The aim of this textbook is to provide a step-by-step guide to financial econometrics using EViews 6.0 statistical package. It contains brief overviews of econometric concepts, models and data analysis techniques followed by empirical examples of how they can be implemented in EViews.
This book is written as a compendium for undergraduate and graduate students in economics and finance. It also can serve as a guide for researchers and practitioners who desire to use EViews for analysing financial data.
Only statistical inference
with stationary variables provides valid results. In simple words, this is
because if variables are non-stationary then any correlation between the
explanatory and the dependent variable could be due to the trending in
both variables that is caused by a third variable not included in the model.
We tested for the non-stationarity of the variables in our model formally
with the help of Levin, Lin and Chu’s (2002) unit root test for panel data.
The rest of the paper is set out as follows. Section 2 provides an
overview of the theoretical arguments that motivate nonlinear mean-
reverting behavior in the futures basis. Section 3 discusses the class of
nonlinear models employed for modeling the futures basis. Section 4
describes the data set.
The structure of the paper is as follows. Section 2 discusses the literature on both measuring
competition and the bank interest rate pass-through. Section 3 describes the Boone indicator of
competition and Section 4 the employed interest rate pass-through model of the error-correction type
and the applied panel unit root and cointegration tests. Section 5 presents the various data sets used.
The results on the various tests and estimates of the spread model and the error correction model
equations are shown in Section 6. Finally, Section 7 summarises and concludes....
In this chapter, students will be able to understand: Highlight the problems that may occur if non-stationary data are used in their levels form, test for unit roots, examine whether systems of variables are cointegrated, estimate error correction and vector error correction models, explain the intuition behind Johansen’s test for cointegration,...
A growing empirical literature examines the relationship between family income and child
health. An article by Case, Lubotsky and Paxson (2002) (CLP) shows that, in the United States,
the socioeconomic gradient in adult health has its origins in childhood. Using data from 1986 to
1994, they find that poor children are reported by their parents to be in worse health than wealthy
children, and this gradient becomes larger as children grow older. These results suggest that the
relationship between income and health that is observed in adulthood has its roots in childhood.