Forecast Failure, Expectations Formation, and the Lucas Critique

Since forecast failure is due to unanticipated large shifts in deterministic factors,'sensible' agents should adopt 'robust forecasting rules'. Unless the model coincides with the generating mechanism, one cannot even prove that causal variables will dominate non-causal in forecasting. In such a non-stationary world, 'rational expectations' do not have an epistemologically-sound basis: agents cannot know how all relevant information enters the joint data density at every point in time. Thus, although econometric models 'break down' intermittently when deterministic shifts occur, that is not due to the Lucas critique and need not impugen their value for policy analyses.

A General Forecast-error Taxonomy

The paper considers the sources of forecast errors and their consequences in an evolving economy subject to structural breaks,forecasting from mis-specified, data-based models. A model-free taxonomy of forecast errors highlights that deterministic shifts are a major cause of systematic forecast failure. Other sources seem to pose fewer problems. The taxonomy embeds several previous model-based taxonomies for VARs, VECMs, and multi-step estimators, and reveals the stringent requirements that rationality assumptions impose on economic agents.

Unpredictability and the Foundations of Economic Forecasting

We revisit the concept of unpredictability to explore its implications for forecasting strategies in a non-stationary world subject to structural breaks, where model and mechanism differ. Six aspects of the role of unpredictability are distinguished, compounding the four additional mistakes most likely in estimated forecasting models. Structural breaks, rather than limited information, are the key problem, exacerbated by conflicting requirements on forecast-error corrections. We consider model transformations and corrections to reduce forecast-error biases, as usual at some cost in increased forecast-error variances. The analysis is illustrated by an empirical application to M1 in the UK.

Cointegration Analysis: An International Enterprise..

Cointegration analysis is truly an international enterprise, with researchers from most continents and major countries participating. You will, of course, recognize that the very word is Danish, in the same sense as menu is English. The history of the concept and related notions, as this is central to understanding its present position in the econometrician’s toolkit is recalled. Then the idea is illustrated with an example of how we conceive of cointegration in the context of an issue such as inflation, which has been the centre of much economic policy and even more theoretical and empirical analysis.

AUTOMATIC TESTS for SUPER EXOGENEITY

We develop a new automatically-computable test for super exogeneity, using a variant of general-to-specific modelling. Based on the recent developments in impulse saturation applied to marginal models under the null that no impulses matter, we select the significant impulses for testing in the conditional. The approximate analytical non-centrality of the test is derived for a failure of invariance and for a failure of weak exogeneity when there is a shift in the marginal model. Monte Carlo simulations confirm the nominal significance levels under the null, and power against the two alternatives.super exogeneity, general-to-specific, test power, indicators, cobreaking

Economic forecasting in a changing world

This article explains the basis for a theory of economic forecasting developed over the past decade by the authors. The research has resulted in numerous articles in academic journals, two monographs, Forecasting Economic Time Series, 1998, Cambridge University Press, and Forecasting Nonstationary Economic Time Series, 1999, MIT Press, and three edited volumes, Understanding Economic Forecasts, 2001, MIT Press, A Companion to Economic Forecasting, 2002, Blackwells, and the Oxford Bulletin of Economics and Statistics, 2005. The aim here is to provide an accessible, non-technical, account of the main ideas. The interested reader is referred to the monographs for derivations, simulation evidence, and further empirical illustrations, which in turn reference the original articles and related material, and provide bibliographic perspective

Non-Parametric Direct Multi-step Estimation for Forecasting Economic Processes

We evaluate the asymptotic and finite-sample properties of direct multi-step estimation (DMS) for forecasting at several horizons. For forecast accuracy gains from DMS in finite samples, mis-specification and non-stationarity of the DGP are necessary, but when a model is well-specified, iterating the one-step ahead forecasts may not be asymptotically preferable. If a model is mis-specified for a non-stationary DGP, omitting either negative residual serial correlation or regime shifts, DMS can forecast more accurately. Monte Carlo simulations clarify the non-linear dependence of the estimation and forecast biases on the parameters of the DGP, and explain existing results.Adaptive estimation, multi-step estimation, dynamic forecasts, model mis-specification.

Combining disaggregate forecasts or combining disaggregate information to forecast an aggregate

To forecast an aggregate, we propose adding disaggregate variables, instead of combining forecasts of those disaggregates or forecasting by a univariate aggregate model. New analytical results show the effects of changing coefficients, mis-specification, estimation uncertainty and mis-measurement error. Forecastorigin shifts in parameters affect absolute, but not relative, forecast accuracies; mis-specification and estimation uncertainty induce forecast-error differences, which variable-selection procedures or dimension reductions can mitigate. In Monte Carlo simulations, different stochastic structures and interdependencies between disaggregates imply that including disaggregate information in the aggregate model improves forecast accuracy. Our theoretical predictions and simulations are corroborated when forecasting aggregate US inflation pre- and post 1984 using disaggregate sectoral data. JEL Classification: C51, C53, E31Aggregate forecasts, Disaggregate information, forecast combination, inflation

Regression Models with Data-based Indicator Variables

OLS estimation of an impulse-indicator coefficient is inconsistent, but its variance can be consistently estimated. Although the ratio of the inconsistent estimator to its standard error has a tdistribution, that test is inconsistent: one solution is to form an index of indicators. We provide Monte Carlo evidence that including a plethora of indicators need not distort model selection, permitting the use of many dummies in a general-to-specific framework. Although White’s (1980) heteroskedasticity test is incorrectly sized in that context, we suggest an improvement. Finally, a possible modification to impulse ‘intercept corrections’ is considered.