Time Series Data Analysis Using EViews (Statistics in Practice)
Time Series Data Analysis Using EViews (Statistics in Practice)
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This book provides a hands-on practical guide to using the most suitable models for analysis of statistical data sets using EViews - an interactive Windows-based computer software program for sophisticated data analysis, regression, and forecasting - to define and test statistical hypotheses. Rich in examples and with an emphasis on how to develop acceptable statistical models, Time Series Data Analysis Using EViews is a perfect complement to theoretical books presenting statistical or econometric models for time series data. The procedures introduced are easily extendible to cross-section data sets.
The author:- Provides step-by-step directions on how to apply EViews software to time series data analysis
- Offers guidance on how to develop and evaluate alternative empirical models, permitting the most appropriate to be selected without the need for computational formulae
- Examines a variety of times series models, including continuous growth, discontinuous growth, seemingly causal, regression, ARCH, and GARCH as well as a general form of nonlinear time series and nonparametric models
- Gives over 250 illustrative examples and notes based on the author's own empirical findings, allowing the advantages and limitations of each model to be understood
- Describes the theory behind the models in comprehensive appendices
- Provides supplementary information and data sets
Table of Contents: Preface
1 EViews workle and descriptive data analysis
1.1 What is the EViews workle?
1.2 Basic options in EViews
1.3 Creating a workle
1.3.1 Creating a workle using EViews 5 or 6
1.3.2 Creating a workle using EViews 4
1.4 Illustrative data analysis
1.4.1 Basic descriptive statistical summary
1.4.2 Box plots and outliers
1.4.3 Descriptive statistics by groups
1.4.4 Graphs over times
1.4.5 Means seasonal growth curve
1.4.6 Correlation matrix
1.4.7 Autocorrelation and partial autocorrelation
1.4.8 Bivariate graphical presentation with regression
1.5 Special notes and comments
1.6 Statistics as a sample space
2 Continuous growth models
2.1 Introduction
2.2 Classical growth models
2.3 Autoregressive growth models
2.3.1 First-order autoregressive growth models
2.3.2 AR(p) growth models
2.4. Residual tests
2.4.1 Hypothesis of no serial correlation
2.4.2 Hypothesis of the homogeneous residual term
2.4.3 Hypothesis of the normality assumption
2.4.4 Correlogram Q-statistic
2.5 Bounded autoregressive growth models
2.6 Lagged variables or autoregressive growth models
2.6.1 The white estimation method
2.6.2 The Newey–West HAC estimation method
2.6.3 The Akaike Information and Schwarz Criterions
2.6.4 Mixed lagged-variables autoregressive growth models
2.6.5 Serial correlation LM test for LV(2,1)_GM
2.7 Polynomial growth model
2.7.1 Basic polynomial growth models
2.7.2 Special polynomial growth models
2.8 Growth models with exogenous variables
2.9 A Taylor series approximation model
2.10 Alternative univariate growth models
2.10.1 A more general growth model
2.10.2 Translog additive growth models
2.10.3 Some comments
2.10.4 Growth model having interaction factors
2.10.5 Trigonometric growth models
2.11 Multivariate growth models
2.11.1 The classical multivariate growth model
2.11.2 Modied multivariate growth models
2.11.3 AR(1) multivariate general growth models
2.11.4 The S-shape multivariate AR(1) general growth models
2.12 Multivariate AR(p) GLM with trend
2.12.1 Kernel density and theoretical distribution
2.13 Generalized multivariate models with trend
2.13.1 The simplest multivariate autoregressive model
2.13.2 Multivariate autoregressive model with two-way interactions
2.13.3 Multivariate autoregressive model with three-way interactions
2.14 Special notes and comments
2.14.1 The true population model
2.14.2 Near singular matrix
2.14.3 ‘To Test or Not’ the assumptions of the error terms
2.15 Alternative multivariate models with trend
2.15.1 The lagged endogenous variables: rst autoregressive model with trend
2.15.2 The lagged endogenous variables: rst autoregressive model with exogenous variables and trend
2.15.3 The mixed lagged variables: rst autoregressive model with trend
2.16 Generalized multivariate models with time-related effects
3 Discontinuous growth models
3.1 Introduction
3.2 Piecewise growth models
3.2.1 Two-piece classical growth models
3.3 Piecewise S-shape growth models
3.3.1 Two-piece linear growth models
3.4 Two-piece polynomial bounded growth models
3.4.1 Two-piece quadratic growth models
3.4.2 Two-piece third-degree bounded growth model
3.4.3 Two-piece generalized exponential growth model
3.5 Discontinuous translog linear AR(1) growth models
3.6 Alternative discontinuous growth models
3.7 Stability test
3.7.1 Chow’s breakpoint test
3.7.2 Chow’s forecast test
3.8 Generalized discontinuous models with trend
3.8.1 General two-piece univariate models with trend
3.8.2 Special notes and comments
3.8.3 General two-piece multivariate models with trend
3.9 General two-piece models with time-related effects
3.10 Multivariate models by states and time periods
3.10.1 Alternative models
3.10.2 Not recommended models
4 Seemingly causal models
4.1 Introduction
4.2 Statistical analysis based on a single time series
4.2.1 The means model
4.2.2 The cell-means models
4.2.3 The lagged-variable models
4.2.4 Autoregressive models
4.2.5 Lagged-variable autoregressive models
4.3 Bivariate seemingly causal models
4.3.1 The simplest seemingly causal models
4.3.2 Simplest models in three-dimensional space
4.3.3 General univariate LVAR(p,q) seemingly causal model
4.4 Trivariate seemingly causal models
4.4.1 Simple models in three-dimensional space
4.4.2 General LVAR(p,q) with exogenous variables
4.5 System equations based on trivariate time series
4.6 General system of equations
4.7 Seemingly causal models with dummy variables
4.7.1 Homogeneous time series models
4.7.2 Heterogeneous time series models
4.8 General discontinuous seemingly causal models
4.9 Additional selected seemingly causal models
4.9.1 A Third-degree polynomial function
4.9.2 A Three-dimensional bounded semilog linear model
4.9.3 Time series Cobb–Douglas models
4.9.4 Time series CES models
4.10 Final notes in developing models
4.10.1 Expert judgment
4.10.2 Other unexpected models
4.10.3 The principal component factor analysis
5 Special cases of regression models
5.1 Introduction
5.2 Specic cases of growth curve models
5.2.1 Basic polynomial model
5.2.2 An AR(1) regression model
5.2.3 Heteroskedasticity-consistent covariance (White)
5.3 Seemingly causal models
5.3.1 Autoregressive models
5.4 Lagged variable models
5.4.1 The basic lagged-variable model
5.4.2 Some notes
5.4.3 Generalized lagged-variable autoregressive model
5.5 Cases based on the US domestic price of copper
5.5.1 Graphical representation
5.5.2 Seemingly causal model
5.5.3 Generalized translog linear model
5.5.4 Constant elasticity of substitution models
5.5.5 Models for the rst difference of an endogenous variable
5.5.6 Unexpected ndings
5.5.7 Multivariate linear seemingly causal models
5.6 Return rate models
5.7 Cases based on the BASICS workle
5.7.1 Special notes
6 VAR and system estimation methods
6.1 Introduction
6.2 The VAR models
6.2.1 The basic VAR model
6.2.2 The VAR models with exogenous variables
6.2.3 Cases based on the demo_modied workle
6.2.4 The VAR models with dummy variables
6.2.5 Selected VAR models based on the US domestic price of copper data
6.3 The vector error correction models
6.3.1 The basic VEC model
6.3.2 General equation of the basic VEC models
6.3.3 The VEC models with exogenous variables
6.3.4 Some notes and comments
6.4 Special notes and comments
7 Instrumental variables models
7.1 Introduction
7.2 Should we apply instrumental models?
7.3 Residual analysis in developing instrumental models
7.3.1 Testing an hypothesis corresponding to the instrumental models
7.3.2 Graphical representation of the residual series
7.4 System equation with instrumental variables
7.5 Selected cases based on the US_DPOC data
7.6 Instrumental models with time-related effects
7.7 Instrumental seemingly causal models
7.7.1 Special notes and comments
7.8 Multivariate instrumental models based on the US_DPOC
7.8.1 Simple multivariate instrumental models
7.8.2 Multivariate instrumental models
7.9 Further extension of the instrumental models
8 ARCH models
8.1 Introduction
8.2 Options of ARCH models
8.3 Simple ARCH models
8.3.1 Simple ARCH models
8.3.2 Special notes on the ARCH models
8.4 ARCH models with exogenous variables
8.4.1 ARCH models with one exogenous variable
8.4.2 ARCH models with two exogenous variables
8.4.3 Advanced ARCH models
8.5 Alternative GARCH variance series
8.5.1 General GARCH variance series for the GARCH/TARCH model
8.5.2 General GARCH variance series for the EGARCH model
8.5.3 General GARCH variance series for the PARCH model
8.5.4 General GARCH variance series for the component ARCH(1,1) model
8.5.5 Special notes on the GARCH variance series
9 Additional testing hypotheses
9.1 Introduction
9.2 The unit root tests
9.2.1 Simple unit root test
9.2.2 Unit root test for higher-order serial correlation
9.2.3 Comments on the unit root tests
9.3 The omitted variables tests
9.4 Redundant variables test (RV-test)
9.5 Nonnested test (NN-test)
9.6 The Ramsey RESET test
9.7 Illustrative examples based on the Demo.wf1
10 Nonlinear least squares models
10.1 Introduction
10.2 Classical growth models
10.3 Generalized Cobb–Douglas models
10.3.1 Cases based on the Demo.wf1
10.3.2 Cases based on the BASIC.wf1
10.3.3 Cases based on the US_DPOC data
10.4 Generalized CES models
10.5 Special notes and comments
10.6 Other NLS models
10.6.1 Cases based on Demo.wf1
10.6.2 Cases based on the US_DPOC data
11 Nonparametric estimation methods
11.1 What is the nonparametric data analysis
11.2 Basic moving average estimates
11.2.1 Simple moving average estimates
11.2.2 The weighted moving average estimates
11.3 Measuring the best t model
11.4 Advanced moving average models
11.4.1 The moving average models
11.4.2 The autoregressive moving average models
11.4.3 The ARMA models with covariates
11.5 Nonparametric regression based on a time series
11.5.1 The Hardle moving average models
11.5.2 The nearest neighbor t
11.5.3 Mathematical background of the nearest neighbor t
11.6 The local polynomial Kernel t regression
11.7 Nonparametric growth models
Appendix A: Models for a single time series
A.1 The simplest model
A.1.1 OLS estimates
A.1.2 Properties of the error terms
A.1.3 Maximum likelihood estimates
A.2 First-order autoregressive models
A.2.1 Properties of the parameters
A.2.2 Autocorrelation function of an AR(1) model
A.2.3 Estimates of the parameters
A.3 Second-order autoregressive model
A.3.1 Properties of the parameters
A.3.2 Autocorrelation function of an AR(2) model
A.3.3 Estimates of the parameters
A.4 First-order moving average model
A.5 Second-order moving average model
A.6 The simplest ARMA model
A.7 General ARMA model
A.7.1 Derivation of the ACF
A.7.2 Estimation method
Appendix B: Simple linear models
B.1 The simplest linear model
B.1.1 Least squares estimators
B.2 Linear model with basic assumptions
B.2.1 Sampling distributions of the model parameters
B.2.2 Student’s t-statistic
B.2.3 Analysis of variance table
B.2.4 Coefcient of determination
B.3 Maximum likelihood estimation method
B.4 First-order autoregressive linear model
B.4.1 Two-stage estimation method
B.4.2 Durbin–Watson statistic
B.4.3 Properties of the error term t
B.4.4 Maximum likelihood estimation method
B.5 AR(p) linear model
B.5.1 Estimation method
B.5.2 Properties of t
B.6 Alternative models
B.6.1 Alternative 1: The simplest model with trend
B.6.2 Alternative 2: The classical growth model
B.6.3 Alternative 3: The AR( p) polynomial model
B.6.4 Alternative 4: The AR( p) return rate model
B.6.5 Alternative 5: The bounded translog linear (Cobb–Douglas) AR( p) model
B.7 Lagged-variable model
B.8 Lagged-variable autoregressive models
B.8.1 The simplest lagged-variable autoregressive model
B.8.2 General lagged-variable autoregressive model
B.9 Special notes and comments
Appendix C: General linear models
C.1 General linear model with i.i.d. Gaussian disturbances
C.1.1 The OLS estimates
C.1.2 Maximum likelihood estimates
C.1.3 Student’s t-statistic
C.1.4 The Wald form of the OLS F-test
C.2 AR(1) general linear model
C.2.1 Properties of t
C.2.2 Estimation method
C.3 AR(p) general linear model
C.4 General lagged-variable autoregressive model
C.5 General models with Gaussian errors
C.5.1 Gaussian errors with a known variance covariance matrix
C.5.2 Generalized least squares with a known covariance matrix
C.5.3 GLS and ML estimations
C.5.4 The variance of the error is proportional to the square of one of the explanatory variables
C.5.5 Generalized least squares with an unknown covariance matrix
Appendix D: Multivariate general linear models
D.1 Multivariate general linear models
D.2 Moments of an endogenous multivariate
D.3 Vector autoregressive model
D.4 Vector moving average model
D.5 Vector autoregressive moving average model
D.6 Simple multivariate models with exogenous variables
D.6.1 The simplest multivariate model
D.6.2 Simple model with a multidimensional exogenous variable
D.6.3 A more general model
D.6.4 Selected bivariate time series models
D.6.5 Bivariate Granger causality tests
D.6.6 Simultaneous causal model
D.6.7 Additional bivariate models
D.7 General estimation methods
D.7.1 The OLS estimates
D.8 Maximum likelihood estimation for an MGLM
D.8.1 Student’s t-test
D.8.2 The Wald form of the OLS F-test
D.8.3 Residual analysis
D.9 MGLM with autoregressive errors
D.9.1 AR(p) MGLM with equal sets of exogenous variables
D.9.2 AR(p) MGLM with unequal sets of exogenous variables
D.9.3 Special notes and comments
References
Index
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