The Effects of Global Supply Chain Pressure on Sentiment, Expectation, and Uncertainty: A VAR Approach
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This paper studies the relationship of global supply chain pressure with consumer sentiment, inflation expectation, and monetary policy uncertainty in the United States. A sample from January 1998 to January 2024 is used, and this paper uses a Vector Autoregression (VAR) approach based on the method proposed by Toda and Yamamoto (1995). The Granger causality test suggests that the predictions of inflation expectation based on its own past values and the past values of the global supply chain pressure are better predictions of inflation expectation than just using the past observations of inflation expectation. In contrast, Impulse Response Functions suggest that surprise increases in global supply chain pressure lead to increased inflation expectation and monetary policy uncertainty; this shock lasts up to two years. Meanwhile, the Impulse Response Functions suggest that surprise increases in the global supply chain pressure decrease consumer sentiment (confidence), lasting up to two and a half years. Afterward, the impact converges back to zero. Additionally, the Variance Decomposition results suggest that by the final period, the impulses of the global supply chain pressure explain over 22%, 7%, and 44% of the variation of consumer sentiment, monetary policy uncertainty, and inflation expectation, respectively.
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Ascari, G., Bonam, D., & Smadu, A. (2024). Global Supply Chain Pressures, Inflation, and Implications for Monetary Policy. Journal of International Money and Finance 142, 1–25, 103029. https://doi.org/10.1016/j.jimonfin.2024.103029 DOI: https://doi.org/10.1016/j.jimonfin.2024.103029
Baker, Scott R., Bloom, N., & Davis, S. J. (2024). Economic Policy Uncertainty Index:Categorical Index: Monetary Policy [EPUMONETARY]. FRED, Federal Reserve Bank of St. Louis. https://fred.stlouisfed.org/series/EPUMONETARY
Benigno, G., Di Giovanni, J., Groen, J. J. J., & Noble, A. I. (2022). The GSCPI: A New Barometer of Global Supply Chain Pressures. Reserve Bank of New York Staff Reports, 1017. https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr1017.pdf DOI: https://doi.org/10.2139/ssrn.4114973
Brahmasrene, T., Huang, J-C., & Sissoko, Y. (2014). Crude Oil Prices and Exchange Rates: Causality, Variance Decomposition and Impulse Response. Energy Economics, 44, 407–412. https://doi.org/10.1016/j.eneco.2014.05.011 DOI: https://doi.org/10.1016/j.eneco.2014.05.011
Dickey, D. A., & Fuller, W. A. (1981). Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root. Econometrica, 49(4), 1057–1072. https://doi.org/10.2307/1912517 DOI: https://doi.org/10.2307/1912517
Di Giovanni, J., Kalemli-Özcan, Ṣ., Silva, A., & Yildirim, M. (2022). Global Supply Chain Pressures, International Trade, and Inflation. National Bureau of Economic Research Working Paper Series, 30240. https://doi.org/10.3386/w30240 DOI: https://doi.org/10.3386/w30240
Dufour, J-M, & Taamouti, A. (2010). Short and Long Run Causality Measures: Theory and Inference. Journal of Econometrics 154(1), 42–58. https://doi.org/10.1016/j.jeconom.2009.06.008 DOI: https://doi.org/10.1016/j.jeconom.2009.06.008
Ercolani, V., & Natoli, F. (2020). Forecasting U.S. Recessions: The Role of Economic Uncertainty. Economics Letters, 193, 109302. https://doi.org/10.1016/j.econlet.2020.109302 DOI: https://doi.org/10.1016/j.econlet.2020.109302
Federal Reserve Bank of New York. (FRBNY). (2024). Global Supply Chain Pressure Index (GSCPI). Estimates for September 2024. https://www.newyorkfed.org/research/policy/gscpi#/interactive
Federal Reserve Bank of St. Louis (FRED). (2024a). University of Michigan: Consumer Sentiment [UMCSENT]. Federal Reserve Bank of St. Louis. https://fred.stlouisfed.org/series/UMCSENT
Federal Reserve Bank of St. Louis (FRED). (2024b). University of Michigan: Inflation Expectation [MICH]. Federal Reserve Bank of St. Louis. https://fred.stlouisfed.org/series/MICH
Gorodnichenko, Y., & Lee, B. (2020). Forecast Error Variance Decompositions with Local Projections. Journal of Business & Economic Statistics, 38(4), 921–933. https://doi.org/10.1080/07350015.2019.1610661 DOI: https://doi.org/10.1080/07350015.2019.1610661
Granger, C. (1969). Investigating Causal Relations by Econometric Models and Cross-Spectral Methods. Econometrica 37(3), 424–438. https://doi.org/10.2307/1912791 DOI: https://doi.org/10.2307/1912791
Gujarati, D., & Porter, D. (2009). Econometría. McGraw-Hill.
Gylych, J., Jbrin, A. A., Celik, B., & Isik, A. (2020). The Effect of Oil Price Fluctuation on the Economy of Nigeria. International Journal of Energy Economics and Policy, 10(5), 461–468. https://doi.org/10.32479/ijeep.9493 DOI: https://doi.org/10.32479/ijeep.9493
Hamilton, J. (1994). Time Series Analysis. Princeton University Press. DOI: https://doi.org/10.1515/9780691218632
Herbstman, J. S., & Brave, S. A. (2023). Persistently Pessimistic: Consumer and Small Business Sentiment After the Covid Recession. Chicago FedLetter, 490, 1–9. https://doi.org/10.21033/cfl-2023-490 DOI: https://doi.org/10.21033/cfl-2023-490
Johansen, S. (1991). Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models. Econometrica, 59(6), 1551–1580. https://doi.org/10.2307/2938278 DOI: https://doi.org/10.2307/2938278
Jordà, Ò. (2005). Estimation and Inference of Impulse Responses by Local Projections. American Economic Review, 95(1), 161–182. https://doi.org/10.1257/0002828053828518 DOI: https://doi.org/10.1257/0002828053828518
Jordà, Ò. (2023). Local Projections for Applied Economics. Annual Review of Economics, 15, 607–631. https://doi.org/10.1146/annurev-economics-082222-065846 DOI: https://doi.org/10.1146/annurev-economics-082222-065846
Jordà, Ò., & Nechio, F. (2023). Inflation and Wage Growth Since the Pandemic. European Economic Review, 156, 1–16. https://doi.org/10.1016/j.euroecorev.2023.104474 DOI: https://doi.org/10.1016/j.euroecorev.2023.104474
Jordà, Ò., & Taylor, A. M. (2024). Local Projections (Working Paper, 2024-24). Federal Reserve Bank of San Francisco Working Paper. https://doi.org/10.24148/wp2024-24 DOI: https://doi.org/10.24148/wp2024-24
Jordà, Ò., Liu, C., Nechio, F., & Rivera-Reyes, F. (2022). Wage Growth When Inflation Is High. FRBSF Economic Letter, 2022-25. Federal Reserve Bank of San Francisco. https://www.frbsf.org/research-and-insights/publications/economic-letter/2022/09/wage-growth-when-inflation-is-high/
Kabaca, S., & Tuzcuoglu, K. (2023). Supply Drivers of U.S. Inflation Since the COVID-19 Pandemic (Working Paper, 2023-19). Bank of Canada Staff Working Paper. https://doi.org/10.34989/swp-2023-19
Kristoufek, L. (2022). On the Role of Stablecoins in Cryptoasset Pricing Dynamics. Financial Innovation, 8(37), 1–26. https://doi.org/10.1186/s40854-022-00343-8 DOI: https://doi.org/10.1186/s40854-022-00343-8
Liu, Z., & Nguyen, T. L. (2023). Global Supply Chain Pressures and U.S. Inflation. FRBSF Economic Letter, 2023-14. Federal Reserve Bank of San Francisco. https://www.frbsf.org/research-and-insights/publications/economic-letter/2023/06/global-supply-chain-pressures-and-us-inflation/
Paramanik, R. N., & Kamaiah, B. (2014). A Structural Vector Autoregression Model for Monetary Policy Analysis in India. Margin: The Journal of Applied Economic Research, 8(4), 401–429. https://doi.org/10.1177/0973801014544580 DOI: https://doi.org/10.1177/0973801014544580
Phillips, P. C. B., & Perron, P. (1988). Testing for a Unit Root in Time Series Regression. Biometrika, 75(2), 335–346. https://doi.org/10.1093/biomet/75.2.335 DOI: https://doi.org/10.1093/biomet/75.2.335
Plagborg-Møller, M., & Wolf, C. K. (2021). Local Projections and VARs Estimate the Same Impulse Responses. Econometrica, 89(2), 955-980. https://doi.org/10.3982/ECTA17813 DOI: https://doi.org/10.3982/ECTA17813
Rodhan, M. (2024). Macroeconomic Impacts of Oil Price Shocks: Evidence from Iraq by Using Vector Autoregressive Model. International Journal of Energy Economics and Policy, 14(3), 162–170. https://doi.org/10.32479/ijeep.15681 DOI: https://doi.org/10.32479/ijeep.15681
Romero-Ramírez, H. (2023). Does U.S. Trade Liberalization Explain Puerto Rico’s Deindustrialization? Problemas del Desarrollo. Revista Latinoamericana de Economía, 54(214), 159–189. https://doi.org/10.22201/iiec.20078951e.2023.214.69995 DOI: https://doi.org/10.22201/iiec.20078951e.2023.214.69995
Sims, C. A. (1972). Money, Income, and Causality. The American Economic Review, 62(4), 540–552. http://www.jstor.org/stable/1806097
Sims, C. A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1-48. https://doi.org/10.2307/1912017 DOI: https://doi.org/10.2307/1912017
Tillmann, P. (2024). The Asymmetric Effect of Supply Chain Pressure on Inflation. Economics Letters, 235, 111540. https://doi.org/10.1016/j.econlet.2024.111540 DOI: https://doi.org/10.1016/j.econlet.2024.111540
Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225–250. https://doi.org/10.1016/0304-4076(94)01616-8 DOI: https://doi.org/10.1016/0304-4076(94)01616-8
Zapata, H. O., & Rambaldi, A. N. (1997). Monte Carlo Evidence on Cointegration and Causation. Oxford Bulletin of Economics and Statistics, 59(2), 285-298. https://doi.org/10.1111/1468-0084.00065 DOI: https://doi.org/10.1111/1468-0084.00065