Forecasting and nowcasting economic growth in the euro area using factor models

Hindrayanto, Irma; Koopman, Siem Jan; Winter, Jasper de

doi:10.1016/j.ijforecast.2016.05.003

Cited by 23 publications

(14 citation statements)

References 36 publications

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“…This suggestion leads to a much lower-dimensional model with much smaller number of parameters and a smaller computational burden for estimation. The estimation process is also easier (as a smaller number of parameters are involved) and may potentially provide more accurate forecasts, given that the model is more parsimonious; see Bräuning and Koopman (2014) and Hindrayanto et al (2016).…”

Section: Collapsed Structural Augmented Dynamic Factor Modelmentioning

confidence: 99%

Modeling, forecasting, and nowcasting U.S. CO2 emissions using many macroeconomic predictors

2021

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We propose a structural augmented dynamic factor model for U.S. CO 2 emissions. Variable selection techniques applied to a large set of annual macroeconomic time series indicate that CO 2 emissions are best explained by industrial production indices covering manufacturing and residential utilities sectors. We employ a dynamic factor structure to explain, forecast, and nowcast the industrial production indices and thus, by way of the structural equation, emissions. We show that our model has good in-sample properties and out-of-sample performance in comparison with univariate and multivariate competitor models. Based on data through September 2019, our model nowcasts a reduction of about 2.6% in U.S. CO 2 emissions in 2019 compared to 2018 as the result of a reduction in industrial production in residential utilities.

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Section: Collapsed Structural Augmented Dynamic Factor Modelmentioning

confidence: 99%

Modeling, forecasting, and nowcasting U.S. CO2 emissions using many macroeconomic predictors

2021

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“…(2011), which combines factor models with the Kalman filter, to deal both with the high‐dimensionality of the auxiliary series and with the estimation of the state space model. The above‐mentioned estimator is generally used to improve the nowcast of variables that are observed such as GDP (see Giannone et al., 2008; Hindrayanto et al., 2016 for applications to the United States and the euro area), which is not the case for the unemployment. Nonetheless, D’Amuri and Marcucci (2017), Naccarato et al.…”

Section: Introductionmentioning

confidence: 99%

“…As the number of search terms related to unemployment can easily become large, we employ the two-step estimator of Doz et al (2011), which combines factor models with the Kalman filter, to deal both with the high-dimensionality of the auxiliary series and with the estimation of the state space model. The above-mentioned estimator is generally used to improve the nowcast of variables that are observed such as GDP (see Giannone et al, 2008;Hindrayanto et al, 2016 for applications to the United States and the euro area), which is not the case for the unemployment. Nonetheless, D'Amuri and Marcucci (2017), Naccarato et al (2018) and Maas (2019) are all recent studies that use Google Trends to nowcast and forecast the unemployment, by treating the latter as known dependent variable in time series models where the Google searches are part of the explanatory variables.…”

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confidence: 99%

A Dynamic Factor Model Approach to Incorporate Big Data in State Space Models for Official Statistics

Schiavoni

Palm

Smeekes

et al. 2020

Journal of the Royal Statistical Society Series A: Statistics in Society

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In this paper we consider estimation of unobserved components in state space models using a dynamic factor approach to incorporate auxiliary information from high‐dimensional data sources. We apply the methodology to unemployment estimation as done by Statistics Netherlands, who uses a multivariate state space model to produce monthly figures for unemployment using series observed with the labour force survey (LFS). We extend the model by including auxiliary series of Google Trends about job‐search and economic uncertainty, and claimant counts, partially observed at higher frequencies. Our factor model allows for nowcasting the variable of interest, providing reliable unemployment estimates in real‐time before LFS data become available.

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“…(), Angelini et al . (), Bessec (), Liebermann (), Hindrayanto, Koopman and de Winter () and Jansen et al . () use three‐month differences, while Bańbura et al .…”

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confidence: 99%

Combining Model‐Based Near‐Term GDP Forecasts and Judgmental Forecasts: A Real‐Time Exercise for the G7 Countries

Jansen

Winter

2018

Oxf Bull Econ Stat

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We investigate the effects of combining model‐based near‐term GDP forecasts and judgmental (quarterly) forecasts by professional analysts (Consensus forecasts) in a real‐time setting for the G7 countries over the years 1999–2013. Model‐based forecasts are produced by a dynamic factor model (DFM). We consider as combination schemes the weighted average and the linear combination. Combining with subjective information delivers sizable gains in forecasting ability of statistical models for all countries except Japan, even when subjective forecasts are somewhat dated. Accuracy gains are much more pronounced in the volatile period after 2008 due to a marked improvement in predictive power of Consensus forecasts relative to the DFM. A possible explanation is that mechanical models may be more vulnerable to extreme observations in estimation samples. Consensus forecasts are superior at the moment of publication for most countries since 2008. For some countries forecast combinations can improve upon Consensus forecasts in between the quarterly release dates of the Consensus survey.

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Forecasting and nowcasting economic growth in the euro area using factor models

Cited by 23 publications

References 36 publications

Modeling, forecasting, and nowcasting U.S. CO2 emissions using many macroeconomic predictors

Modeling, forecasting, and nowcasting U.S. CO2 emissions using many macroeconomic predictors

A Dynamic Factor Model Approach to Incorporate Big Data in State Space Models for Official Statistics

Combining Model‐Based Near‐Term GDP Forecasts and Judgmental Forecasts: A Real‐Time Exercise for the G7 Countries

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