Model with exogenous variables and trend 114 2.15.3 The mixed lagged variables: first autoregressive Model with trend 113 2.15.2 The lagged endogenous variables: first autoregressive Interactions 102 2.14 Special notes and comments 104Ģ.14.1 The true population model 104 2.14.2 Near singular matrix 105 2.14.3 'To Test or Not' the assumptions of the error terms 107Ģ.15 Alternative multivariate models with trend 113 2.15.1 The lagged endogenous variables: first autoregressive Interactions 100 2.13.3 Multivariate autoregressive model with three-way
#Eviews 10 demo serial#
Residual tests 32 2.4.1 Hypothesis of no serial correlation 33 2.4.2 Hypothesis of the homogeneous residual term 34 2.4.3 Hypothesis of the normality assumption 34 2.4.4 Correlogram Q-statistic 35Ģ.5 Bounded autoregressive growth models 38 2.6 Lagged variables or autoregressive growth models 41Ģ.6.1 The white estimation method 42 2.6.2 The Newey-West estimation method 43 2.6.3 The Akaike Information and Schwarz Criterions 44 2.6.4 Mixed lagged-variables autoregressive growth models 44 2.6.5 Serial correlation LM test for LV(2,1)_GM 48Ģ.7 Polynomial growth model 49 2.7.1 Basic polynomial growth models 49 2.7.2 Special polynomial growth models 55Ģ.8 Growth models with exogenous variables 56 2.9 A Taylor series approximation model 59 2.10 Alternative univariate growth models 60Ģ.10.1 A more general growth model 60 2.10.2 Translog additive growth models 60 2.10.3 Some comments 63 2.10.4 Growth model having interaction factors 64 2.10.5 Trigonometric growth models 69Ģ.11 Multivariate growth models 70 2.11.1 The classical multivariate growth model 70 2.11.2 Modified multivariate growth models 74 2.11.3 AR(1) multivariate general growth models 78 2.11.4 The S-shape multivariate AR(1) general growth models 79Ģ.12 Multivariate AR(p) GLM with trend 79 2.12.1 Kernel density and theoretical distribution 88Ģ.13 Generalized multivariate models with trend 95 2.13.1 The simplest multivariate autoregressive model 95 2.13.2 Multivariate autoregressive model with two-way in Mathematical Statistics from University of North Carolina at Chapel Hillġ EViews workfile and descriptive data analysis 1 1.1 What is the EViews workfile? 1 1.2 Basic options in EViews 1 1.3 Creating a workfile 3ġ.3.1 Creating a workfile using EViews 5 or 6 3 1.3.2 Creating a workfile using EViews 4 3ġ.4 Illustrative data analysis 7 1.4.1 Basic descriptive statistical summary 7 1.4.2 Box plots and outliers 11 1.4.3 Descriptive statistics by groups 11 1.4.4 Graphs over times 12 1.4.5 Means seasonal growth curve 15 1.4.6 Correlation matrix 15 1.4.7 Autocorrelation and partial autocorrelation 17 1.4.8 Bivariate graphical presentation with regression 18ġ.5 Special notes and comments 19 1.6 Statistics as a sample space 22Ģ Continuous growth models 25 2.1 Introduction 25 2.2 Classical growth models 25 2.3 Autoregressive growth models 29Ģ.3.1 First-order autoregressive growth models 29 2.3.2 AR(p) growth models 30Ģ.4. TIME SERIES DATA ANALYSIS USING EVIEWS I Gusti Ngurah Agung Graduate School Of Management Faculty Of Economics University Of Indonesia Ph.D.