Pooled time series analysis
Researchers have often been troubled with relevant data available from both temporal observations at regular intervals (time series) and from observations at single points of time (cross-sections). Pooled Time Series Analysis combines time series and cross-sectional data to provide the researcher with an efficient method of analysis and improved estimates of the population being studied. In addition, with more relevant data available this analysis technique allows the sample size to be increased, which ultimately yields a more effective study.
What people are saying - Write a review
We haven't found any reviews in the usual places.
Series Editors Introduction
The Structural Equation Model 52
The Constant Coefficients Model
5 other sections not shown
Other editions - View all
actor ARCH model ARMA assume assumption autocorrelation autoregressive process Bartlett test comparable constant coefficients model correlation covariance data set dependent variable derived diplomatic friendliness distribution dummy variable dummy variable model Durbin-Watson statistic econometric esti example Executive Adjustments fects fixed Friendliness on Trade full pool function GLS estimator Goldfeld Goldfeld-Quandt test heteroscedastic errors heteroscedasticity homoscedasticity Hsaio included intercept international conflict lagged endogenous variable linear LOGIT LSDV estimates LSDV model matrix maximum likelihood nonconstant variance nonlinear null hypothesis OLS and LSDV parameter partial autocorrelation pooled design pooled Durbin-Watson pooled regression pooled time series Popularity on Prior Prior Approval PROBIT problem random coefficient model regression model Regression of Diplomatic residual variance sample Scatterplot Seemingly Unrelated Regression source of contamination specific stacking standard error stochastic structural equation model Swamy model switching model Table techniques theoretical tion toregression two-stage estimation unique unit effects unit of analysis vari variance-covariance matrix variation vector