique. The main goal was to determine which input has the smallest error for the given output. The input with the smallest error has the highest influence on the output. Fuzzy inference system determines the errors by training and testing procedure. The fuzzy inference system consists of three components: (1) Rule base, (2) Database and (3) Reasoning mechanism.
30
These intelligent systems are a combination of knowledge, methods and techniques from neural networks and fuzzy logic. Fig. 1 illustrates a simple flow chart of the selection procedure. Fig. 2 shows all inputs parameters in the selection procedure. The model should select the parameter or a set of parameters which are the most influential to the output.
31
2.2. Dataset
28 29
32 33 34 35 36 37 38 39 40 41 42 43 44 45 46
In this investigation four factors are selected as the inputs. The data are taken from EUROSTAT for 28 countries in European Union. Countries may differ in their economic growth rate. The first input parameter is agriculture, which includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The second input parameter is manufacturing which refers to value added as the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The third input parameter is industry which corresponds to the value added in mining, manufacturing, construction, electricity, water, and gas. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The fourth input parameter is services, which correspond to value added in wholesale and retail trade, transport, and government, financial, professional, and personal services such as education, health care, and real estate services. Also
G. Maksimović et al. / Physica A xx (xxxx) xxx–xxx
3
Fig. 2. Input parameters for selection procedure. Table 1 Input and output parameters. Input 1 Input 2 Input 3 Input 4 Output
Agriculture, value added (% of GDP) Manufacturing, value added (% of GDP) Industry, value added (% of GDP) Services, etc., value added (% of GDP) GDP growth rate
included are imputed bank service charges, import duties, and any statistical discrepancies noted by national compilers as well as discrepancies arising from rescaling. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. Table 1 shows input and output parameters.
1 2 3 4
3. Results Neuro-fuzzy model was trained for each input separately in the beginning in order to estimate the prediction errors for the each input. The influence of the each input on the output can be determined according to the prediction errors. The input with the smallest error has the highest influence on the output. Fig. 3(a) shows the GDP growth rate prediction accuracy for each input separately. The smallest training error can be noted for input 4 (services). Therefore the input 4 has the highest influence on the GDP growth rate prediction. The input 2 (manufacturing) has the highest training error therefore the smallest influence on the output. Since training and testing errors are correlated one can combine two inputs in order to determine the most influential combination of the two inputs on the GDP growth rate prediction. Fig. 2(b) shows that the combination of agriculture and services (input 1 and input 4) forms the optimal combination for the GDP growth rate forecasting. Input–output decision surface for prediction of the GDP growth rate is shown in Fig. 4 based on the neuro-fuzzy algorithm. The figure shows the response of neuro-fuzzy model for the varying input parameters. The highest GDP growth rate can be noted for the smallest value of services and for the highest value of agriculture. 4. Conclusion Forecasting of the future GDP growth rate management is complex task due to the many factors, which influence the GDP. Therefore in this study was proposed an approach to overcome the forecasting difficulties of by selecting the most important parameters for the GDP growth rate prediction. A systematic approach was performed for the selection procedure by neuro-fuzz approach. The approach was used to eliminate the vagueness in the GDP management and to produce the best prediction conditions. The results show that the services have the most influence for the GDP growth rate forecasting. On the other hand, manufacturing has the smallest influence on the GDP growth rate forecasting.
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G. Maksimović et al. / Physica A xx (xxxx) xxx–xxx
Fig. 3. GDP growth rate prediction accuracy for: (a) single input and (b) two inputs.
5
0
–5 60 65
1 2
70 3
75
4
80 85
5 6
Fig. 4. Decision surface for agriculture and services benefit in GDP growth rate.
G. Maksimović et al. / Physica A xx (xxxx) xxx–xxx
5
References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]
Joseph Zeira, Hosny Zoabi, Economic growth and sector dynamics, Eur. Econ. Rev. 79 (2015) 1–15. Benno Ferrarini, Pasquale Scaramozzino, Production complexity, adaptability and economic growth, Struct. Change Econ. Dyn. 37 (2016) 52–61. Lihua Feng, Jianzhen Zhang, Application of artificial neural networks in tendency forecasting of economic growth, Econ. Modell. 40 (2014) 76–80. Libor Krkoska, Utku Teksoz, How reliable are forecasts of GDP growth and inflation for countries with limited coverage? Econ. Syst. 33 (2009) 376–388. Libor Krkoska, Utku Teksoz, Accuracy of GDP growth forecasts for transition countries: Ten years of forecasting assessed, Int. J. Forecast. 23 (2007) 29–45. Francisco Dias, Maximiano Pinheiro, António Rua, Forecasting Portuguese GDP with factor models: Pre- and post-crisis evidence, Econ. Modell. 44 (2015) 266–272. Theodore Modis, Long-term GDP forecasts and the prospects for growth, Technol. Forecast. Soc. Change 80 (2013) 1557–1562. George Kapetanios, Massimiliano Marcellino, Fotis Papailias, Forecasting inflation and GDP growth using heuristic optimisation of information criteria and variable reduction methods, Comput. Statist. Data Anal., http://dx.doi.org/10.1016/j.csda.2015.02.017. W. Jos Jansen, Xiaowen Jin, Jasper M. de Winter, Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts, Int. J. Forecast. 32 (2016) 411–436. Fady Barsoum, Sandra Stankiewicz, Forecasting GDP growth using mixed-frequency models with switching regimes, Int. J. Forecast. 31 (2015) 33–50. J.-S.R. Jang, ANFIS: Adaptive-network-based fuzzy inference systems, IEEE Trans. Syst. Man Cybern. 23 (1993) 665–685. A.Al. Ghandoor, M. Samhouri, Electricity consumption in the industrial sector of Jordan: Application of multivariate linear regression and adaptive neuro-fuzzy techniques, Jordan J. Mech. Ind. Eng. 3 (1) (2009) 69–76. R. Singh, A. Kianthola, T.N. Singh, Estimation of elastic constant of rocks using an ANFIS approach, Appl. Soft Comput. 12 (2012) 40–45. S. Kurnaz, O. Cetin, O. Kaynak, Adaptive neuro-fuzzy inference system based autonomous flight control of unmanned air vehicles, Expert Syst. Appl. 37 (2010) 1229–1234. L. Tian, C. Collins, Adaptive neuro-fuzzy control of a flexible manipulator, Mechatronics 15 (2005) 1305–1320.
1
2 3 4 5 6 7 8 9
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10 11 12 13 14 15 16 17
Contents lists available at ScienceDirect
Physica A journal homepage: www.elsevier.com/locate/physa
Q1
Q2
Economic growth rate management by soft computing approach Goran Maksimović a , Srđan Jović b,∗ , Radomir Jovanović a a
University of Priština, Faculty of Agriculture, Kopaonička, 38219 Lešak, Serbia
b
University of Priština, Faculty of Technical Sciences, 38220 Kosovska Mitrovica, Kneza Milosa 7, Serbia
highlights • • • •
Economic growth may be developed on the basis of combination of different factors. The influence of agriculture, manufacturing, industry and services. Gross domestic product (GDP) was used as economic growth indicator. Neuro fuzzy inference system was applied to the data.
article
info
Article history: Received 14 July 2016 Received in revised form 8 August 2016 Available online xxxx Keywords: Neuro-fuzzy Forecasting Gross domestic product GDP
abstract Economic growth rate management is very important process in order to improve the economic stability of any country. The main goal of the study was to manage the impact of agriculture, manufacturing, industry and services on the economic growth rate prediction. Soft computing methodology was used in order to select the inputs influence on the economic growth rate prediction. It is known that the economic growth may be developed on the basis of combination of different factors. Gross domestic product (GDP) was used as economic growth indicator. It was found services have the highest impact on the GDP growth rate. On the contrary, the manufacturing has the smallest impact on the GDP growth rate. © 2016 Elsevier B.V. All rights reserved.
1. Introduction Prediction of economic activity is very important for decision makers in many financial and nonfinancial firms. The growth of Gross Domestic Product (GDP) is considered an essential ingredient of a healthy economy. There are many factors, which have different influence on the economic growth. The GDP growth is influences based on the synthesis of various known or unknown and certain or uncertain factors. Mapping of the stimuli effects and the input and output estimates of many algorithms can be obtained via combinations of nonlinear functions. There is need for more advances algorithm for GDP growth rate forecasting and analysis. In paper [1] was analyzed the endogenous determination of sectors in a growing economy where it was shown that for the equilibrium to be stable and well behaved, it is required that the modern and traditional sectors should be substitutes and not complements. The impact of production complexity and its adaptability on the level of output and on its rate of economic growth was analyzed in Ref. [2] where it was confirmed that increased complexity has an ambiguous effect on the level of output, but positively impacts economic growth by enhancing human capital formation. Based on the results in
∗
Corresponding author. E-mail address: [email protected] (S. Jović).
http://dx.doi.org/10.1016/j.physa.2016.08.063 0378-4371/© 2016 Elsevier B.V. All rights reserved.
1
Q3 Q4
2 3 4 5 6 7 8 9 10 11 12
2
G. Maksimović et al. / Physica A xx (xxxx) xxx–xxx
Fig. 1. Flowchart of the selection procedure.
18
investigation [3] with artificial neutral network (ANN) method, a forecasting system of economic growth was proposed and software with related applications was developed. The ANN method achieved better results in performance and efficiency compared to conventional methods. In paper [4] was analyzed the performance of GDP growth and inflation forecasts for 25 transition countries between 1994 and 2007 and the empirical results were shown that there is a positive correlation between the number of forecasters covering a given country and the forecast accuracy. The GDP forecast accuracy improves with progress in transition as well as with the expansion in the information domain [5]. The relative performance of several factor models to forecast GDP growth using a large monthly dataset was analyzed in article [6]. It was found that factor models outperform significantly the univariate autoregressive model for nowcasting and one-quarter ahead forecasting while for longer forecast horizons the gains are substantially reduced. The growth of GDP is considered as a natural-growth process amenable to description by the logistic-growth equation [7]. Although the mathematical modeling of the GDP growth analyzing has been proposed in several investigations [8–10], the main aim in this study was to determine which parameters have the highest influence on the GDP growth rate prediction. For such a purpose soft computing method was used since this method does not require knowledge of internal system and could provide compact solutions. In this study neuro-fuzzy methodology [11] was applied to manage the most influential parameters for the GDP growth forecasting based on the data in different countries. As the input parameter, agriculture, manufacturing, industry and services were used. Neuro-fuzzy shows very good learning and forecasting capabilities, which makes it an efficient tool to deal with encountered uncertainties in any system [12–15].
19
2. Methodology
20
2.1. Neuro-fuzzy selection procedure
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
21 22 23 24 25 26 27
In the beginning input–output samples were collected and used for the training procedure of the neuro-fuzzy technique. The main goal was to determine which input has the smallest error for the given output. The input with the smallest error has the highest influence on the output. Fuzzy inference system determines the errors by training and testing procedure. The fuzzy inference system consists of three components: (1) Rule base, (2) Database and (3) Reasoning mechanism.
30
These intelligent systems are a combination of knowledge, methods and techniques from neural networks and fuzzy logic. Fig. 1 illustrates a simple flow chart of the selection procedure. Fig. 2 shows all inputs parameters in the selection procedure. The model should select the parameter or a set of parameters which are the most influential to the output.
31
2.2. Dataset
28 29
32 33 34 35 36 37 38 39 40 41 42 43 44 45 46
In this investigation four factors are selected as the inputs. The data are taken from EUROSTAT for 28 countries in European Union. Countries may differ in their economic growth rate. The first input parameter is agriculture, which includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The second input parameter is manufacturing which refers to value added as the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The third input parameter is industry which corresponds to the value added in mining, manufacturing, construction, electricity, water, and gas. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The fourth input parameter is services, which correspond to value added in wholesale and retail trade, transport, and government, financial, professional, and personal services such as education, health care, and real estate services. Also
G. Maksimović et al. / Physica A xx (xxxx) xxx–xxx
3
Fig. 2. Input parameters for selection procedure. Table 1 Input and output parameters. Input 1 Input 2 Input 3 Input 4 Output
Agriculture, value added (% of GDP) Manufacturing, value added (% of GDP) Industry, value added (% of GDP) Services, etc., value added (% of GDP) GDP growth rate
included are imputed bank service charges, import duties, and any statistical discrepancies noted by national compilers as well as discrepancies arising from rescaling. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. Table 1 shows input and output parameters.
1 2 3 4
3. Results Neuro-fuzzy model was trained for each input separately in the beginning in order to estimate the prediction errors for the each input. The influence of the each input on the output can be determined according to the prediction errors. The input with the smallest error has the highest influence on the output. Fig. 3(a) shows the GDP growth rate prediction accuracy for each input separately. The smallest training error can be noted for input 4 (services). Therefore the input 4 has the highest influence on the GDP growth rate prediction. The input 2 (manufacturing) has the highest training error therefore the smallest influence on the output. Since training and testing errors are correlated one can combine two inputs in order to determine the most influential combination of the two inputs on the GDP growth rate prediction. Fig. 2(b) shows that the combination of agriculture and services (input 1 and input 4) forms the optimal combination for the GDP growth rate forecasting. Input–output decision surface for prediction of the GDP growth rate is shown in Fig. 4 based on the neuro-fuzzy algorithm. The figure shows the response of neuro-fuzzy model for the varying input parameters. The highest GDP growth rate can be noted for the smallest value of services and for the highest value of agriculture. 4. Conclusion Forecasting of the future GDP growth rate management is complex task due to the many factors, which influence the GDP. Therefore in this study was proposed an approach to overcome the forecasting difficulties of by selecting the most important parameters for the GDP growth rate prediction. A systematic approach was performed for the selection procedure by neuro-fuzz approach. The approach was used to eliminate the vagueness in the GDP management and to produce the best prediction conditions. The results show that the services have the most influence for the GDP growth rate forecasting. On the other hand, manufacturing has the smallest influence on the GDP growth rate forecasting.
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6 7 8 9 10 11 12 13 14 15 16
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G. Maksimović et al. / Physica A xx (xxxx) xxx–xxx
Fig. 3. GDP growth rate prediction accuracy for: (a) single input and (b) two inputs.
5
0
–5 60 65
1 2
70 3
75
4
80 85
5 6
Fig. 4. Decision surface for agriculture and services benefit in GDP growth rate.
G. Maksimović et al. / Physica A xx (xxxx) xxx–xxx
5
References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]
Joseph Zeira, Hosny Zoabi, Economic growth and sector dynamics, Eur. Econ. Rev. 79 (2015) 1–15. Benno Ferrarini, Pasquale Scaramozzino, Production complexity, adaptability and economic growth, Struct. Change Econ. Dyn. 37 (2016) 52–61. Lihua Feng, Jianzhen Zhang, Application of artificial neural networks in tendency forecasting of economic growth, Econ. Modell. 40 (2014) 76–80. Libor Krkoska, Utku Teksoz, How reliable are forecasts of GDP growth and inflation for countries with limited coverage? Econ. Syst. 33 (2009) 376–388. Libor Krkoska, Utku Teksoz, Accuracy of GDP growth forecasts for transition countries: Ten years of forecasting assessed, Int. J. Forecast. 23 (2007) 29–45. Francisco Dias, Maximiano Pinheiro, António Rua, Forecasting Portuguese GDP with factor models: Pre- and post-crisis evidence, Econ. Modell. 44 (2015) 266–272. Theodore Modis, Long-term GDP forecasts and the prospects for growth, Technol. Forecast. Soc. Change 80 (2013) 1557–1562. George Kapetanios, Massimiliano Marcellino, Fotis Papailias, Forecasting inflation and GDP growth using heuristic optimisation of information criteria and variable reduction methods, Comput. Statist. Data Anal., http://dx.doi.org/10.1016/j.csda.2015.02.017. W. Jos Jansen, Xiaowen Jin, Jasper M. de Winter, Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts, Int. J. Forecast. 32 (2016) 411–436. Fady Barsoum, Sandra Stankiewicz, Forecasting GDP growth using mixed-frequency models with switching regimes, Int. J. Forecast. 31 (2015) 33–50. J.-S.R. Jang, ANFIS: Adaptive-network-based fuzzy inference systems, IEEE Trans. Syst. Man Cybern. 23 (1993) 665–685. A.Al. Ghandoor, M. Samhouri, Electricity consumption in the industrial sector of Jordan: Application of multivariate linear regression and adaptive neuro-fuzzy techniques, Jordan J. Mech. Ind. Eng. 3 (1) (2009) 69–76. R. Singh, A. Kianthola, T.N. Singh, Estimation of elastic constant of rocks using an ANFIS approach, Appl. Soft Comput. 12 (2012) 40–45. S. Kurnaz, O. Cetin, O. Kaynak, Adaptive neuro-fuzzy inference system based autonomous flight control of unmanned air vehicles, Expert Syst. Appl. 37 (2010) 1229–1234. L. Tian, C. Collins, Adaptive neuro-fuzzy control of a flexible manipulator, Mechatronics 15 (2005) 1305–1320.
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