The book describes and illustrates many advances that have taken place in a number of areas in theoretical and applied econometrics over the past four decades.
This book is concerned with recent developments in time series and panel data techniques for the analysis of macroeconomic and financial data. It provides a rigorous, nevertheless user-friendly, account of the time series techniques dealing with univariate and multivariate time series models, as well as panel data models.
M. Hashem Pesaran is the John Elliot Distinguished Chair in Economics and professor of economics at USC Dornsife, the Director of the USC Dornsife Institute of Economic Thinking, and Director of Centre in Applied Financial Economics at USC. He is also a Fellow of Trinity College, and an emeritus Professor of Economics at Cambridge University. He received his Ph.D. in economics from Cambridge University. Prior to 1979 he headed the Economic Research Department of the Central Bank of Iran and served as Under-Secretary of the Iranian Ministry of Education. Dr Pesaran is a fellow of the British Academy, the Econometric Society, and the Journal of Econometrics. He has received the George Sell Prize and the Royal Economic Society Prize. He has more than 200 publications in the areas of econometrics, empirical finance, and macroeconomics and the Iranian economy. He is a co-developer of Microfit, an econometric software package published by Oxford University Press.
Part I: Introduction to Econometrics 1: Relationship Between Two Variables 2: Multiple Regression 3: Hypothesis Testing in Regression Models 4: Heteroskedasticity 5: Autocorrelated Disturbances 6: Introduction to Dynamic Economic Modelling 7: Predictability of Asset Returns and the EMH Part II: Statistical Theory 8: Asymptotic Theory 9: Maximum Likelihood Estimation 10: Generalized Method of Moments 11: Model Selection and Testing Non-Nested Hypotheses Part III: Stochastic Processes 12: Introduction to Stochastic Processes 13: Spectral Analysis Part IV: Univariate Time Series Models 14: Estimation of Stationary Time Series Processes 15: Unit Root Processes 16: Trend and Cycle Decomposition 17: Introduction to Forecasting 18: Measurement and Modelling of Volatility Part V: Multivariate Time Series Models 19: Multivariate Analysis 20: Multivariate Rational Expectations Models 21: Vector Autoregressive Models 22: Cointegration Analysis 23: VARX Modelling 24: Impulse Response Analysis 25: Modelling the Conditional Correlation of Asset Returns Part VI: Panel Data Econometrics 26: Panel Data Models with Strictly Exogenous Regressors 27: Short T Dynamic Panel Data Models 28: Large Heterogeneous Panel Data Models 29: Cross Section Dependence in Panels 30: Spatial Panel Econometrics 31: Unit Roots and Cointegration in Panels 32: Aggregation of Large Panels 33: Theory and Practice of GVAR Modelling Part VII: Appendices A: Mathematics B: Probability and Statistics C: Bayesian Analysis
This book is concerned with recent developments in time series and panel data techniques for the analysis of macroeconomic and financial data. It provides a rigorous, nevertheless user-friendly, account of the time series techniques dealing with univariate and multivariate time series models, as well as panel data models.It is distinct from other time series texts in the sense that it also covers panel data models and attempts at a more coherent integration of time series, multivariate analysis, and panel data models. It builds on the author's extensive research in the areas of time series and panel data analysis and covers a wide variety of topics in one volume. Different parts of the book can be used as teaching material for a variety of courses in econometrics. It can also be used as reference manual.It begins with an overview of basic econometric and statistical techniques, and provides an account of stochastic processes, univariate and multivariate time series, tests for unit roots, cointegration, impulse response analysis, autoregressive conditional heteroskedasticity models, simultaneous equation models, vector autoregressions, causality, forecasting, multivariate volatility models, panel data models, aggregation and global vector autoregressive models (GVAR). The techniques are illustrated using Microfit 5 (Pesaran and Pesaran, 2009, OUP) with applications to real output, inflation, interest rates, exchange rates, and stock prices.
Covers both time series and panel data analysis Covers introductory as well as advanced topics in one volume Comprehensive graduate text that combines theory and practice with many examples and empirical applications All chapters contain supplementary exercises Includes detailed cross references