The book is organized into six parts:
PartI:Chapters1to7present the classical linearregression model,describeestimation and statistical inference,and discuss the violation of the assumption sunderlying the classical linear regression model.This part also includes an introduction to dynamic economic modelling,and ends with a chapter onpredictability of asset returns. PartII:Chapters8to11deal with a symptotic theory and present the maximum likelihood and generalized method of moments estimation frameworks. PartIII:Chapters12and13provide an introduction to stochastic processes ands pectral density analysis. PartIV:Chapters14to18focus on univariate times eries models and coverstationary ARMA models,unitroop processes,trendandcy clede composition,forecasting and univariatevolatility models. PartV:Chapters19to25consider avariety of reduced form and structural multivariate models,rational expectations models,as well as VARs,vector error corrections,cointegrating VARs, VARX models,impulse response analysis,and multivariate volatility models.
PartVI:Chapters26to33considers panel data models both when the time dimension(T) of the panelsis short,as well as when panels withN (thecross-sectiondimension) andT are large.These chapters cover a wide range of panel data models,starting with static panels with homogenous slopes and graduating to dynamic panels with slope heterogeneity, error cross section dependence,unitroots,andcointegration.