A Medium-N Approach to Macroeconomic Forecasting

Cubadda, Gianluca; Guardabascio, Barbara

doi:10.2139/ssrn.1722638

Cited by 10 publications

(17 citation statements)

References 46 publications

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“…Theoretical and empirical research in this area, however, is left to future research. For related discussion of some of these methods, see Cubadda and Guardabascio (2012).…”

Section: Estimating Diffusion Indexesmentioning

confidence: 99%

Methods for backcasting, nowcasting and forecasting using factor‐MIDAS: With an application to Korean GDP

Kim

Swanson

2017

Journal of Forecasting

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We utilize mixed‐frequency factor‐MIDAS models for the purpose of carrying out backcasting, nowcasting, and forecasting experiments using real‐time data. We also introduce a new real‐time Korean GDP dataset, which is the focus of our experiments. The methodology that we utilize involves first estimating common latent factors (i.e., diffusion indices) from 190 monthly macroeconomic and financial series using various estimation strategies. These factors are then included, along with standard variables measured at multiple different frequencies, in various factor‐MIDAS prediction models. Our key empirical findings as follows. (i) When using real‐time data, factor‐MIDAS prediction models outperform various linear benchmark models. Interestingly, the “MSFE‐best” MIDAS models contain no autoregressive (AR) lag terms when backcasting and nowcasting. AR terms only begin to play a role in “true” forecasting contexts. (ii) Models that utilize only one or two factors are “MSFE‐best” at all forecasting horizons, but not at any backcasting and nowcasting horizons. In these latter contexts, much more heavily parametrized models with many factors are preferred. (iii) Real‐time data are crucial for forecasting Korean gross domestic product, and the use of “first available” versus “most recent” data “strongly” affects model selection and performance. (iv) Recursively estimated models are almost always “MSFE‐best,” and models estimated using autoregressive interpolation dominate those estimated using other interpolation methods. (v) Factors estimated using recursive principal component estimation methods have more predictive content than those estimated using a variety of other (more sophisticated) approaches. This result is particularly prevalent for our “MSFE‐best” factor‐MIDAS models, across virtually all forecast horizons, estimation schemes, and data vintages that are analyzed.

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“…Theoretical and empirical research in this area, however, is left to future research. For related discussion of some of these methods, see Cubadda and Guardabascio (2012).…”

Section: Estimating Diffusion Indexesmentioning

confidence: 99%

Methods for backcasting, nowcasting and forecasting using factor‐MIDAS: With an application to Korean GDP

Kim

Swanson

2017

Journal of Forecasting

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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%

“…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%

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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“…Cubadda and Guardabascio (2010) showed that under the same condition single-equation partial least squares provide consistent estimates of a stable autoregressive distributed lag model.…”

mentioning

confidence: 93%

“…PLS, introduced by Wold (1985), are a family of multivariate techniques with the aim of maximizing the covariance between linear combinations of two variable sets; see, for example, Rosipal and Krämer (2006) for a recent survey. Groen and Kapetanios (2008) and Cubadda and Guardabascio (2010) have recently documented that PLS have superior forecasting performances to better-known data-rich prediction methods such as principal component and ridge regressions. This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%