By I. Gusti Ngurah Agung
A accomplished and available advisor to panel information research utilizing EViews software program. This e-book explores using EViews software program in growing panel information research utilizing acceptable empirical types and actual datasets. assistance is given on constructing replacement descriptive statistical summaries for evaluate and supplying coverage research in response to pool panel information. a number of substitute versions in line with panel info are explored, together with univariate common linear types, mounted impression types and causal versions, and suggestions at the benefits and drawbacks of every one is given.
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Additional info for Panel Data Analysis using EViews
24 presents the statistical results based on a MAR(1) ANCOVA growth model, using the object “System”. Based on these results the following conclusions and notes are presented. ^ 1. Note that the growth rates of GDP of the four states are assumed to be equal to Cð11Þ ¼ 0:011266 which ^ are unacceptable in a theoretical sense. Similarly so for the first autoregressive indicator Cð12Þ ¼ 0:960457. 25 Statistical results based on an alternative MAR(1) ANCOVA growth model 2. However, in a statistical sense, this model is an acceptable MANCOVA model of the variables log(GDP_US), log(GDP_Ger), log(GDP_UK), and log(GDP_JPN), where the time t is considered covariate, with the intercept parameters: C(10), C(20), C(30), and C(40).
N. Note that any continuous and discontinuous growth models in Agung (2009a) could be inserted for the function Fi(t,C(iÃ)). 1 A Polynomial Growth Model The independent-states system of polynomial growth models has the following equation for i ¼ 1, . . ,N. logðY_it Þ ¼ cði0Þ þ cði1ÞÃ t þ . . 2 A Translog Linear Model The independent-states system of the translog linear growth models has the following equation for i ¼ 1, . . ,N. 3 The Simplest Nonlinear Growth Model The independent-states system of the simplest nonlinear growth model has the following equation for i ¼ 1, .
31 presents the results of a VAR model using the system function, or the object “System”. Based on these results, the following notes and conclusions are made. 1. The model represents a VAR model by entering “1 1” as the lag interval of endogenous. Refer to alternative lag intervals of alternative VAR models presented in Chapter 6 (Agung, 2009a), as well as the limitation of a VAR model compared to the system equation. 2. Since it is defined that the effect of Z1 on (Y_1,Y_2,Y_3) depends on time, t, then the null hypothesis H0: C(15) ¼ C(16) ¼ C(25) ¼ C(26) ¼ C(35) ¼ C(36) ¼ 0 should be tested at the first stage of testing the hypothesis.