Los efectos de la presión de la cadena de suministro global sobre el sentimiento, las expectativas y la incertidumbre: un enfoque VAR
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Este trabajo estudia el vínculo entre la presión de la cadena de suministro global y el sentimiento del consumidor, las expectativas de inflación y la incertidumbre de la política monetaria en los Estados Unidos. Se emplea una muestra de enero de 1998 a enero de 2024, y el trabajo sigue un enfoque VAR (vectorial autorregresivo) basado en el método propuesto por Toda y Yamamoto (1995). La prueba de causalidad de Granger sugiere que las predicciones de la expectativa de inflación basadas en sus propios valores pasados y los valores pasados de la presión de la cadena de suministro global son mejores predicciones de la expectativa de inflación que el uso exclusivo de las observaciones pasadas de la expectativa de inflación. En contraste, las funciones de impulso respuesta sugieren que los aumentos sorpresivos en la presión de la cadena de suministro global conducen a aumentos de las expectativas de inflación y de la incertidumbre de la política monetaria; los efectos de este shock duran hasta dos años. Mientras tanto, las funciones de impulso respuesta sugieren que los aumentos sorpresivos en la presión de la cadena de suministro global disminuyen el sentimiento del consumidor (confianza), y estos efectos duran hasta dos años y medio. Después, el impacto converge de nuevo a cero. Además, los resultados de la descomposición de la varianza sugieren que, en el período final, los impulsos de la presión de la cadena de suministro global explican más del 22%, el 7% y el 44% de la variación del sentimiento del consumidor, la incertidumbre de la política monetaria y las expectativas de inflación, respectivamente.
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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
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
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
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
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
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
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
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
Granger, C. (1969). Investigating Causal Relations by Econometric Models and Cross-Spectral Methods. Econometrica 37(3), 424–438. 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
Hamilton, J. (1994). Time Series Analysis. Princeton University Press.
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
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
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
Jordà, Ò. (2023). Local Projections for Applied Economics. Annual Review of Economics, 15, 607–631. 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
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
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
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
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
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
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
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
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
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
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
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