Ebook: Time series and panel data econometrics
Author: Pesaran M. Hashem
- Tags: Econometrics, Macroeconomics--Mathematical models, Electronic books, Macroeconomics -- Mathematical models
- Year: 2016
- Publisher: Oxford University Press
- City: Oxford
- Edition: First edition
- Language: English
- pdf
This work describes and illustrates many advances that have taken place in a number of areas in theoretical and applied econometrics over the past four decades.;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 Efficient Market Hypothesis -- 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 Multivariate 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 -- Chapter 21 Vector Autoregressive Models -- Chapter 22 Cointegration Analysis -- Chapter 23 Varx Modelling -- Chapter 24 Impulse Response Analysis -- Chapter 25 Modelling the Conditional Correlation of Asset Returns -- Part VI Panel Data Econometrics -- Chapter 26 Panel Data Models with Strictly Exogenous Regressors -- Chapter 27 Short T Dynamic Panel Data Models -- Chapter 28 Large Heterogeneous Panel Data Models -- Chapter 29 Cross-Sectional Dependence in Panels -- Chapter 30 Spatial Panel Econometrics -- Chapter 31 Unit Roots and Cointegration in Panels -- Chapter 32 Aggregation of Large Panels -- Chapter 33 Theory and Practice of GVAR Modelling.
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