- •Preface
- •Part IV. Basic Single Equation Analysis
- •Chapter 18. Basic Regression Analysis
- •Equation Objects
- •Specifying an Equation in EViews
- •Estimating an Equation in EViews
- •Equation Output
- •Working with Equations
- •Estimation Problems
- •References
- •Chapter 19. Additional Regression Tools
- •Special Equation Expressions
- •Robust Standard Errors
- •Weighted Least Squares
- •Nonlinear Least Squares
- •Stepwise Least Squares Regression
- •References
- •Chapter 20. Instrumental Variables and GMM
- •Background
- •Two-stage Least Squares
- •Nonlinear Two-stage Least Squares
- •Limited Information Maximum Likelihood and K-Class Estimation
- •Generalized Method of Moments
- •IV Diagnostics and Tests
- •References
- •Chapter 21. Time Series Regression
- •Serial Correlation Theory
- •Testing for Serial Correlation
- •Estimating AR Models
- •ARIMA Theory
- •Estimating ARIMA Models
- •ARMA Equation Diagnostics
- •References
- •Chapter 22. Forecasting from an Equation
- •Forecasting from Equations in EViews
- •An Illustration
- •Forecast Basics
- •Forecasts with Lagged Dependent Variables
- •Forecasting with ARMA Errors
- •Forecasting from Equations with Expressions
- •Forecasting with Nonlinear and PDL Specifications
- •References
- •Chapter 23. Specification and Diagnostic Tests
- •Background
- •Coefficient Diagnostics
- •Residual Diagnostics
- •Stability Diagnostics
- •Applications
- •References
- •Part V. Advanced Single Equation Analysis
- •Chapter 24. ARCH and GARCH Estimation
- •Basic ARCH Specifications
- •Estimating ARCH Models in EViews
- •Working with ARCH Models
- •Additional ARCH Models
- •Examples
- •References
- •Chapter 25. Cointegrating Regression
- •Background
- •Estimating a Cointegrating Regression
- •Testing for Cointegration
- •Working with an Equation
- •References
- •Binary Dependent Variable Models
- •Ordered Dependent Variable Models
- •Censored Regression Models
- •Truncated Regression Models
- •Count Models
- •Technical Notes
- •References
- •Chapter 27. Generalized Linear Models
- •Overview
- •How to Estimate a GLM in EViews
- •Examples
- •Working with a GLM Equation
- •Technical Details
- •References
- •Chapter 28. Quantile Regression
- •Estimating Quantile Regression in EViews
- •Views and Procedures
- •Background
- •References
- •Chapter 29. The Log Likelihood (LogL) Object
- •Overview
- •Specification
- •Estimation
- •LogL Views
- •LogL Procs
- •Troubleshooting
- •Limitations
- •Examples
- •References
- •Part VI. Advanced Univariate Analysis
- •Chapter 30. Univariate Time Series Analysis
- •Unit Root Testing
- •Panel Unit Root Test
- •Variance Ratio Test
- •BDS Independence Test
- •References
- •Part VII. Multiple Equation Analysis
- •Chapter 31. System Estimation
- •Background
- •System Estimation Methods
- •How to Create and Specify a System
- •Working With Systems
- •Technical Discussion
- •References
- •Vector Autoregressions (VARs)
- •Estimating a VAR in EViews
- •VAR Estimation Output
- •Views and Procs of a VAR
- •Structural (Identified) VARs
- •Vector Error Correction (VEC) Models
- •A Note on Version Compatibility
- •References
- •Chapter 33. State Space Models and the Kalman Filter
- •Background
- •Specifying a State Space Model in EViews
- •Working with the State Space
- •Converting from Version 3 Sspace
- •Technical Discussion
- •References
- •Chapter 34. Models
- •Overview
- •An Example Model
- •Building a Model
- •Working with the Model Structure
- •Specifying Scenarios
- •Using Add Factors
- •Solving the Model
- •Working with the Model Data
- •References
- •Part VIII. Panel and Pooled Data
- •Chapter 35. Pooled Time Series, Cross-Section Data
- •The Pool Workfile
- •The Pool Object
- •Pooled Data
- •Setting up a Pool Workfile
- •Working with Pooled Data
- •Pooled Estimation
- •References
- •Chapter 36. Working with Panel Data
- •Structuring a Panel Workfile
- •Panel Workfile Display
- •Panel Workfile Information
- •Working with Panel Data
- •Basic Panel Analysis
- •References
- •Chapter 37. Panel Estimation
- •Estimating a Panel Equation
- •Panel Estimation Examples
- •Panel Equation Testing
- •Estimation Background
- •References
- •Part IX. Advanced Multivariate Analysis
- •Chapter 38. Cointegration Testing
- •Johansen Cointegration Test
- •Single-Equation Cointegration Tests
- •Panel Cointegration Testing
- •References
- •Chapter 39. Factor Analysis
- •Creating a Factor Object
- •Rotating Factors
- •Estimating Scores
- •Factor Views
- •Factor Procedures
- •Factor Data Members
- •An Example
- •Background
- •References
- •Appendix B. Estimation and Solution Options
- •Setting Estimation Options
- •Optimization Algorithms
- •Nonlinear Equation Solution Methods
- •References
- •Appendix C. Gradients and Derivatives
- •Gradients
- •Derivatives
- •References
- •Appendix D. Information Criteria
- •Definitions
- •Using Information Criteria as a Guide to Model Selection
- •References
- •Appendix E. Long-run Covariance Estimation
- •Technical Discussion
- •Kernel Function Properties
- •References
- •Index
- •Symbols
- •Numerics
References—703
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where k is one of the supported kernel functions and b is the bandwidth.
Combined Individual Tests (Fisher/Johansen)
(38.21)
(38.22)
Fisher (1932) derives a combined test that uses the results of the individual independent tests. Maddala and Wu (1999) use Fisher’s result to propose an alternative approach to testing for cointegration in panel data by combining tests from individual cross-sections to obtain at test statistic for the full panel.
If pi is the p-value from an individual cointegration test for cross-section i , then under the null hypothesis for the panel,
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By default, EViews reports the x2 value based on MacKinnon-Haug-Michelis (1999) p-val- ues for Johansen’s cointegration trace test and maximum eigenvalue test.
References
Boswijk, H. Peter (1995). “Identifiability of Cointegrated Systems,” Technical Report, Tinbergen Institute.
Engle, Robert F. and C. W. J. Granger (1987). “Co-integration and Error Correction: Representation, Estimation, and Testing,” Econometrica, 55, 251–276.
Fisher, R. A. (1932). Statistical Methods for Research Workers, 4th Edition, Edinburgh: Oliver & Boyd.
Hamilton, James D. (1994). Time Series Analysis, Princeton: Princeton University Press.
Johansen, Søren (1991). “Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models,” Econometrica, 59, 1551–1580.
Johansen, Søren (1995). Likelihood-based Inference in Cointegrated Vector Autoregressive Models, Oxford: Oxford University Press.
Johansen, Søren and Katarina Juselius (1990). “Maximum Likelihood Estimation and Inferences on Coin- tegration—with applications to the demand for money,” Oxford Bulletin of Economics and Statistics, 52, 169–210.
704—Chapter 38. Cointegration Testing
Kao, Chinwa D. (1999). “Spurious Regression and Residual-Based Tests for Cointegration in Panel Data,”
Journal of Econometrics, 90, 1–44.
Maddala, G. S. and S. Wu (1999). “A Comparative Study of Unit Root Tests with Panel Data and A New Simple Test,” Oxford Bulletin of Economics and Statistics, 61, 631–52.
MacKinnon, James G. (1996). “Numerical Distribution Functions for Unit Root and Cointegration Tests,”
Journal of Applied Econometrics, 11, 601-618.
MacKinnon, James G., Alfred A. Haug, and Leo Michelis (1999), “Numerical Distribution Functions of Likelihood Ratio Tests for Cointegration,” Journal of Applied Econometrics, 14, 563-577.
Osterwald-Lenum, Michael (1992). “A Note with Quantiles of the Asymptotic Distribution of the Maximum Likelihood Cointegration Rank Test Statistics,” Oxford Bulletin of Economics and Statistics, 54, 461–472.
Pedroni, P. (1999). “Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors,” Oxford Bulletin of Economics and Statistics, 61, 653–70.
Pedroni, P. (2004). “Panel Cointegration; Asymptotic and Finite Sample Properties of Pooled Time Series Tests with an Application to the PPP Hypothesis,” Econometric Theory, 20, 597–625.