Running Head GLOBAL COMPETITIVENESS

Global Competitiveness

Global Competitiveness



1) Using time-series and panel data from 2008, this paper examines the Granger causality relations between GDP, exports, and FDI among China, Korea, Taiwan, Hong Kong, Singapore, Malaysia, Philippines, and Thailand, the eight rapidly developing East and Southeast Asian economies. After reviewing the current literature and testing the properties of individual time-series data, we estimate the VAR of the three variables to find various Granger causal relations for each of the eight economies. We found each country has different causality relations and does not yield general rules. We then construct the panel data of the three variables for the eight economies as a group and then use the fixed effects and random effects approaches to estimate the panel data VAR equations for Granger causality tests. The panel data causality results reveal that FDI has unidirectional effects on GDP directly and also indirectly through exports, and there also exists bidirectional causality between exports and GDP for the group. Our results indicate that the panel data causality analysis has superior results over the time-series causality analysis. Economic and policy implications of our analyses are then explored in the conclusions.

2) ln(GDP i) = b0 + b1 QMSEi + ui

we examine the causality relations among the real variables Y, X, and F. If certain regularity conditions are satisfied, the non-linear functions C(Y), I(Y, r), and M(Y, e), or more directly, Eq. (1), can be expanded logarithmically around the origin by the Taylor expansion. Taking the linear part of the variables, regressing each of three variables on the other two variables, and taking the lags of each variable for the purpose of econometric analysis, we have the prototype of a vector autoregression (VAR) form for the Granger causality test. Eq. (2) in Section below shows the final form of the VAR model, which may be written either in levels or differenced series.

H(Y,X,F)=0

Table 1.

ADF and DF-GLS unit root tests on level series: eight individual economies

ADF test

DF-GLS

k

Test-statistic (p-value)

k

ERS test-statistic

1 China

 ex

3

-4.097 (0.03)**

3

-4.237***

 fdi

3

-2.298 (0.41)

3

-2.681

 gdp

3

-1.130 (0.89)

0

-1.428

2 Korea

 ex

1

-3.467 (0.08)*

1

-3.399**

 fdi

1

-2.982 (0.16)

1

-3.116*

gdp

0

-2.694 (0.25)

0

-2.502

3 Taiwan

 ex

0

-3.742 (0.05)**

0

-3.281**

 fdi

3

-4.605 (0.01)***

2

-4.330***

 gdp

3

-2.622 (0.28)

0

-2.720

4 Hong Kong

 ex

0

-3.188 (0.12)

0

-2.341

 fdi

0

-2.747 (0.23)

0

-2.928*

 gdp

0

-3.057 (0.15)

0

-2.640

5 Singapore

 ex

1

-2.213 (0.45)

1

-2.111

 fdi

0

-3.099 (0.14)

0

-3.245**

 gdp

0

-1.137 (0.89)

1

-1.708

6 Malaysia

 ex

0

-1.455 (0.81)

0

-1.495

 fdi

0

-2.240 (0.44)

0

-2.275

 gdp

0

-1.630 (0.74)

0

-1.682

7 Philippines

 ex

0

-1.912 (0.61)

0

-2.020

 fdi

3

0.956 (0.99)

0

-2.684

 gdp

0

-2.285 (0.42)

0

-2.337

8 Thailand

 ex

0

-2.115 (0.50)

1

-1.924

 fdi

2

-2.522 (0.31)

2

-2.663

gdp

1

-2.169 (0.48)

1

-2.027

Notes: (1) The test equations include constant and linear trend. Null hypothesis: series has a unit root. (2) In DF-GLS test, the critical values are -3.77, -3.19, and -2.89 for the 1%, 5%, and 10% level, respectively. (3) The lag length (k) is selected by the minimum AIC with maximum lag = 3. (4) ***, **, * denote rejection of null hypothesis at the 1%, 5%, 10% level of significance, respectively.

For Hong Kong, Singapore, Malaysia, Philippines, and Thailand, the ADF and DF-GLS test results in Table 1 show that all the level series are not stationary, except that Hong Kong and Singapore's fdi are stationary at the 10% and 5% level, respectively, in the DF-GLS test. In addition, in Table 2, the ADF and DF-GLS tests show that the first-difference series are all stationary series for these ...