As a unified discipline, econometrics is still relatively young and has been transforming
and expanding very rapidly over the past two decades since an earlier version of
this entry was published in the New Palgrave in 1987. Major advances have taken place
in the analysis of cross sectional data by means of semi-parametric and non-parametric
techniques. Heterogeneity of economic relations across individuals, firms and industries is
increasingly acknowledged and attempts have been made to take them into account either
by integrating out their effects or by modeling the sources of heterogeneity when suitable
panel data exists. The counterfactual considerations that underlie policy analysis and
treatment evaluation have been given a more satisfactory foundation. New time series
econometric techniques have been developed and employed extensively in the areas of
macroeconometrics and finance. Non-linear econometric techniques are used increasingly
in the analysis of cross section and time series observations. Applications of Bayesian
techniques to econometric problems have been given new impetus largely thanks to advances
in computer power and computational techniques. The use of Bayesian techniques
have in turn provided the investigators with a unifying framework where the tasks of forecasting,
decision making, model evaluation and learning can be considered as parts of the
same interactive and iterative process; thus paving the way for establishing the foundation
of “real time econometrics”. See Pesaran and Timmermann (2005a).