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Is Per Capita Real GDP Stationary in African Countries? Evidence from Panel SURADF Test

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Is Per Capita Real GDP Stationary in African Countries? Evidence from Panel SURADF Test Applied Economics Letters, 2006, 13, 1003–1008 Is per capita real GDP stationary in African countries? Evidence from panel SURADF test Tsangyao Changa,*, Hsu-Ling Changb, Hsiao-Ping Chuc and Chi-Wei Sud aDepartment of Finance, Feng Chia University, Taichung, Taiwan bDepartment of Accounting and Information Technology, Ling Tung University, Taichung, Taiwan cDepartment of Business Administration, Ling Tung University, Taichung, Taiwan dDepartment of Finance, Providence University, Taichung, Taiwan This note uses the newly developed panel SURADF tests advanced by Breuer et al. (2001) to investigate the time-series properties of real GDP for 47 African countries for the period 1980 to 2004. While the other Panel-based unit root tests are joint tests of a unit root for all members of the panel and are incapable of determining the mix of I(0) and I(1) series in the panel setting, the Panel SURADF tests a separate unit-root null hypothesis for each individual panel member and, therefore identifies how many and which series in the panel are stationary processes. The empirical results from several panel-based unit root tests indicate that the per capita real GDP for all the countries studied are non-stationary, however, when Breuer et al.’s Panel SURADF tests are conducted, one finds unit root in per capita real GDP only exist in two-third of countries studied. These results have important policy implications for African countries. I. Introduction making, modelling, testing and forecasting. Studies on this issue are of concern not only to empirical Ever since Nelson and Plosser (1982) published their researchers but also policymakers. seminal work, various studies have been devoted to While numerous studies support a unit root in real investigating the potential non-stationarity of impor- output levels, critics have claimed that the drawing of tant macroeconomic variables. Researchers have such conclusions may be attributed to the lower been especially interested in the time-series properties power of the conventional unit root tests employed. of real output levels. As pointed out by Nelson and More recently, it has been reported that conventional Plosser, the modelling of real output levels as either unit root tests not only fail to consider information a trend stationary or a difference stationary process across regions, thereby leading to less efficient has important implications for macroeconomic policy estimations, but also have lower power when *Corresponding author. E-mail: [email protected] Applied Economics Letters ISSN 1350–4851 print/ISSN 1466–4291 online ß 2006 Taylor & Francis 1003 http://www.tandf.co.uk/journals DOI: 10.1080/13504850500425881 1004 T. Chang et al. Table 1. Summary statistics of real gross domestic product per capita Country (US dollar) Mean Std Max. Min. Skewness Kurtosis J-B Algeria 2035.897 381.422 2753.697 1499.143 0.518 1.498 3.469 Angola 497.549 538.762 1189.953 363.506 0.191 1.092 3.944 Benin 300.673 102.918 581.233 178.336 0.779 3.136 2.546 Botswana 2313.439 680.007 3572.989 1272.046 0.212 1.978 1.274 Burkina Faso 256.590 68.339 388.669 178.041 0.614 2.070 2.471 Burundi 175.549 120.816 371.072 23.367 0.164 1.595 2.168 Cameroon 774.224 332.512 1343.355 345.756 0.124 1.566 2.206 Cape Verde 879.951 138.754 1240.351 653.064 1.032 4.033 5.545* Central African Republic 338.491 148.081 680.999 161.469 0.425 2.198 1.424 Chad 195.618 63.145 316.784 112.137 0.479 1.828 2.386 Comoros 411.015 120.596 721.4585 244.951 0.570 2.789 1.402 Congo, Republic of 769.195 299.048 1282.790 415.787 0.550 1.797 2.768 Coˆte d’Ivoire 1201.525 562.539 2729.909 550.948 0.772 3.221 2.532 Djibouti 722.763 182.969 974.7221 502.455 0.054 1.367 2.788 Equatorial Guinea 236.526 182.602 808.950 89.908 1.939 5.941 24.675*** Ethiopia 79.659 43.4106 131.701 28.572 À0.045 1.116 3.704 Gabon 4346.859 1662.414 8554.497 2154.364 0.592 2.713 1.545 Gambia, The 486.039 439.192 1635.512 90.918 1.454 3.747 9.387*** Ghana 711.827 734.978 2984.895 271.557 4.497 2.335 33.903*** Guinea 918.228 1142.041 4231.459 137.319 1.832 5.228 19.153*** Guinea-Bissau 180.305 73.212 335.528 81.995 0.297 1.957 1.500 Kenya 333.481 261.302 1034.231 96.826 1.073 3.411 4.968* Lesotho 363.363 236.658 1012.594 110.362 1.352 4.140 8.971*** Madagascar 179.399 243.841 1005.623 15.305 2.069 6.819 33.039*** Malawi 206.078 220.511 772.170 5.524 1.069 3.204 4.807* Mali 231.828 76.581 417.832 134.539 0.635 2.398 2.056 Mauritania 325.860 164.159 662.988 127.336 0.494 2.219 1.652 Mauritius 2451.358 359.508 3539.317 1711.967 0.746 4.888 6.029** Morocco 525.732 113.615 973.546 396.456 2.652 10.922 94.670*** Mozambique 1264.538 2012.412 5814.852 12.0382 1.266 3.009 6.677** Namibia 2635.201 2491.935 10 697.51 559.390 1.972 6.237 27.113*** Niger 234.053 116.218 574.534 105.240 1.039 3.981 5.501* Nigeria 661.764 1100.186 3991.718 21.536 1.989 5.783 24.543*** Rwanda 220.151 144.776 437.481 48.952 0.114 1.343 2.913 Sa˜o Tome´and Prı´ncipe 793.407 965.308 2601.297 7.055 0.767 1.882 3.751 Senegal 563.376 204.657 1065.590 318.445 0.494 2.350 1.456 Seychelles 5260.307 1503.335 7236.928 2959.876 À0.359 1.566 2.681 Sierra Leone 242.423 87.477 412.0230 132.253 1.808 4.698 16.619*** South Africa 6462.024 5457.954 21045.53 1389.232 1.456 4.116 10.132*** Sudan 100.571 163.942 687.869 123.579 2.194 7.798 44.031*** Swaziland 1041.070 603.715 2471.466 305.989 1.055 3.409 4.809* Tanzania 886.439 1346.512 3934.956 35.241 1.363 3.267 7.809** Togo 322.239 155.899 751.911 137.682 0.788 3.255 2.654 Tunisia 1615.540 370.336 2944.431 1294.548 2.439 8.624 57.752*** Uganda 314.9610 80.587 546.667 135.638 2.446 7.643 47.379*** Zambia 3943.519 7183.844 677.150 260.170 1.742 4.508 15.011*** Zimbabwe 873.905 983.927 1881.997 497.806 1.149 3.203 5.538* Notes: Std denotes standard deviation and J-B denotes the Jarque–Bera test for normality. ***, **, and * indicate significance at the 0.01, 0.05 and 0.1 levels, respectively. GDP in African countries 1005 compared with near-unit-root but stationary alter- results meanwhile indicate that the per capita real natives. It is not surprising that these factors have GDP datasets for more than half of the 47 African cast considerable doubt on many of the earlier countries are not approximately normal. findings that have been based on a unit root in real output levels. A feasible way for increasing the power when testing unit root is to suggest that panel data have III. Panel Unit Root Methodology and been used. Taylor and Sarno (1998), Breuer et al. Empirical Results (2001), Taylor (2003) and Taylor and Taylor (2004) showed that the recent methodological refinements of Breuer et al.’s seemingly unrelated regressions aug- the Levin-Lin test fail to fully address the ‘all- mented Dickey–Fuller test (SURADF). Breuer et al. or-nothing’ nature of the test. Because they are joint (2001) claimed that, by analogy to simple regression, tests of the null hypothesis, they are not informative when an F-statistic rejects the null that a vector of with regard to the number of series that are stationary coefficients is equal to zero, it does not follow that processes when the null hypothesis is rejected. Breuer each coefficient is nonzero. Similarly, when the unit- et al. (2001) further claimed that, by analogy to root null hypothesis is rejected, it may be erroneous simple regression, when an F-statistic rejects the null to conclude that all series in the panel are stationary. that a vector of coefficients is equal to zero, it does To avoid the problem, Breuer et al. (2001) introduced not follow that each coefficient is nonzero. Similarly, the ‘seemingly unrelated regressions augmented when the unit-root null hypothesis is rejected, it may Dickey–Fuller’ (SURADF) test, which is an augmen- be erroneous to conclude that all series in the panel ted Dickey–Fuller test based on the panel estimation are stationary. method of seemingly unrelated regression (SUR). The This empirical note contributes to this line of system of the ADF equations that is estimated here is: research by determining whether or not the unit root Xk1 process is characteristic of the African real output ÁX1, t ¼ 1 þ 1X1, tÀ1 þ t þ 1, jÁX1, tÀj þ "1, t levels. This study, tests the non-stationarity of per j¼1 capita real GDP of 47 African countries by using the t ¼ 1, 2, ..., T panel SURADF unit root tests of Breuer et al.
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