This book is intended for graduate instruction in subjects like econometrics, economics, environmental science, social science and many other fields, at the Masters or PhD levels. It can be used as a textbook or as a reference guide. Several aspects in the book depart from traditional treatments. The emphasis is on understanding the main issues, concepts and methods in Econometrics, how to implement them and how to interpret the results. The mathematical aspects are kept to a minimum as the aim is to provide an intuitive understanding of how various parts fit together, as opposed to a sophisticated mathematical treatment of the subject. Many examples and discussions are provided. Hence, minimal mathematical pre-requisites are needed. Extensive references are also provided to dig deeper into the mathematical aspects of the theories. The second volume deals with various estimation and inference methods applicable when using time series data or with panel data having a large time-dimension. The treatment covers both stationary and non-stationary (i.e., unit root) data as well as long-memory processes. Also covered extensively are issues related to structural change including estimation and inference methods with stationary and/or non-stationary data, related issues in the context of forecasting and methods to address the interplay between changes in trends and unit roots.
Contents:
- Estimation of Time Series Models
- Spectral Analysis: Theory
- Spectral Analysis: Estimation
- State-Space Models
- DSGE Models
- Structural Vector Autoregressions
- Linear Panel Data Models: Large N and T
- Continuous-time Processes and the FCLT
- Unit Roots
- Testing for a Unit Root
- Cointegration
- Long-Memory Processes
- Volatility Models
- Structural Change
- Unit Root and Trend Breaks
- Forecasting
- Numerical Methods
Readership: For graduate students in econometrics and researchers (academia, industry, consulting) in fields such as economics, environmental science, social science and many others.
