A medium-N approach to macroeconomic forecasting

Cubadda, Gianluca; Guardabascio, Barbara

doi:10.1016/j.econmod.2012.03.027

Cited by 19 publications

(11 citation statements)

References 20 publications

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“…In particular, F t are computed by using three different approaches: PLS (Cubadda and Guardabascio, 2012), PCR and PCR SW Watson, 2002a, 2002b). The PLS method first standardizes all series.…”

Section: The Empirical Frameworkmentioning

confidence: 99%

“…The benchmark window size consists of 240 observations (with the first estimation sample covering the sample from 1987:M9 to 2007:M8). The choice of a relatively large estimation span has been dictated by the fact that the PLS method requires a sizable number of degrees of freedom to reach consistency (Cubadda and Guardabascio, 2012). 6 The training sample is set to 24 months, from 2007:M9 to 2009:M8, while the forecast exercise embraces the period 2009:M9-2013: M8 (corresponding to a horizon of 48 months).…”

Section: The Baseline Casementioning

confidence: 99%

“…Factors are extracted by using different techniques: partial least squares (PLS) (Cubadda and Guardabascio, 2012), as well as principal component regression (PCR) and Stock and Watson's dynamic principal component (PCR SW ) Watson, 2002a, 2002b). While the weighting scheme resulting from the PLS method is such that the covariance between the factors and the target variable is maximized, PCR-based alternatives aim at producing linear combinations of the individual series that maximize the explained variance of the entire panel of regressors.…”

Section: Introductionmentioning

confidence: 99%

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Factor‐Augmented Bridge Models (FABM) and Soft Indicators to Forecast Italian Industrial Production

Girardi

Guardabascio

Ventura

2016

Journal of Forecasting

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This paper presents a new forecasting approach straddling the conventional methods applied to the Italian industrial production index. Specifically, the proposed method treats factor models and bridge models as complementary ingredients feeding a unique model specification. We document that the proposed approach improves upon traditional bridge models by making efficient use of the information conveyed by a large amount of survey data on manufacturing activity. Different factor algorithms are compared and, under the provision that a large estimation window is used, partial least squares outperform principal component-based alternatives.

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Section: The Empirical Frameworkmentioning

confidence: 99%

Section: The Baseline Casementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Factor‐Augmented Bridge Models (FABM) and Soft Indicators to Forecast Italian Industrial Production

Girardi

Guardabascio

Ventura

2016

Journal of Forecasting

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“…The fourth factor, denoted PLS, is the first partial least squares factor, whose weights are obtained as the eigenvector corresponding to the largest eigenvalue of Γ′Γ, where Γ is the matrix of the covariances between elements of Y t and Y t − 1 after having standardized them to unit variance. Cubadda and Guardabascio (2012) discuss the conditions under which PLS is a consistent estimator of the common factor when only the sample size T diverges. Moreover, Cubadda and Hecq (2011) provide evidence that PLS are capable to identify a common cycle even when the sample size is small compared to the number of series.…”

Section: Clustering European Economies On Business Cycle Co-movementsmentioning

confidence: 99%

A general to specific approach for constructing composite business cycle indicators

2013

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This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. Combining economic time series with the aim to obtain an indicator for business cycle analyses is an important issue for policy makers. In this area, econometric techniques usually rely on systems with either a small number of series, N, or, at the other extreme, a very large N. In this paper we propose tools to select the relevant business cycle indicators in a "medium" N framework, a situation that is likely to be the most frequent in empirical works. An example is provided by our empirical application, in which we study jointly the short-run co-movements of 24 European countries. We show, under not too restrictive conditions, that parsimonious single-equation models can be used to split a set of N countries in three groups. The first group comprises countries that share a synchronous common cycle, a non-synchronous common cycle is present among the countries of the second group, and the third group collects countries that exhibit idiosyncratic cycles. Moreover, we offer a method for constructing a composite coincident indicator that explicitly takes into account the existence of these various forms of short-run co-movements among variables.

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“…As an alternative to CCA we use a PLS algorithm similar to the one used in Cubadda and Hecq (2011) or Cubadda and Guardabascio (2012). In order to make the solution of this eigenvalue problem invariant to scale changes of individual elements, we compute the eigenvectors associated with the largest eigenvalues of the matrix…”

Section: Estimating the Factorsmentioning

confidence: 99%

Testing for Granger causality in large mixed-frequency VARs

Götz

Hecq

Smeekes

2016

Journal of Econometrics

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Reproduction permitted only if source is stated.ISBN 978-3-95729-217-9 (Printversion) Non-technical summary Research QuestionWhen dealing with time series sampled at various frequencies it has become common practice to directly incorporate high-frequency information into the econom(etr)ic model at hand. These specifications were first restricted to the single regression case; with the development of the (stacked) mixed-frequency vector autoregressive (MF-VAR) system (Ghysels, 2015) it is now possible to treat all series similarly and investigate causal effects between them. However, if the difference in frequencies between the series involved is large (as, e.g., in a month/working day scenario), estimation accuracy of the system coefficients is exacerbated, implying the detection of causal effects to be potentially inaccurate. To overcome this issue various parameter reduction techniques are introduced and analyzed. These methods are then evaluated in terms of their ability to detect causality patterns between the series under consideration in the resulting restricted model. ContributionTwo parameter reduction techniques are discussed in detail: three reduced rank regression (RRR) model variants and a Bayesian MF-VAR. Using a Monte Carlo experiment both approaches are compared in terms of their Granger causality testing behavior with an unrestricted VAR, a (time-aggregated) low-frequency VAR and the max-test (Ghysels, Motegi, and Hill, 2015a). To further enhance their finite sample properties we develop and evaluate (whenever possible) two bootstrap variants of these tests. Finally, the methods are applied to U.S. data by investigating channels of causality between the monthly growth rate of the industrial production index (IPI) and daily bipower variation (BV) of the S&P500 index. ResultsWe find that, depending on the direction of causality under consideration, a different set of tests results in the best Granger non-causality testing behavior. For the direction from the high-to the low-frequency series, standard testing within the Bayesian MF-VAR, the max-test and the restricted bootstrap version of the Wald test in two RRR model versions performs best. For the reverse direction, the unrestricted bootstrap variants of the Bonferroni-corrected Wald tests within the unrestricted VAR and the RRR models dominate. As far as our application is concerned, Granger causality from BV to IPIgrowth is clearly supported by the data; evidence for causality in the reverse direction, however, only comes from a subset of tests. Maastricht UniversityAbstract We analyze Granger causality testing in a mixed-frequency VAR, where the difference in sampling frequencies of the variables is large. Given a realistic sample size, the number of high-frequency observations per low-frequency period leads to parameter proliferation problems in case we attempt to estimate the model unrestrictedly. We propose several tests based on reduced rank restrictions, and implement bootstrap versions to account for the uncertainty when estimating fac...

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A medium-N approach to macroeconomic forecasting

Cited by 19 publications

References 20 publications

Factor‐Augmented Bridge Models (FABM) and Soft Indicators to Forecast Italian Industrial Production

Factor‐Augmented Bridge Models (FABM) and Soft Indicators to Forecast Italian Industrial Production

A general to specific approach for constructing composite business cycle indicators

Testing for Granger causality in large mixed-frequency VARs

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