The econometrics of macroeconomic modelling (original) (raw)

Macroeconometric modelling for policy

The Palgrave Handbook of Econometrics, 2009

The first part of this chapter sets out a coherent approach to dynamic macroeconometric model building; the second part demonstrates the approach through building and evaluating a small econometric model; the final part demonstrates various ...

Dissemination of macroeconometric models: Is a new age dawning

Journal of Policy Modeling, 1991

In the history of macroeconomic modeling one particular development can be observed consistently. It is the steadily growing involvement of model users in determining the what, why, and when of model construction, in the present article we are interested in the most recent and most elaborate expression of this phenomenon---the dissemination, to users, of models built by econometric specialists, allowing the users partial or total control over the models. This article discusses various approaches to dissemination and considers the merits of each. From this exploration, and in the light of the experiences of several modelers in the United States, United Kingdom, Canada, and Australia, the article sets out guidelines for successfully disseminating a macroeconometric model.

Reformulating Empirical Macro-econometric Modelling

2000

The policy implications of estimated macro-econometric systems depend on the formulations of their equations, the methodology of empirical model selection and evaluation, the techniques of pol- icy analysis, and their forecast performance. Drawing on recent results in the theory of forecasting, we question the role of 'rational expectations'; criticize a common approach to testing economic theories; show that impulse-response methods

The roles of Mathematical statistics and economic theory in Macroeconometrics

Macroeconometrics draws upon and combines two academic disciplines—Economics and statistics. There is hardly any doubt that statisticians have had a decisive influence on quantitative economics in general and on modern Macroeconometric modelling in particular. . For our purpose, it is central to note that Haavelmo recognized and explained in the context of an economic model, that the joint distribution of all observable variables for the whole sample period provides the most general framework for statistical inference (see Hendry et al. 1989). This applies to specification (op. cit., pp. 48–49), as well as identification, estimation, and hypothesis testing: all come down to one and the same thing, namely to study the properties of the joint probability distribution of random (observable) variables in a stochastic equation system (Haavelmo 1944, p. 85).