Topic
Simultaneous equations model
About: Simultaneous equations model is a research topic. Over the lifetime, 877 publications have been published within this topic receiving 68551 citations.
Papers published on a yearly basis
1 / 2
Papers
TL;DR: In this article, the null hypothesis of no misspecification was used to show that an asymptotically efficient estimator must have zero covariance with its difference from a consistent but asymptonically inefficient estimator, and specification tests for a number of model specifications in econometrics.
Abstract: Using the result that under the null hypothesis of no misspecification an asymptotically efficient estimator must have zero asymptotic covariance with its difference from a consistent but asymptotically inefficient estimator, specification tests are devised for a number of model specifications in econometrics. Local power is calculated for small departures from the null hypothesis. An instrumental variable test as well as tests for a time series cross section model and the simultaneous equation model are presented. An empirical model provides evidence that unobserved individual factors are present which are not orthogonal to the included right-hand-side variable in a common econometric specification of an individual wage equation.
19,295 citations
Book•
1 Jan 2009
TL;DR: This paper presents a meta-modelling framework for Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables and Two Stage Least Squares, and discusses Serial Correlation and Heteroskedasticity in Time Series Regressions.
Abstract: 1. The Nature of Econometrics and Economic Data. Part I: REGRESSION ANALYSIS WITH CROSS-SECTIONAL DATA. 2. The Simple Regression Model. 3. Multiple Regression Analysis: Estimation. 4. Multiple Regression Analysis: Inference. 5. Multiple Regression Analysis: OLS Asymptotics. 6. Multiple Regression Analysis: Further Issues. 7. Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables. 8. Heteroskedasticity. 9. More on Specification and Data Problems. Part II: REGRESSION ANALYSIS WITH TIME SERIES DATA. 10. Basic Regression Analysis with Time Series Data. 11. Further Issues in Using OLS with Time Series Data. 12. Serial Correlation and Heteroskedasticity in Time Series Regressions. Part III: ADVANCED TOPICS. 13. Pooling Cross Sections across Time: Simple Panel Data Methods. 14. Advanced Panel Data Methods. 15. Instrumental Variables Estimation and Two Stage Least Squares. 16. Simultaneous Equations Models. 17. Limited Dependent Variable Models and Sample Selection Corrections. 18. Advanced Time Series Topics. 19. Carrying out an Empirical Project. APPENDICES. Appendix A: Basic Mathematical Tools. Appendix B: Fundamentals of Probability. Appendix C: Fundamentals of Mathematical Statistics. Appendix D: Summary of Matrix Algebra. Appendix E: The Linear Regression Model in Matrix Form. Appendix F: Answers to Chapter Questions. Appendix G: Statistical Tables. References. Glossary. Index.
8,661 citations
Book•
1 Jan 1993
TL;DR: In this paper, the authors propose a nonlinear regression model based on the Gauss-Newton Regression for least squares, and apply it to time-series data and show that the model can be used for regression models for time series data.
Abstract: 1. The Geometry of Least Squares 2. Nonlinear Regression Models and Nonlinear Least Squares 3. Inference in Nonlinear Regression Models 4. Introduction to Asymptotic Theory and Methods 5. Asymptotic Methods and Nonlinear Least Squares 6. The Gauss-Newton Regression 7. Instrumental Variables 8. The Method of Maximum Likelihood 9. Maximum Likelihood and Generalized Least Squares 10. Serial Correlation 11. Tests Based on the Gauss-Newton Regression 12. Interpreting Tests in Regression Directions 13. The Classical Hypothesis Tests 14. Transforming the Dependent Variable 15. Qualitative and Limited Dependent Variables 16. Heteroskedasticity and Related Topics 17. The Generalized Method of Moments 18. Simultaneous Equations Models 19. Regression Models for Time-Series Data 20. Unit Roots and Cointegration 21. Monte Carlo Experiments
6,481 citations
TL;DR: In this article, the authors considered the formulation and estimation of simultaneous equation models with both discrete and continuous endogenous variables and proposed a statistical model that is sufficiently rich to encompass the classical simultaneous equation model for continuous endogenous variable and more recent models for purely discrete endogenous variables as special cases of a more general model.
Abstract: This paper considers the formulation and estimation of simultaneous equation models with both discrete and continuous endogenous variables. The statistical model proposed here is sufficiently rich to encompass the classical simultaneous equation model for continuous endogenous variables and more recent models for purely discrete endogenous variables as special cases of a more general model.
2,551 citations
Book•
1 Jan 1991
TL;DR: The choice of point and interval forecasts as well as innovation accounting are presented as tools for structural analysis within the multiple time series context.
Abstract: This graduate-level textbook deals with analyzing and forecasting multiple time series. It considers a wide range of multiple time series models and methods. The models include vector autoregressive, vector autoregressive moving average, cointegrated and periodic processes as well as state space and dynamic simultaneous equations models. Least squares, maximum likelihood and Bayesian methods are considered for estimating these models. Different procedures for model selection or specification are treated and a range of tests and criteria for evaluating the adequacy of a chosen model are introduced. The choice of point and interval forecasts as well as innovation accounting are presented as tools for structural analysis within the multiple time series context.
2,295 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2025 | 3 |
| 2024 | 7 |
| 2023 | 10 |
| 2022 | 14 |
| 2021 | 18 |
| 2020 | 8 |