g contractual obligations Gujarati 2009622 22 A researcher wants to set up a

G contractual obligations gujarati 2009622 22 a

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Institutional reasons: e.g contractual obligations (Gujarati, 2009:622). 2.2 A researcher wants to set up a regression equation where Y is a function X. Evaluate the researcher s options given the following scenarios: (4) (a) Y is I(1); X is I(2) The researcher has a problem as the results are likely to be meaningless. Time series have to be integrated of the same order, to use the Engle Granger procedure. Possibly try to use the logs of the time series which provides partial correction for non-stationarity. Otherwise go back to the beginning and re-specify the equation using possible proxy variables. (b) Y is I(1); X is I(1); and the error term is I(0) This indicates that some linear combination of the non-stationary variables might be stationary. The researcher can proceed with the modeling. Downloaded by Odirile See ([email protected]) lOMoARcPSD|4893311
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ECS4863/202 7 Question 3: (10 marks) 3.1 One of the major sources of data used by econometricians is the World Bank World Development Indicators (WDI). For this exercise, you will select three out of the five countries in the Southern African Customs Union (SACU) region (Your choice!). Find per capita Gross Domestic Product (GDP) for the period 1980 -2016. Conduct Unit root tests for each of the selected series. Remember to generate the logs of all the relevant variables, prior to conducting the tests. Also, remember to use stars (*) next to your ADF statistics to indicate their level of significance. Time series used: GDP per capita in local currency units (LCU) as obtained from the World Bank’s WDI series. (7) Series Model ADF Lags ADF: L(Botswana) Trend and intercept 9 -3.220656 Intercept 4 -3.111025** None 0 8.701812 D(LBotswana) Trend and intercept 3 -3.995796** Intercept 3 -2.752892* None 4 -1.046768 L(Lesotho) Trend and intercept 1 -0.708884 Intercept 0 -1.597176 None 3 1.028873 D(LLesotho) Trend and intercept 0 -5.374825*** Intercept 2 -2.209057 None 2 -0.400254 L(Namibia) Trend and intercept 0 -3.456264* Intercept 5 -1.995977 None 0 14.17678 D(LNamibia) Trend and intercept 4 -5.035540*** Intercept 3 -4.292213*** None 5 -0.610457 L(South Africa) Trend and intercept 0 0.357525 Intercept 0 -5.605774*** None 1 1.629068 D(LSouth Africa) Trend and intercept 0 -4.195574** Intercept 0 -2.604241 None 1 -0.870997 L(Swaziland) Trend and intercept 9 -4.930147*** Intercept 9 -4.930147*** None 0 8.235602 D(LSwaziland) Trend and intercept 8 -6.407082*** Intercept 4 -1.309983 None 9 -5.146507*** * Statistically significant at the 10% level ** Statistically significant at the 5% level ***Statistically significant at the 1% level 3.2 Based on your findings in 3.1, comment on the order of integration of GDP per capita for each of the three selected countries. (3) Downloaded by Odirile See ([email protected]) lOMoARcPSD|4893311
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8 Lesotho and Namibia are the only countries which clearly tasted I(1); or non-stationary as we expected. [Note: For Namibia we can strictly speaking ignore the Level (trend and intercept) reading; as it is only statistically significant at the 10% level of significance] Botswana, South Africa and Swaziland all three tested I(0); or stationary in levels. [Note: It is likely that the reason for the “strange” results is the use of ‘per capita’ data . This likely compensates for some of the non-stationarity similar to the effect of using logs] [Note: However the primary aim of this exercise was to get you, the student, used to obtaining data, importing it into EViews and doing some basic calculations. Throughout this course you will do a lot more unit root tests. Therefore we will not look further into the (unexpected) outcome of these results] [Total = 50 mark] Downloaded by Odirile See ([email protected]) lOMoARcPSD|4893311
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