Chapter 4 Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change

This paper extends Pesaran's (2006) work on common correlated e¤ects (CCE) estimators for large heterogeneous panels with a general multifactor error structure by allowing for unknown common structural breaks. Structural breaks due to new policy implementation or major technological shocks, are more likely to occur over a longer time span. Consequently, ignoring structural breaks may lead to inconsistent estimation and invalid inference. We propose a general framework that includes heterogeneous panel data models and structural break models as special cases. The least squares method proposed by Bai (1997aBai ( , 2010 is applied to estimate the common change points, and the consistency of the estimated change points is established. We …nd that the CCE estimator have the same asymptotic distribution as if the true change points were known. Additionally, Monte Carlo simulations are used to verify the main results of this paper.

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“…Thus, similar to the result M f F = 0, by (19) it is expected that the terms involving M w F are ignorable asymptotically as N ! 1.…”

Section: T P Vectorsupporting

confidence: 61%

Estimation of heterogeneous panels with structural breaks

Baltagi

Feng

Kao

2016

Journal of Econometrics

116

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“…For example, by means of a Monte Carlo study, Banerjee et al (2008) conclude that, in contrast to Stock and Watson's diagnosis, the instability of factor loadings when big (i.e., not local-to-zero) breaks occur is the most likely reason behind the worsening factor-based forecasts, particularly in small samples. Likewise, when discussing Stock and Watson's research on this topic, Giannone (2007) argues that ''.…”

Section: Introductionmentioning

confidence: 95%

Detecting big structural breaks in large factor models

Liang

Dolado

Gonzalo

2014

Journal of Econometrics

121

107

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a b s t r a c tTime invariance of factor loadings is a standard assumption in the analysis of large factor models. Yet, this assumption may be restrictive unless parameter shifts are mild (i.e., local to zero). In this paper we develop a new testing procedure to detect big breaks in these loadings at either known or unknown dates. It relies upon testing for parameter breaks in a regression of one of the factors estimated by Principal Components analysis on the remaining estimated factors, where the number of factors is chosen according to Bai and Ng's (2002) information criteria. The test fares well in terms of power relative to other recently proposed tests on this issue, and can be easily implemented to avoid forecasting failures in standard factor-augmented (FAR, FAVAR) models where the number of factors is a priori imposed on the basis of theoretical considerations.

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“…It, however, appears that the consensus in the empirical literature is leaning towards defining outliers as observations exceeding the median of the series by more than six times the interquartile range. Examples of this include Banerjee, Marcellino, and Masten (2008), Breitung and Eickmeier (2011), Stock and Watson (2009), 1 and Artis, Banerjee, and Marcellino (2005). In this paper we wish to challenge the common practice of screening for outliers.…”

Section: Introductionmentioning

confidence: 99%

Factor-based forecasting in the presence of outliers: Are factors better selected and estimated by the median than by the mean?

Kristensen

2014

Studies in Nonlinear Dynamics &Amp; Econometrics

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Macroeconomic forecasting using factor models estimated by principal components has become a popular research topic with many both theoretical and applied contributions in the literature. In this paper we attempt to address an often neglected issue in these models: The problem of outliers in the data. Most papers take an ad-hoc approach to this problem and simply screen datasets prior to estimation and remove anomalous observations. We investigate whether forecasting performance can be improved by using the original unscreened dataset and replacing principal components with a robust alternative. We propose to use an estimator based on least absolute deviations (LAD) as this alternative and establish a tractable method for computing the estimator. In addition to this we demonstrate the robustness features of the estimator through a number of Monte Carlo simulation studies. Finally, we apply the estimator in a simulated real-time forecasting exercise to test its merits. We use a newly compiled dataset of US macroeconomic series spanning the period 1971:2-2012:10. Our findings suggest that the chosen treatment of outliers does affect forecasting performance and that in many cases improvements can be made using a robust estimator such as the proposed LAD estimator.

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Chapter 4 Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change

Cited by 64 publications

References 20 publications

Estimation of heterogeneous panels with structural breaks

Estimation of heterogeneous panels with structural breaks

Detecting big structural breaks in large factor models

Factor-based forecasting in the presence of outliers: Are factors better selected and estimated by the median than by the mean?

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