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.
Chapter 14 Unit Roots and Cointegration
In this chapter, we turn our attention to models for a particular type of nonstationary time series. For present purposes, the usual deﬁnition of covariance stationarity is too strict. We consider instead an asymptotic version
Signals and Signal Spaces
The goal of this chapter is to give a brief overview of methods for characterizing signals and for describing their properties. Wewill start with a discussion of signal spaces such as Hilbert spaces, normed and metric spaces. Then, the energy density and correlation function of deterministic signals will be discussed. The remainder of this chapter is dedicated to random signals, which are encountered in almost all areas of signal processing. Here, basic concepts such as stationarity, autocorrelation, and power spectral densitywill be discussed.
The great advantage of using panel data over a simple cross-sectional
sample is that one can control for the country-specific fixed effects ai.
Failure to do so leads to biased estimates if these fixed or latent effects are
correlated with the explanatory variables, as is likely to be the case.
However, unfortunately the use of panel data also leads to more compli-
cations if some or all of the variables in the estimating equation follow a
trend over time. Such trending typically implies what econometricians call
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.
Cointegration and error correction
Professor Roy Batchelor
City University Business School, London
& ESCP, Paris
r On the City University system, EVIEWS 3.1 is in
Start/ Programs/ Departmental Software/CUBS
r Analysing stationarity in a single variable using VIEW
r Analysing cointegration among a group of variables
r Estimating an ECM model
r Estimating a VAR-ECM model
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,...