Forecasting Economic Time Series by Michael Clements, David Hendry

By Michael Clements, David Hendry

David Hendry is without doubt one of the world's best econometricians, and during this significant new paintings he and Michael Clements supply a longer formal research of monetary forecasting with econometric types: their research builds in lots of of the good points of the genuine international which are frequently missed in conventional, textbook analyses of forecasting. hence, Clements and Hendry may be able to recommend ways that current forecasting practices may be more suitable, in addition to offering a reason for a number of the routine practices of forecasters that experience hitherto lacked a systematic starting place.

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Sufficient conditions for a macroeconometric model to capture the constant features of the economic system, and to forecast reasonably accurately and more reliably than the alternatives are given by the following criteria: (i) it is well specified, which requires its congruence, so that the model fully embodies the available data information; (ii) it dominates alternatives, which is its encompassing ability, namely how well it accounts for the results obtained by rival explanations; (iii) it remains constant even if economic policies change, which is its invariance to regime shifts and structural change; (iv) it is accurately and precisely estimated, to minimize the impact of estimation uncertainty.

The intuitive appeal of such schemes stems from the belief that the future is more likely to resemble the recent, rather than the remote, past. Harvey (1989) shows that such procedures can be given a sound statistical basis since they are derivable from the class of structural time-series models. Alternative forecasting methods 17 when they are estimated. The calculations are relatively straightforward for scalar or vector representations of stationary processes in the absence of parameter uncertainty, but otherwise require recourse to the formulae in Schmidt (1974, 1977) and Calzolari (1981) for estimated parameters, and a rather different approach, developed in chapters 6 and 11, for non-stationary processes.

2. 6 plots the two levels combinations given by /3[xt and f3f2xtj and provides visual support for the proposition that the former is 1(0) while the latter is 1(1). Moreover Cllisjust-S*. 6 Cointegrating vector and non- stationary combination This parameterization is a restricted version of the second-order VAR. 1), but that is peripheral to the aim of this example. 2), denoted by M i (which in this case is also the DGP), is then over-identified relative to URFi, with three over-identifying restrictions.

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