Forecast Encompassing and Model Evaluation

Lu, Maozu; Mizon, Grayham E.

doi:10.1007/978-3-662-06824-3_9

Cited by 18 publications

(20 citation statements)

References 19 publications

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“…we look for (MSFE) forecast encompassing, see, e.g. Chong and Hendry (1986), Lu and Mizon (1991), Ericsson (1992), Harvey et al (1998), andMarcellino (2000). Harvey et al (1998) suggested that f i MSFE encompasses f j when λ = 0 in…”

Section: Oecd and Ec Forecastsmentioning

confidence: 99%

Fiscal forecasting: The track record of the IMF, OECD and EC

Artis

Marcellino

2001

The Econometrics Journal

114

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Section: Oecd and Ec Forecastsmentioning

confidence: 99%

Fiscal forecasting: The track record of the IMF, OECD and EC

Artis

Marcellino

2001

The Econometrics Journal

114

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“…The need to pool violates encompassing (see Lu and Mizon, 1991), so reveals non-congruence, but it was shown above that congruence per se could not be established as a necessary feature for good forecasts. Hendry and Clements (2001g) show that no proof is possible of whether or not to include purely non-encompassed models when pooling, since all others are inferentially redundant only over the sample, not necessarily over the forecast period when deterministic shifts occur.…”

Section: Pooling Of Forecastsmentioning

confidence: 99%

Economic forecasting: some lessons from recent research

Hendry

Clements

2003

Economic Modelling

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This paper describes some recent advances and contributions to our understanding of economic forecasting. The framework we develop helps explain the findings of forecasting competitions and the prevalence of forecast failure. It constitutes a general theoretical background against which recent results can be judged. We compare this framework to a previous formulation, which was silent on the very issues of most concern to the forecaster. We describe a number of aspects which it illuminates, and draw out the implications for model selection. Finally, we discuss the areas where research remains needed to clarify empirical findings which lack theoretical explanations.

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“…the figures below. Sometimes, properties of forecasts are summarized, as in Chow (1960) statistics and forecast-encompassing statistics; see Chong andHendry (1986), Fair andShiller (1989), Lu andMizon (1991), Ericsson (1992), and Ericsson and Marquez (1993) on the latter. 4…”

Section: Post-evaluation Analysismentioning

confidence: 99%

Computationally attractive stability tests for the efficient method of moments

Ericsson

1998

The Econometrics Journal

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This paper proposes a tripartite framework of design, evaluation, and postevaluation analysis for generating and interpreting economic forecasts. This framework's value is illustrated by re-examining mean square forecast errors from dynamic models and nonlinearity biases from empirical forecasts of US external trade. Previous studies have examined properties such as nonlinearity bias and the possible nonmonotonicity and nonexistence of mean square forecast errors in isolation from other aspects of the forecasting process, resulting in inefficient forecasting techniques and seemingly puzzling phenomena. The framework developed reveals how each such property follows from systematically integrating all aspects of the forecasting process.A framework for economic forecasting C229 forecast errors for certain estimation sample sizes and forecast horizons; the uncertainty arising from Monte Carlo simulation of mean square forecast errors when exact analytical results are unavailable; and a parallel uncertainty in estimating the bias from using deterministic forecasts from nonlinear models. Each case is of substantive economic and methodological interest, and each shows how the proposed framework clarifies and improves upon existing results and techniques.This paper is organized as follows. Section 2 develops the general framework, which partitions forecast activities into design, evaluation, and post-evaluation analysis. Design includes specification of the model for forecasting and selection of the forecast's characteristics of interest. Thus, design includes choice of the variables being forecast, the forecast horizon, the model's specification, and estimation method. Evaluation specifies how the forecasts are actually generated, and includes the choice of analytical or numerical techniques and the type of approximation used. Post-evaluation analysis includes presentation and summarization of the forecasts.Sections 3 and 4 illustrate the principles of design, evaluation, and post-evaluation analysis for some time-series models that have been studied previously. That choice emphasizes how existing results can be beneficially re-interpreted in the proposed framework. Section 3 presents analytical properties of the mean square forecast error (MSFE) for one-step and multi-step ahead forecasts from vector autoregressions, relying on approximations due to Schmidt (1974) and Baillie (1979b). While the general formula for the MSFE is useful for empirical applications within this class of models, many of its properties can be most easily understood for a special case, the univariate first-order autoregressive [AR(1)] process. As detailed below, Section 4 discusses three potential properties of the MSFE for the AR(1) model: nonmonotonicity, nonexistence, and uncertainty in its estimation by Monte Carlo simulation.Firstly, Section 4.1 re-interprets Hoque et al. 's (1988) exact numerical results on the MSFE for an AR(1) model without an intercept. Mean square forecast errors are commonly viewed as being monotonic in the forecast...

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Forecast Encompassing and Model Evaluation

Cited by 18 publications

References 19 publications

Fiscal forecasting: The track record of the IMF, OECD and EC

Fiscal forecasting: The track record of the IMF, OECD and EC

Economic forecasting: some lessons from recent research

Computationally attractive stability tests for the efficient method of moments

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