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AEC's Demand for ICT: Maximum Entropy Bootstrap Approach in Panel Data
Available online at www.sciencedirect.com
ScienceDirect Procedia Economics and Finance 5 (2013) 125 – 132
International Conference on Applied Economics (ICOAE) 2013
AEC’s Demand for ICT: Maximum Entropy Bootstrap Approach in Panel data. Prasert Chaitipa Chukiat Chaiboonsrib a
Assoc. Prof., Faculty of Economics, Chiang Mai University, Chiang Mai, Thailand b Lecturer. Faculty of Economics, Chiang Mai University, Chiang Mai, Thailand.
Abstract This paper is preliminary information of some empirical findings based on an analysis demand for ICT by using public access facilities of AEC countries. The purpose of the study is to quantify ICT for well-being development of AEC countries with special emphasis on mobile phone, fixed phones and internet user. The maximum entropy bootstrap approach in panel data was presented that rejected the property of stationary. Moreover, the methodology was stratified both the ergodic theorem and the central limit theorem. Firstly, there is a statistically significant positive nonlinear relationship between endogenous demand for mobile phone and exogenous as (1) the number of AEC population and (2) GDP of AEC countries. Secondly, there is a statistically significant positive nonlinear relationship between endogenous demand for fixed phone and exogenous as (1) the number of AEC population and (2) GDP of AEC countries. Lastly, there is a statistically significant positive nonlinear relationship between endogenous demand for internet user and exogenous as (1) the number of AEC population and (2) GDP of AEC countries. The results confirmed that every one percent increase in the number of AEC population influenced on a decrease of AEC demand for ICT by using public access facilities covering mobile phone, fixed phones and internet user. ©2013 2013The The Authors. Published by Elsevier © Authors. Published by Elsevier B.V. B.V. Selectionand/or and/or peer-review under responsibility of the Organising Committee of ICOAE 2013. Selection peer-review under responsibility of the Organising Committee of ICOAE 2013 Keyword:
Demand of ICT; Thailand; Panel data; Maximum Entropy Bootstrap Approach
2212-5671 © 2013 The Authors. Published by Elsevier B.V. Selection and/or peer-review under responsibility of the Organising Committee of ICOAE 2013 doi:10.1016/S2212-5671(13)00018-X
126
Prasert Chaitip and Chukiat Chaiboonsri / Procedia Economics and Finance 5 (2013) 125 – 132
1. Introduction The structure of demand for ICT by using public access facilities is changing rapidly. ICT by using public access facilities of AEC countries have become larger in number, larger in size and more specialized in technological development. This study examines the factors that influence ICT by using public users’ choices among business arrangements offered in the AEC countries. An empirical analysis based on the maximum entropy bootstrap framework for analyzing AEC demand covering mobile phone, fixed phones and internet user. Econometric models are often used to forecast economic well-being developments. Since a model was utilized to generate quantitative forecasts of international economic activity, it is of interest to determine as following: 2. Dynamic Econometric Modeling. A model can be expressed by lny=ln(x1, x2)4. The dynamic form means that as AEC’s demand for ICT5 depends upon the value of x1 or AEC’s population (denotes the number of AEC population) and the value of x2 or GDP of AEC’s countries (denotes AEC’s GDP). A model can be expressed by lny=ln(x1,x2). The natural log button on programming software is probably labeled ln(AEC demand for ICT6) denotes the natural logarithm transformation of the number AEC demand for ICT based on the idea of separating functions covering mobile phone, fixed phones and internet user is a function of ln(x1) is AEC’s population, and ln(x2) is the number of AEC’s GDP. Econometrics modelling can be adopted to investigate of ambiguity states that the timed behaviour of the stochastic process. The maximum entropy experiment is recently introduced process to fix those problems of ambiguity states especially within the case of econometric model estimation. Maximum entropy bootstrap method can be found in the research work of Vinod, (2006, 2009). An overview of the steps in Vinod 's ME bootstrap algorithm7 in seven steps were shown as follows. First step arranges the original data in increasing order to create order statistics X(t)and stores the ordering index vector. Second step computes intermediate points Zt= (X(t) + X(t-1)) / 2 for t = 1,…..T – 1 from the order statistics. Third step computes the trimmed mean mtrm of deviations Xt - Xt-1among all consecutive observation and also computes the lower limit for left tail as Z0 = X(1) - mtrm and upper limit for right tail as ZT = X(T) - mtrm.
----------------------------------------------------
4
The natural log button on programming software is probably labelled ln (mobile phone) denotes the natural logarithm transformation of a number mobile phone as AEC demand for mobile phone is a function of ln(x1 or population, x 1 denotes the numbers of AEC’s population. 5 Mobile phone, fixed phones and internet user. 6 The idea of separating functions : mobile phone function, fixed phone function and internet user function can be explained in three formed separated from each other’s. 7 Unit root testing compares I(1) with I(0), but completely ignores I(d) or long memory models, where d is fractional which are quite realistic for econometrics. Maximum Entropy Bootstrap for Time Series. http://www.jstatsoft.org/v29/i05/paper. 8 Unit root testing does not give conclusive results extended Nelson-Plosser data. The testing compares I(1) with I(0), but completely ignores I(d) or long memory models,. It is nearly impossible to make every variable in a regression to be of the same order, I(0), I(1) etc., as is suggested by the unit root theorists. The problem is the usual t-tests are unreliable. http://www.jstatsoft.org/v29/i05/paper.
127
Prasert Chaitip and Chukiat Chaiboonsri / Procedia Economics and Finance 5 (2013) 125 – 132
These limits become the limiting intermediate point. Forth step computes the mean of the maximum entropy density within each interval such that the “mean-preserving constraint” (designed to eventually satisfy the ergodic theorem) is satisfied. Interval means are denoted as mt. The means for the first and the last interval have simpler formulas. Fifth step generates random number from the [0,1] uniform interval, compute sample quantiles of the ME density at those points and sort them. Sixth step reorders the sorted sample quantiles by using the ordering index of step. Lastly, seventh step recovers the time dependence relationships of the originally observed data.Repeat steps 2 to 6 several times (e.g.,999). And also the maximum entropy bootstrap method can be found in Vinod's works (2006, 2009). The overview of the steps in Vinod's ME bootstrap algorithm8 can be found in details. Moreover, the Highest Density Regions (HDR) approach was conducted to reaffirm an estimation developed by Hyndman (1996) (see more detail in Hyndman, 1996). 3. Results and Discussion From table (1-3), public access facilities of ICT for AEC countries need to be developed to provide solid phrase for sustainable development. Endogenous as demand for ICT by using public access facilities using by mobile phone, fixed phones and internet user and exogenous as (1) the number of AEC population and (2) GDP of AEC countries Table (1-2) presents secondary data covering the sixteen-year period 1996-2011. Table (3) presents secondary data covering the ten-year period 2001-2011. Table 1: AEC demand 1 for mobile phones during period of 1996-2011(unit:1,000)
Variable
Obs.
Mean
Std. Dev.
Min
Max
Brunei
16
222.85
149.6871
43.5
443.2
Combodia
16
2,505.044
3,868.74
23.1
13,757.0
Indonesia
16
65,308.66
82,385.36
562.5
249,805.6
Laos
16
1,145.481
1,699.428
3.8
5,480.9
Malaysia
16
15,421.13
12,077.93
1,520.3
36,661.3
Myanmar
16
223.5187
329.3478
7.3
1,243.6
Philippines
16
34,522.8
32,080.07
959
94,189.8
128
Prasert Chaitip and Chukiat Chaiboonsri / Procedia Economics and Finance 5 (2013) 125 – 132
Singapore
16
4,012.537
2,372.549
431
7,794.3
Thailand
16
30,355.21
27,718.48
1,844.6
11,604.7
Vietnam
16
31,119.95
45,409.70
68.9
127.318.0
From: computed Table 2: AEC demand 2 for fixed phones during period of 1996-2011(unit:1,000)
Variable
Obs.
Mean
Std. Dev.
Min
Max
Brunei
16
80.76875
2.712863
76.8
88.4
Combodia
16
83.31875
144.6518
15.8
530.0
Indonesia
16
15,842.6
12,931.93
4,186.0
40,932.1
Laos
16
70.275
33.75742
19.5
127.8
Malaysia
16
4,421.637
225.4762
3,771.3
4,709.6
Myanmar
16
379.5312
127.2821
178.6
571.3
Philippines
16
3,232.625
655.3195
1,787.0
4,100.0
Singapore
16
1,865.631
114.0449
1,562.7
2,018.1
Thailand
16
6,262.294
989.8385
4,160.2
7,394.3
Vietnam
16
7,134.884
5,366.729
1,186.4
17,427.4
From: computer
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Prasert Chaitip and Chukiat Chaiboonsri / Procedia Economics and Finance 5 (2013) 125 – 132
Table 3: AEC demand 3 for internet user during period of 2001-2011(unit:1,000)
Variable
Obs.
Mean
Std. Dev.
Min
Max
Brunei
11
36.81
15.27415
12.92
56
Combodia
11
0.6863636
0.8565544
0.08
3.1
Indonesia
11
6.095455
4.851701
2.02
18
Laos
11
2.759091
3.162102
0.18
9
Malaysia
11
47.38455
11.53984
26.7
61
Myanmar
11
0.2572727
0.3392666
0
0.98
Philippines
11
9.217273
9.050528
0.63
29
Singapore
11
61.31
10.15907
41.67
71
Thailand
11
15.42636
6.250877
5.56
23.7
Vietnam
11
15.66182
11.30286
0.32
35.07
From: computed From table (4), the first step results of estimation did not confirmed by using simple regression approach 9* the number of AEC population increase 1% then AEC demand for mobile phone will increased 17.22%. The estimation results indicated that the fixed effect model is an inappropriate to estimate the AEC demand for mobile phone covering the sixteen-year period 1996-2011. Therefore; the second step introduced maximum entropy bootstrap approach for panel data analysis that was conducted to estimate this model. There is a statistically significant nonlinear relationship between endogenous as demand for mobile phone and
9
* OLS approach was suffered due to the problem of panel data in time series analysis, for example, stationary in panel data analysis, non-stationary in panel data analysis and panel co-integration analysis.
130
Prasert Chaitip and Chukiat Chaiboonsri / Procedia Economics and Finance 5 (2013) 125 – 132
exogenous as the number of AEC population. The first results confirmed that every one percent increase in the number of AEC population influenced on a increase of demand for mobile phone coefficient 17.28 percent or 17.41 percent. The results confirmed that every one percent increase in GDP of AEC influenced on an increase of demand for mobile phone coefficient 0.046 percent or 0.051 percent. Table 4: Results of estimation based on maximum entropy bootstrap approach in panel data for AEC demand for mobile phone.
Percentile.ln.pop Refined.ln.pop Percentile.ln.gdp Refined.ln.pop
2.5% 15.39678146 14.84511767 -0.01046543 -0.01996020
97.5% 17.41560558 17.28170475 0.05102648 0.04699975 From: computed
From table (5), first step results of estimation did not confirmed by using simple regression approach 10 the number of AEC population increase 1% then AEC demand for fixed phone will increased. The estimation results indicated that the fixed effect model is an inappropriate to estimate the AEC demand for fixed phone covering the sixteen-year period 1996-2011. Therefore, the second step introduced maximum entropy bootstrap approach for panel data analysis that was conducted to estimate this model. There is a statistically significant nonlinear relationship between endogenous as demand for fixed phones and exogenous as the number of AEC population. The second step results confirmed that every one percent increase in the number of AEC population influenced on a increase of demand for fixed phones coefficient 3.71% or 3.76% at significant level of 97.5%. The results confirmed that every one percent increase in GDP of AEC influenced on an increase of the number of demand for fixed phones coefficient 0.0102% or 0.0108% at significant level of 97.5%. Table 5: Results of estimation based on maximum entropy bootstrap approach in panel data for AEC demand for fixed phone.
Percentile.ln.pop Refined.ln.pop Percentile.ln.gdp Refined.ln.pop
2.5% 3.06346528 2.88128549 -0.01209793 -0.01161350
97.5% 3.76510185 3.71208699 0.01029604 0.01084718 From: computed
----------------10
OLS approach was suffered due to the problem of panel data in time series analysis, for example, stationary in panel data analysis, non-stationary in panel data analysis and panel co-integration analysis.
131
Prasert Chaitip and Chukiat Chaiboonsri / Procedia Economics and Finance 5 (2013) 125 – 132
The estimation results indicated that the fixed effect model is an inappropriate 11† to estimate the AEC demand for internet user during the ten-year period 2001 to 2011. From table (6), first step results of estimation did not confirmed by using simple regression approach12‡ the number of AEC population increase 1% then AEC demand for internet user will increased. There is a statistically significant nonlinear relationship between endogenous as demand for internet user and exogenous as the number of AEC population. The estimation results indicated that the fixed effect model is an inappropriate to estimate the AEC demand for internet user covering the ten-year period 2001-2011. Therefore; the second step introduced maximum entropy bootstrap approach for panel data analysis that was conducted to estimate this model. The second results confirmed that every one percent increase in the number of AEC population influenced on an increase of demand for internet user coefficient 120.75% or 122.96% at significant level of 97.5%. The results confirmed that every one percent increase in GDP of AEC influenced on an increase of the number of demand for internet user coefficient 0.35% or 0.39% at significant level of 97.5%. Table 6: Results of estimation based on maximum entropy bootstrap approach in panel data for AEC demand for internet user.
Percentile.ln.pop Refined.ln.pop Percentile.ln.gdp Refined.ln.pop
2.5% 99.06205543 92.91344404 -0.04661431 -0.15201383
97.5% 122.9690549 120.7512136 0.3922369 0.3556151 From: computed
10†
OLS approach was suffered due to the problem of panel data in time series analysis, for example, stationary in panel data analysis, non-stationary in panel data analysis and panel co-integration analysis.
11‡
OLS approach was suffered due to the problem of panel data in time series analysis, for example, stationary in panel data analysis, non-stationary in panel data analysis and panel co-integration analysis.
132
Prasert Chaitip and Chukiat Chaiboonsri / Procedia Economics and Finance 5 (2013) 125 – 132
4. Conclusion The maximum entropy approach in panel data was presented that rejected the property of stationary. The maximum entropy approach is newly one statistical process to fix those problems especially with the estimation of econometric model. Firstly, there is a statistically significant positive nonlinear relationship between endogenous demand for mobile phone and exogenous as (1) the number of AEC population and (2) GDP of AEC countries. Secondly, there is a statistically significant positive nonlinear relationship between endogenous demand for fixed phone and exogenous as (1) the number of AEC population and (2) GDP of AEC countries. Lastly, there is a statistically significant positive nonlinear relationship between endogenous demand for internet user and exogenous as (1) the number of AEC population and (2) GDP of AEC countries. The results confirmed that every one percent increase in the number of AEC population influenced on a decrease of AEC demand for ICT by using public access facilities covering mobile phone, fixed phones and internet user. Acknowledgements First of all, the authors would like to thank Faculty of Economics, Chiang Mai University for supporting to produce this research. References 1. 2. 3. 4. 5.
Chaiboonsri, C., Chokethaworn,K., and Chaitip,P. (2012), “Frontier of Econometrics Time Series Analysis in ICT's Stock Market of Thailand: Maximum Entropy Bootstrap Approach”, Procedia Economics and Finance, Volume 1, 2012, Pages 8187 Hrishikesh D. Vinod and Javier L_opez-de-Lacalle,(2009 ), “ Maximum Entropy Bootstrap for Time Series: The meboot R Package”, Journal of Statistical Software. Hyndman RJ (1996). “Computing and Graphing Highest Density Regions." The American Statistician, 50, 120-126. Hausman JA (1978). “Specification Tests in Econometrics.”, Econometrica, 46, 1251-1271. Hyndman RJ (2008). hdrcde: Highest Density Regions and Conditional Density Estimation. R package version 2.09, URL http://CRAN.R-project.org/package=hdrcde.
ScienceDirect Procedia Economics and Finance 5 (2013) 125 – 132
International Conference on Applied Economics (ICOAE) 2013
AEC’s Demand for ICT: Maximum Entropy Bootstrap Approach in Panel data. Prasert Chaitipa Chukiat Chaiboonsrib a
Assoc. Prof., Faculty of Economics, Chiang Mai University, Chiang Mai, Thailand b Lecturer. Faculty of Economics, Chiang Mai University, Chiang Mai, Thailand.
Abstract This paper is preliminary information of some empirical findings based on an analysis demand for ICT by using public access facilities of AEC countries. The purpose of the study is to quantify ICT for well-being development of AEC countries with special emphasis on mobile phone, fixed phones and internet user. The maximum entropy bootstrap approach in panel data was presented that rejected the property of stationary. Moreover, the methodology was stratified both the ergodic theorem and the central limit theorem. Firstly, there is a statistically significant positive nonlinear relationship between endogenous demand for mobile phone and exogenous as (1) the number of AEC population and (2) GDP of AEC countries. Secondly, there is a statistically significant positive nonlinear relationship between endogenous demand for fixed phone and exogenous as (1) the number of AEC population and (2) GDP of AEC countries. Lastly, there is a statistically significant positive nonlinear relationship between endogenous demand for internet user and exogenous as (1) the number of AEC population and (2) GDP of AEC countries. The results confirmed that every one percent increase in the number of AEC population influenced on a decrease of AEC demand for ICT by using public access facilities covering mobile phone, fixed phones and internet user. ©2013 2013The The Authors. Published by Elsevier © Authors. Published by Elsevier B.V. B.V. Selectionand/or and/or peer-review under responsibility of the Organising Committee of ICOAE 2013. Selection peer-review under responsibility of the Organising Committee of ICOAE 2013 Keyword:
Demand of ICT; Thailand; Panel data; Maximum Entropy Bootstrap Approach
2212-5671 © 2013 The Authors. Published by Elsevier B.V. Selection and/or peer-review under responsibility of the Organising Committee of ICOAE 2013 doi:10.1016/S2212-5671(13)00018-X
126
Prasert Chaitip and Chukiat Chaiboonsri / Procedia Economics and Finance 5 (2013) 125 – 132
1. Introduction The structure of demand for ICT by using public access facilities is changing rapidly. ICT by using public access facilities of AEC countries have become larger in number, larger in size and more specialized in technological development. This study examines the factors that influence ICT by using public users’ choices among business arrangements offered in the AEC countries. An empirical analysis based on the maximum entropy bootstrap framework for analyzing AEC demand covering mobile phone, fixed phones and internet user. Econometric models are often used to forecast economic well-being developments. Since a model was utilized to generate quantitative forecasts of international economic activity, it is of interest to determine as following: 2. Dynamic Econometric Modeling. A model can be expressed by lny=ln(x1, x2)4. The dynamic form means that as AEC’s demand for ICT5 depends upon the value of x1 or AEC’s population (denotes the number of AEC population) and the value of x2 or GDP of AEC’s countries (denotes AEC’s GDP). A model can be expressed by lny=ln(x1,x2). The natural log button on programming software is probably labeled ln(AEC demand for ICT6) denotes the natural logarithm transformation of the number AEC demand for ICT based on the idea of separating functions covering mobile phone, fixed phones and internet user is a function of ln(x1) is AEC’s population, and ln(x2) is the number of AEC’s GDP. Econometrics modelling can be adopted to investigate of ambiguity states that the timed behaviour of the stochastic process. The maximum entropy experiment is recently introduced process to fix those problems of ambiguity states especially within the case of econometric model estimation. Maximum entropy bootstrap method can be found in the research work of Vinod, (2006, 2009). An overview of the steps in Vinod 's ME bootstrap algorithm7 in seven steps were shown as follows. First step arranges the original data in increasing order to create order statistics X(t)and stores the ordering index vector. Second step computes intermediate points Zt= (X(t) + X(t-1)) / 2 for t = 1,…..T – 1 from the order statistics. Third step computes the trimmed mean mtrm of deviations Xt - Xt-1among all consecutive observation and also computes the lower limit for left tail as Z0 = X(1) - mtrm and upper limit for right tail as ZT = X(T) - mtrm.
----------------------------------------------------
4
The natural log button on programming software is probably labelled ln (mobile phone) denotes the natural logarithm transformation of a number mobile phone as AEC demand for mobile phone is a function of ln(x1 or population, x 1 denotes the numbers of AEC’s population. 5 Mobile phone, fixed phones and internet user. 6 The idea of separating functions : mobile phone function, fixed phone function and internet user function can be explained in three formed separated from each other’s. 7 Unit root testing compares I(1) with I(0), but completely ignores I(d) or long memory models, where d is fractional which are quite realistic for econometrics. Maximum Entropy Bootstrap for Time Series. http://www.jstatsoft.org/v29/i05/paper. 8 Unit root testing does not give conclusive results extended Nelson-Plosser data. The testing compares I(1) with I(0), but completely ignores I(d) or long memory models,. It is nearly impossible to make every variable in a regression to be of the same order, I(0), I(1) etc., as is suggested by the unit root theorists. The problem is the usual t-tests are unreliable. http://www.jstatsoft.org/v29/i05/paper.
127
Prasert Chaitip and Chukiat Chaiboonsri / Procedia Economics and Finance 5 (2013) 125 – 132
These limits become the limiting intermediate point. Forth step computes the mean of the maximum entropy density within each interval such that the “mean-preserving constraint” (designed to eventually satisfy the ergodic theorem) is satisfied. Interval means are denoted as mt. The means for the first and the last interval have simpler formulas. Fifth step generates random number from the [0,1] uniform interval, compute sample quantiles of the ME density at those points and sort them. Sixth step reorders the sorted sample quantiles by using the ordering index of step. Lastly, seventh step recovers the time dependence relationships of the originally observed data.Repeat steps 2 to 6 several times (e.g.,999). And also the maximum entropy bootstrap method can be found in Vinod's works (2006, 2009). The overview of the steps in Vinod's ME bootstrap algorithm8 can be found in details. Moreover, the Highest Density Regions (HDR) approach was conducted to reaffirm an estimation developed by Hyndman (1996) (see more detail in Hyndman, 1996). 3. Results and Discussion From table (1-3), public access facilities of ICT for AEC countries need to be developed to provide solid phrase for sustainable development. Endogenous as demand for ICT by using public access facilities using by mobile phone, fixed phones and internet user and exogenous as (1) the number of AEC population and (2) GDP of AEC countries Table (1-2) presents secondary data covering the sixteen-year period 1996-2011. Table (3) presents secondary data covering the ten-year period 2001-2011. Table 1: AEC demand 1 for mobile phones during period of 1996-2011(unit:1,000)
Variable
Obs.
Mean
Std. Dev.
Min
Max
Brunei
16
222.85
149.6871
43.5
443.2
Combodia
16
2,505.044
3,868.74
23.1
13,757.0
Indonesia
16
65,308.66
82,385.36
562.5
249,805.6
Laos
16
1,145.481
1,699.428
3.8
5,480.9
Malaysia
16
15,421.13
12,077.93
1,520.3
36,661.3
Myanmar
16
223.5187
329.3478
7.3
1,243.6
Philippines
16
34,522.8
32,080.07
959
94,189.8
128
Prasert Chaitip and Chukiat Chaiboonsri / Procedia Economics and Finance 5 (2013) 125 – 132
Singapore
16
4,012.537
2,372.549
431
7,794.3
Thailand
16
30,355.21
27,718.48
1,844.6
11,604.7
Vietnam
16
31,119.95
45,409.70
68.9
127.318.0
From: computed Table 2: AEC demand 2 for fixed phones during period of 1996-2011(unit:1,000)
Variable
Obs.
Mean
Std. Dev.
Min
Max
Brunei
16
80.76875
2.712863
76.8
88.4
Combodia
16
83.31875
144.6518
15.8
530.0
Indonesia
16
15,842.6
12,931.93
4,186.0
40,932.1
Laos
16
70.275
33.75742
19.5
127.8
Malaysia
16
4,421.637
225.4762
3,771.3
4,709.6
Myanmar
16
379.5312
127.2821
178.6
571.3
Philippines
16
3,232.625
655.3195
1,787.0
4,100.0
Singapore
16
1,865.631
114.0449
1,562.7
2,018.1
Thailand
16
6,262.294
989.8385
4,160.2
7,394.3
Vietnam
16
7,134.884
5,366.729
1,186.4
17,427.4
From: computer
129
Prasert Chaitip and Chukiat Chaiboonsri / Procedia Economics and Finance 5 (2013) 125 – 132
Table 3: AEC demand 3 for internet user during period of 2001-2011(unit:1,000)
Variable
Obs.
Mean
Std. Dev.
Min
Max
Brunei
11
36.81
15.27415
12.92
56
Combodia
11
0.6863636
0.8565544
0.08
3.1
Indonesia
11
6.095455
4.851701
2.02
18
Laos
11
2.759091
3.162102
0.18
9
Malaysia
11
47.38455
11.53984
26.7
61
Myanmar
11
0.2572727
0.3392666
0
0.98
Philippines
11
9.217273
9.050528
0.63
29
Singapore
11
61.31
10.15907
41.67
71
Thailand
11
15.42636
6.250877
5.56
23.7
Vietnam
11
15.66182
11.30286
0.32
35.07
From: computed From table (4), the first step results of estimation did not confirmed by using simple regression approach 9* the number of AEC population increase 1% then AEC demand for mobile phone will increased 17.22%. The estimation results indicated that the fixed effect model is an inappropriate to estimate the AEC demand for mobile phone covering the sixteen-year period 1996-2011. Therefore; the second step introduced maximum entropy bootstrap approach for panel data analysis that was conducted to estimate this model. There is a statistically significant nonlinear relationship between endogenous as demand for mobile phone and
9
* OLS approach was suffered due to the problem of panel data in time series analysis, for example, stationary in panel data analysis, non-stationary in panel data analysis and panel co-integration analysis.
130
Prasert Chaitip and Chukiat Chaiboonsri / Procedia Economics and Finance 5 (2013) 125 – 132
exogenous as the number of AEC population. The first results confirmed that every one percent increase in the number of AEC population influenced on a increase of demand for mobile phone coefficient 17.28 percent or 17.41 percent. The results confirmed that every one percent increase in GDP of AEC influenced on an increase of demand for mobile phone coefficient 0.046 percent or 0.051 percent. Table 4: Results of estimation based on maximum entropy bootstrap approach in panel data for AEC demand for mobile phone.
Percentile.ln.pop Refined.ln.pop Percentile.ln.gdp Refined.ln.pop
2.5% 15.39678146 14.84511767 -0.01046543 -0.01996020
97.5% 17.41560558 17.28170475 0.05102648 0.04699975 From: computed
From table (5), first step results of estimation did not confirmed by using simple regression approach 10 the number of AEC population increase 1% then AEC demand for fixed phone will increased. The estimation results indicated that the fixed effect model is an inappropriate to estimate the AEC demand for fixed phone covering the sixteen-year period 1996-2011. Therefore, the second step introduced maximum entropy bootstrap approach for panel data analysis that was conducted to estimate this model. There is a statistically significant nonlinear relationship between endogenous as demand for fixed phones and exogenous as the number of AEC population. The second step results confirmed that every one percent increase in the number of AEC population influenced on a increase of demand for fixed phones coefficient 3.71% or 3.76% at significant level of 97.5%. The results confirmed that every one percent increase in GDP of AEC influenced on an increase of the number of demand for fixed phones coefficient 0.0102% or 0.0108% at significant level of 97.5%. Table 5: Results of estimation based on maximum entropy bootstrap approach in panel data for AEC demand for fixed phone.
Percentile.ln.pop Refined.ln.pop Percentile.ln.gdp Refined.ln.pop
2.5% 3.06346528 2.88128549 -0.01209793 -0.01161350
97.5% 3.76510185 3.71208699 0.01029604 0.01084718 From: computed
----------------10
OLS approach was suffered due to the problem of panel data in time series analysis, for example, stationary in panel data analysis, non-stationary in panel data analysis and panel co-integration analysis.
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The estimation results indicated that the fixed effect model is an inappropriate 11† to estimate the AEC demand for internet user during the ten-year period 2001 to 2011. From table (6), first step results of estimation did not confirmed by using simple regression approach12‡ the number of AEC population increase 1% then AEC demand for internet user will increased. There is a statistically significant nonlinear relationship between endogenous as demand for internet user and exogenous as the number of AEC population. The estimation results indicated that the fixed effect model is an inappropriate to estimate the AEC demand for internet user covering the ten-year period 2001-2011. Therefore; the second step introduced maximum entropy bootstrap approach for panel data analysis that was conducted to estimate this model. The second results confirmed that every one percent increase in the number of AEC population influenced on an increase of demand for internet user coefficient 120.75% or 122.96% at significant level of 97.5%. The results confirmed that every one percent increase in GDP of AEC influenced on an increase of the number of demand for internet user coefficient 0.35% or 0.39% at significant level of 97.5%. Table 6: Results of estimation based on maximum entropy bootstrap approach in panel data for AEC demand for internet user.
Percentile.ln.pop Refined.ln.pop Percentile.ln.gdp Refined.ln.pop
2.5% 99.06205543 92.91344404 -0.04661431 -0.15201383
97.5% 122.9690549 120.7512136 0.3922369 0.3556151 From: computed
10†
OLS approach was suffered due to the problem of panel data in time series analysis, for example, stationary in panel data analysis, non-stationary in panel data analysis and panel co-integration analysis.
11‡
OLS approach was suffered due to the problem of panel data in time series analysis, for example, stationary in panel data analysis, non-stationary in panel data analysis and panel co-integration analysis.
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4. Conclusion The maximum entropy approach in panel data was presented that rejected the property of stationary. The maximum entropy approach is newly one statistical process to fix those problems especially with the estimation of econometric model. Firstly, there is a statistically significant positive nonlinear relationship between endogenous demand for mobile phone and exogenous as (1) the number of AEC population and (2) GDP of AEC countries. Secondly, there is a statistically significant positive nonlinear relationship between endogenous demand for fixed phone and exogenous as (1) the number of AEC population and (2) GDP of AEC countries. Lastly, there is a statistically significant positive nonlinear relationship between endogenous demand for internet user and exogenous as (1) the number of AEC population and (2) GDP of AEC countries. The results confirmed that every one percent increase in the number of AEC population influenced on a decrease of AEC demand for ICT by using public access facilities covering mobile phone, fixed phones and internet user. Acknowledgements First of all, the authors would like to thank Faculty of Economics, Chiang Mai University for supporting to produce this research. References 1. 2. 3. 4. 5.
Chaiboonsri, C., Chokethaworn,K., and Chaitip,P. (2012), “Frontier of Econometrics Time Series Analysis in ICT's Stock Market of Thailand: Maximum Entropy Bootstrap Approach”, Procedia Economics and Finance, Volume 1, 2012, Pages 8187 Hrishikesh D. Vinod and Javier L_opez-de-Lacalle,(2009 ), “ Maximum Entropy Bootstrap for Time Series: The meboot R Package”, Journal of Statistical Software. Hyndman RJ (1996). “Computing and Graphing Highest Density Regions." The American Statistician, 50, 120-126. Hausman JA (1978). “Specification Tests in Econometrics.”, Econometrica, 46, 1251-1271. Hyndman RJ (2008). hdrcde: Highest Density Regions and Conditional Density Estimation. R package version 2.09, URL http://CRAN.R-project.org/package=hdrcde.