The book (edited in French) is
built on the previous editions. This, the
seventh edition, adds a new chapter to
the previous –one, “Introduction to
econometric Panel- data analysis
This new edition book is
structured in 13 chapters in which there
are combined in a well-balanced
measure the theoretical and practical
In the first chapter there are
briefly presented the concepts, ideas,
definitions, role, advantages and
disadvantages of econometrics.
After the general overview of
econometrics approach the second
chapter presents in a detailed but simply
way the simple regression model.
Studying examples, brief mathematical
demonstration combined with economical
application the reader can obtain solid information about parameters estimations, effects
and tests of the regression model’s hypotheses, about the validity and quality of the
regression model and in the end of chapter we may find the description of one goal of the
econometric approach: the forecast.
The third chapter extends the econometric analysis to the multiple regression. In
the eight subchapters there are presented general problems of the multiple regression such
as: parameters estimation, statistical tests, analysis of variance and forecasting, and also some particular discussion such as: the influence and role of binary variable, the stability and
model specification tests. The chapters end with a set of exercises which helps the reader to
complete and consolidate his knowledge about econometric extended application.
In the chapters four and five the author describes in an extended way the
violation of the simple and multiple regression hypotheses. In the fourth chapter there is a
focus on the partial correlation and multiple correlation. There is a special approach of the
multicollinearity process by treating presence/detection, effects and possibility of correction
→ optimal model selection. In the next chapter the focus is changed to autocorrelation,
heteroskedascity and variables errors. These problems are treated in sense of detection,
effects and possibility of avoiding/eliminations or at least of reduction of the negative effects.
A short chapter about non-linears models is the sixth chapter. The chapters has a
general overview about parameters estimation with particular analyses about exceptions
given by non-linear approach. Here we can meet the terms of exponential and polynomial
regression function and also the diffusion models.
In the chapter seven there are presented the LAG models and their particularities
regarding specification, estimation, hypotheses testing and prediction.
Times series in a detailed description are presented in the chapters nine, ten and
eleven. The description starts with an introduction in time series. The reader will be familiar
with concepts like white noise, unit root, stationary process, random walk, non-stationary
process, autoregressive (AR), moving averages (MA), ARMA, ARIMA, SARIMA models and
Box-Jenkins methodology for model identification . The models VAR, ARMAX and Granger or
Sims causality with their particularities are described in the tenth chapter. Concepts tests and
model estimation about cointegrated series can be found in the eleventh chapter.
An introduction in econometric approach with qualitative variables is described in
the twelfth chapter. Here there are presented the particularities of the models which
include qualitative variables with more specified cases for the Logit, Probit or Tobit models.
In the last chapter, the thirteenth can be found an introduction in panel data
econometric analysis. A short presentation of model specifications, homogeneity tests and
parameters estimation is also described in a global form. All the examples are made with
specific programs used in econometric analysis like RATS, EXCEL, Eviews, etc.
The book ends with a case study and a collection of exercises.
The combination between mathematic theories, economic implication, case studies
along with general or particular exercises recommend the book to a large area of readers
from many domains.