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.

Series Editor's Introduction 1. Introduction 2. Theoretical Derivation of the Pooled Time Series Model 3. The Constant Coefficients Model 4. The LSDV Model 5. The Random Coefficient Model 6. THe Structural Equation Model 7. Robustness: How Good Are These Estimates? 8. Conclusions o Pooled Time Series Note Reference About the Author