A comparison of monthly global indicators for forecasting growth

Baumeister, Christiane; Guérin, Pierre

doi:10.1016/j.ijforecast.2021.02.008

Cited by 28 publications

(6 citation statements)

References 43 publications

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“…Third, our study contributes to the scarce but rapidly growing body of work on modelling economic growth in a mixed-frequency context (see, Baumeister & Guérin, 2021;Clements & Galvão, 2008;Enilov & Wang, 2022;Ferrara & Marsilli, 2019;Liu & Song, 2018). The advantage of this mixed-frequency approach is in its explicit dealing with the issue of temporal aggregation.…”

Section: Introductionmentioning

confidence: 92%

The predictive power of commodity prices for future economic growth: Evaluating the role of economic development

Enilov

2023

Int. J Fin Econ

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This paper investigates the extent to which commodity prices can predict economic growth, the growth exposure of the countries to specific commodity group, and whether the country stage of development matters for growth dependency on commodities. We use a novel time‐varying mixed‐frequency vector autoregressive model to jointly estimate causality between monthly commodity prices and quarterly economic growth for the period from January 1980 to March 2020. Our findings suggest that growth dependency on commodities varies over time and across different synthetic measures, but in general solid evidence of predictability is determined. The overall commodity index shows better performance in predicting economic growth than the segregated proxies of either fuel or non‐fuel commodities in developed economies. However, the overall commodity index is as effective as the fuel index in predicting economic growth for their developing counterparts. We develop a new index of global commodity growth connectivity (GCGCI) to determine the economic growth dependency on commodities over time. Our results show that economic growth dependency on commodities has increased at least three times with financialization of commodity markets. The GCGCI for all commodities reveals a higher growth dependency on commodities in developing countries than developed ones in the post‐financialization period. Interestingly, we find that growth decreases its reliance on fuel commodities in both developed and developing countries after financialization of commodity markets. Our findings bear implications for policymakers to design stabilization policies in an attempt to protect economies vulnerable to commodity price shocks and lay the foundation for sustainable economic growth.

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Section: Introductionmentioning

confidence: 92%

The predictive power of commodity prices for future economic growth: Evaluating the role of economic development

Enilov

2023

Int. J Fin Econ

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“…More specifically, we consider the real productivity growth (RPOD), the growth in car registrations (CREG), the rate of unemployment (UNEM), the consumer inflation rate (Consumer Price Index [CPI]), the producer inflation rate (Producer Price Index [PPI]), the growth in construction production (Construction Volume Index of Production [CONPROD]), long‐term interest rates (LONGR), equity market returns (STOCK), and the change in the OECD Leading Indicator (LEAD). Similar factors have been employed in various business cycle‐related studies: Baumeister and Guérin (2021), Berger et al (2023), Cimadomo et al (2022), and Morley and Wong (2020). Table A1 reports the ultimate set of factors, as well as their corresponding transformations, that is, the underlying series are transformed by either taking differences in the natural logarithms (

normalΔ l n

) or the first difference (

normalΔ l v

) to achieve stationarity.…”

Section: Data and Empirical Analysismentioning

confidence: 99%

Forecasting GDP growth: The economic impact of COVID‐19 pandemic

Vrontos,

Galakis,

Panopoulou

et al. 2024

Journal of Forecasting

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The primary goal of this study is to effectively measure the impact of a severe random shock, such as the COVID‐19 pandemic on aggregate economic activity in Greece, seven other euro area economies, three Scandinavian countries, and the United States. The class of linear and quantile predictive regression models is proposed for the analysis of real gross domestic product (GDP) growth, and a Bayesian approach for model selection is developed, by using a computationally flexible Markov chain Monte Carlo stochastic search algorithm that explores the posterior distribution of linear and quantile models, and identifies the relevant predictor variables. Penalized likelihood regression models are also implemented to tackle the issue of model selection. The model confidence set approach is applied and verifies that the selected models identified by the stochastic search algorithm belong to the set of superior models. Our analysis confirms that the outbreak of the pandemic had a profound effect on the economies under study, and reveals that different predictor variables are able to explain different quantiles of the underlying real GDP growth distribution for analyzed countries, suggesting that the quantile modeling approach improves the ability to adequately explain real GDP series compared with the standard conditional mean approach that explains only the average of the relationship between real GDP growth and several predictor variables.

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“…In addition, other nine country-specific explanatory variables are taken into account, i.e., real productivity, car registrations, the consumer inflation rate, the producer inflation rate, the construction production index, long interest rates, equity market returns, and the Organization for Economic Cooperation and Development (OECD) leading indicator. Thus, the set of country-specific covariates contains macroeconomic and financial-marketrelated indicators, the majority of which are widely followed by both policymakers and practitioners, and have been used in the existing literature, see, for example, Morley and Wong (2020), Baumeister and Guérin (2021), Chudik et al (2021), Cimadomo et al (2022), Berger et al (2023), and Vrontos et al (2024). The list of predictor variables/factors and their transformations to achieve stationarity are presented in Table 1.…”

Section: Datamentioning

confidence: 99%

Modeling the Economic Impact of the COVID-19 Pandemic Using Dynamic Panel Models and Seemingly Unrelated Regressions

Vrontos,

Galakis,

Panopoulou

et al. 2024

Econometrics

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The importance of assessing and estimating the impact of the COVID-19 pandemic on financial markets and economic activity has attracted the interest of researchers and practitioners in recent years. The proposed study aims to explore the pandemic’s impact on the economic activity of six Euro area economies. A class of dynamic panel data models and their corresponding Seemingly Unrelated Regression (SUR) models are developed and applied to model the economic activity of six Eurozone countries. This class of models allows for common and country-specific covariates to affect the real growth, as well as for cross-sectional dependence in the error processes. Estimation and inference for this class of panel models are based on both Bayesian and classical techniques. Our findings reveal that significant heterogeneity exists among the different economies with respect to the explanatory/predictive factors. The impact of the COVID-19 pandemic varied across the Euro area economies under study. Nonetheless, the outbreak of the COVID-19 pandemic profoundly affected real economic activity across all regions and countries. As an exogenous shock of such magnitude, it caused a sharp increase in overall uncertainty that spread quickly across all sectors of the global economy.

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A comparison of monthly global indicators for forecasting growth

Cited by 28 publications

References 43 publications

The predictive power of commodity prices for future economic growth: Evaluating the role of economic development

The predictive power of commodity prices for future economic growth: Evaluating the role of economic development

Forecasting GDP growth: The economic impact of COVID‐19 pandemic

Modeling the Economic Impact of the COVID-19 Pandemic Using Dynamic Panel Models and Seemingly Unrelated Regressions

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