The role of indicator selection in nowcasting euro-area GDP in pseudo-real time

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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confidence: 99%

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

Kim

Swanson

2017

“…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. Both PLS-and PCR-based methods tend to perform very similarly to ridge regression (Frank and Friedman, 1993); moreover, it can also be shown that both PCR and PLS behave as shrinkage methods (Hastie et al, 2001), as those used by Bai and Ng (2008); Bulligan et al (2012) and Girardi et al (2014), among others. We document that the proposed approach outperforms standard BM alternatives in terms of mean absolute errors (MAE), suggesting that the use of soft data is likely to be helpful to improve forecasts IPI developments.…”

Section: Introductionmentioning

confidence: 86%

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

Girardi

Guardabascio

Ventura

2016

Self Cite

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.

“…Consequently, Bai and Ng (2008) propose targeted indicators using certain preselection methods, and report improvements in the framework of the DFM. Girardi, Golinelli, and Pappalardo (2017) also recently documented that predictions obtained through dimension reduction methods in nowcasting euro area GDP outperform both the benchmark AR and the DFM without any preselection. One of the most popular preselection methods is the least absolute shrinkage and selection operator (lasso) introduced by Tibshirani (1996), which aims to obtain higher prediction accuracy and economic interpretability for estimation in linear models.…”

Section: The Dynamic Factor Model and The Three Refinementsmentioning

confidence: 93%

“…As pointed out by Diebold and Rudebusch (1991), forecasting performance based on revised data can be substantially better than that based on real-time data. However, a historical simulation aimed at comparing the relative forecasting performance under alternative models should not be greatly affected by using revised data, as argued in Girardi et al (2017); see also Bernanke and Boivin (2003); Schumacher and Breitung (2008). 9 In a previous version of the paper, we used a recursive window method for our empirical simulation exercise (which is available upon request).…”

Section: Nowcasting Exercisementioning

confidence: 99%

A note on the predictive power of survey data in nowcasting euro area GDP

Kurz‐Kim¹

2019

This paper investigates the trade‐off between timeliness and quality in nowcasting practices. This trade‐off arises when the frequency of the variable to be nowcast, such as gross domestic product (GDP), is quarterly, while that of the underlying panel data is monthly; and the latter contains both survey and macroeconomic data. These two categories of data have different properties regarding timeliness and quality: the survey data are timely available (but might possess less predictive power), while the macroeconomic data possess more predictive power (but are not timely available because of their publication lags). In our empirical analysis, we use a modified dynamic factor model which takes three refinements for the standard dynamic factor model of Stock and Watson (Journal of Business and Economic Statistics, 2002, 20, 147–162) into account, namely mixed frequency, preselections and cointegration among the economic variables. Our main finding from a historical nowcasting simulation based on euro area GDP is that the predictive power of the survey data depends on the economic circumstances; namely, that survey data are more useful in tranquil times, and less so in times of turmoil.