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Spillover effects of disaggregated macroeconomic uncertainties on U.S. real activity: evidence from the quantile vector autoregressive connectedness approach

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Abstract

This paper employs a quantile vector autoregressive approach to compare the extent of connectedness between macroeconomic uncertainty and U.S. real activity in normal and extreme economic and financial conditions. Based on a database ranging from January 1960 to February 2023 and using the methodology of Jurado et al. (Am Econ Rev 105(3):1177–1216, 2015), we derive, first, eight real and financial uncertainty measures based on 134 macroeconomic indicators. Second, we examine the quantile connectedness among the eight estimated macroeconomic uncertainties and real activity. The results indicate that real activity is strongly (weakly) influenced by the extremely high (low) values of the real and financial uncertainty estimates, indicating asymmetric spillover effects of US macroeconomic uncertainties on real economic activity. Among the real and financial uncertainty estimates, the real output uncertainty blocks are the most important transmitters of shocks to real activity under looser economic and financial conditions. However, under tighter economic and financial conditions, higher output and income and stock market uncertainty blocks are the major spillover transmitters of shocks to real activity. Finally, connectedness between macroeconomic uncertainties and real activity exhibits the highest level during the recent COVID-19 pandemic outbreak at various quantiles.

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Notes

  1. Data are retrieved from the http://research.stlouisfed.org/econ/mccracken/sel/. The database contains 128 macroeconomic indicators, we add the 6 monthly survey of purchasing managers in the manufacturing and service sectors provided by the Institute for Supply Management (ISM). We retrieve the 6 ISM manufacturing indices from the subscribed Global Economic Indicators database on Quandl (ECD | Global Economic Indicators | Nasdaq Data Link).

  2. ‘‘To ensure that the latent uncertainty factor is positive, the method of principal components is applied to the logarithm of the individual uncertainty estimates and then rescaled’’ Jurado et al. (2015).

  3. We use the free MATLAB code provided by Serena Ng (sn2294—Code and data (google.com). After four lags selected in the FAVAR model and the suitable transformations used to transform the raw data into stationary one, the remaining sample period span from 1960:7 to 2023:2.

  4. For each macroeconomic uncertainty plot include the uncertainty estimate along with the NBER dates, and a horizontal line corresponding to 1.65 standard deviation from the long-term average. High uncertainty is identified as macro uncertainty exceeding the 1.65 line.

  5. 4We use the R Connectedness Package provided by David Gabauer. Redirecting (google.com).

  6. We thank an anonyms reviewer to suggest us this sensitivity analysis. The real macroeconomic uncertainty estimates include the four output uncertainties (output and income, labour market, housing, and consumption, orders and inventories) and prices uncertainty, whereas financial uncertainty measures include the three remaining uncertainties: money and credit uncertainty, interest and exchange rates uncertainty, and stock market uncertainty.

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Funding

Funding was provided by Imam Mohammed Ibn Saud Islamic University (Grant No. RG-21-10-01).

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Authors and Affiliations

  1. Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia

    Hedi Ben Haddad, Imed Mezghani, Imed Medhioub & Sohale Altamimi

  2. High Business School of Sfax, University of Sfax, Sfax, Tunisia

    Hedi Ben Haddad, Imed Mezghani & Imed Medhioub

Authors
  1. Hedi Ben Haddad
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  2. Imed Mezghani
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  4. Sohale Altamimi
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Correspondence to Hedi Ben Haddad.

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Appendix

Appendix

See Figs. 13, 14, 15, 16, 17 and 18.

Fig. 13
figure 13

Robustness checks: selection of real macroeconomic uncertainty (RMU) versus IPI (12-month average of the monthly industrial production growth), and financial uncertainty (FU) versus IPI. MU versus IPI is the total connectedness index (TCI) of the system including the seven real and the financial uncertainty measures, and the IPI. RMU versus IPI is the TCI of the system including the five real macroeconomic uncertainty measures (output and income, labor market, housing, consumption, orders and inventories, prices) and the IPI. FU versus IPI is the TCI of the system including the financial uncertainty measures (money and credits, interest and exchange rates, and stock market) and the IPI. All TCIs are estimated at extreme lower (\(\tau =0.10\)) and upper (\(\tau =0.90\)) quantiles

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Fig. 14
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Robustness check: selection of the Chicago Fed National Activity Index (CFNAI) as proxy of real activity

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Fig. 15
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Robustness check: alternative selection of real and financial uncertainty measures at one-month (M = 1), three-month (M = 3), and twelve-month (M = 12) ahead forecasts

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Robustness check: alternatives extreme lower (\(\uptau\)= 0.01, 0.05, 0.10, 0.15) and upper quantiles (\(\uptau\)= 0.99, 0.95, 0.90, 0.85)

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Fig. 17
figure 17

Robustness check: alternatives different horizons (\(H=\mathrm{6,12,24}\)) at lower quantile (\(\tau =0.1)\) and upper quantile (\(\tau =0.9)\)

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Fig. 18
figure 18

Robustness check: alternatives rolling windows (\(W=\mathrm{150,200,250}\)) at lower quantile (\(\tau =0.1)\) and upper quantile (\(\tau =0.9)\)

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Ben Haddad, H., Mezghani, I., Medhioub, I. et al. Spillover effects of disaggregated macroeconomic uncertainties on U.S. real activity: evidence from the quantile vector autoregressive connectedness approach. Empir Econ 66, 829–858 (2024). https://doi.org/10.1007/s00181-023-02474-y

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