Econometrics is the application of mathematics, statistical methods, and, more recently, computer science, to economic data and is described as the branch of economics that aims to give empirical content to economic relations (M. Hashem Pesaran, 1987). More precisely, it is "the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference."(Samuelson et al., 1954)

Econometrics is the unification of economics, mathematics, and statistics. This unification produces more than the sum of its parts. Econometrics adds empirical content to economic theory allowing theories to be tested and used for forecasting and policy evaluation.

There are recent signs of progress. Work by [Chib (1995)], [Chib and Greenberg (1995)] and others on importing into econometrics Monte Carlo integration methods like importance sampling and Markov Chain Monte Carlo methods, which were applied successfully in other fields, (see Casella and George (1992) and Tierney (1994)), has made possible complete Bayesian analyses of models for which that would have been impossible a few years ago. This approach relaxes the constraint on model complexity somewhat, so that DSGE (Dynamic stochastic general equilibrium) models that tell appealing stories about behaviour can at the same time be complex enough to fit the data about as well as the best Bayesian reduced form VAR’s (Vector Auto-Regression models). Moreover, recent works on the relation of econometric modelling and model choice to policy analysis, for example Brock et al. (2003) and Leeper and Zha, (2001), suggest models for policy evaluation in uncertain economic environments.

Related terms: Dynamic Stochastic General Equilibrium Models, Econometric Modelling


    M. Hashem Pesaran (1987), "Econometrics," The New Palgrave: A Dictionary of Economics, v. 2, p. 8, pp. 8-22

    P. A. Samuelson, T. C. Koopmans, and J. R. N. Stone (1954), "Report of the Evaluative Committee for Econometrica," Econometrica 22(2), p. 142., pp. 141-146

    Chib, S., (1995), Marginal likelihood from the Gibbs output, Journal of the American Statistical Association, 90(432), p. 1313-1321

    Chib, S. and E. Greenberg, (1995), Understanding the Metropolis-Hastings algorithm, The American Statistician, 49(4), p. 327-335

    Casella, G. and E. George, (1992), Explaining the Gibbs sampler, The American Statistician, 46(3), p. 167-174

    Tierney, L., (1994), Markov Chains for Exploring Posterior Distributions, Annals of Statistics, 22, p. 1701-1762

    Brock, W.A., Durlauf, S.N. and K.D. West, (2003), Policy evaluation in uncertain economic environments, Brookings Papers on Economic Activity (1), p. 1-67

    Leeper, E. and T. Zha, (2001), Models policy interventions. Technical report, Indiana University and Federal Reserve Bank of Atlanta

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