Referencias

Antolín-Díaz, Juan, and Juan F. Rubio-Ramírez. 2019. “Narrative Sign Restrictions for SVARs.” The American Economic Review 109 (7): 2802–29.

Arias, Jonas E., Dario Caldara, and Juan F. Rubio-Ramírez. 2019. “The Systematic Component of Monetary Policy in SVARs: An Agnostic Identification Procedure.” Journal of Monetary Economics 101: 1–13.

Arias, Jonas E., Juan F. Rubio-Ramírez, and Daniel F. Waggoner. 2018. “Inference Based on Structural Vector Autoregressions Identified with Sign and Zero Restrictions: Theory and Applications.” Econometrica 86 (2): 685–720.

Atkeson, Andrew, and Lee E. Ohanian. 2001. “Are Phillips Curves Useful for Forecasting Inflation?” Federal Reserve Bank of Minneapolis Quarterly Review 25 (1): 2–11.

Bańbura, Marta, Domenico Giannone, and Lucrezia Reichlin. 2010. “Large Bayesian Vector Auto Regressions.” Journal of Applied Econometrics 25 (1): 71–92.

Baumeister, Christiane, and James D. Hamilton. 2015. “Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information.” Econometrica 83 (5): 1963–99.

Bergmeir, Christoph, Rob J. Hyndman, and Bonsoo Koo. 2018. “A Note on the Validity of Cross-Validation for Evaluating Autoregressive Time Series Prediction.” Computational Statistics & Data Analysis 120: 70–83.

Bernanke, Ben S., Jean Boivin, and Piotr Eliasz. 2005. “Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach.” Quarterly Journal of Economics 120 (1): 387–422.

Beveridge, Stephen, and Charles R. Nelson. 1981. “A New Approach to Decomposition of Economic Time Series into Permanent and Transitory Components.” Journal of Monetary Economics 7 (2): 151–74.

Blake, Andrew P., and Haroon Mumtaz. 2017. Applied Bayesian Econometrics for Central Bankers. Technical Handbook. Centre for Central Banking Studies, Bank of England.

Blanchard, Olivier Jean, and Danny Quah. 1989. “The Dynamic Effects of Aggregate Demand and Supply Disturbances.” The American Economic Review 79 (4): 655–73.

Box, George E. P., and David R. Cox. 1964. “An Analysis of Transformations.” Journal of the Royal Statistical Society, Series B 26 (2): 211–52.

Box, George E. P., and Gwilym M. Jenkins. 1970. Time Series Analysis: Forecasting and Control. Holden-Day.

Breiman, Leo. 2001. “Random Forests.” Machine Learning 45 (1): 5–32.

Burns, Arthur F., and Wesley C. Mitchell. 1946. Measuring Business Cycles. National Bureau of Economic Research.

Campbell, John Y., and N. Gregory Mankiw. 1987. “Are Output Fluctuations Transitory?” Quarterly Journal of Economics 102 (4): 857–80.

Canova, Fabio, and Gianni De Nicoló. 2002. “Monetary Disturbances Matter for Business Fluctuations in the G-7.” Journal of Monetary Economics 49 (6): 1131–59.

Chernozhukov, Victor, Denis Chetverikov, Mert Demirer, et al. 2018. “Double/Debiased Machine Learning for Treatment and Structural Parameters.” The Econometrics Journal 21 (1): C1–68.

Christiano, Lawrence J., Martin Eichenbaum, and Charles L. Evans. 1999. “Monetary Policy Shocks: What Have We Learned and to What End?” In Handbook of Macroeconomics, edited by John B. Taylor and Michael Woodford, vol. 1. Elsevier.

Clark, Todd E., and Kenneth D. West. 2007. “Approximately Normal Tests for Equal Predictive Accuracy in Nested Models.” Journal of Econometrics 138 (1): 291–311.

Cleveland, Robert B., William S. Cleveland, Jean E. McRae, and Irma Terpenning. 1990. “STL: A Seasonal-Trend Decomposition Procedure Based on Loess.” Journal of Official Statistics 6 (1): 3–73.

Cogley, Timothy, and Thomas J. Sargent. 2005. “Drifts and Volatilities: Monetary Policies and Outcomes in the Post WWII US.” Review of Economic Dynamics 8 (2): 262–302.

Croushore, Dean, and Tom Stark. 2001. “A Real-Time Data Set for Macroeconomists.” Journal of Econometrics 105 (1): 111–30.

Dagum, Estela Bee. 1980. The X-11-ARIMA Seasonal Adjustment Method. Nos. 12-564E. Statistics Canada.

De Mol, Christine, Domenico Giannone, and Lucrezia Reichlin. 2008. “Forecasting Using a Large Number of Predictors: Is Bayesian Shrinkage a Valid Alternative to Principal Components?” Journal of Econometrics 146 (2): 318–28.

Dickey, David A., and Wayne A. Fuller. 1979. “Distribution of the Estimators for Autoregressive Time Series with a Unit Root.” Journal of the American Statistical Association 74 (366): 427–31.

Diebold, Francis X., and Roberto S. Mariano. 1995. “Comparing Predictive Accuracy.” Journal of Business & Economic Statistics 13 (3): 253–63.

Efron, Bradley, and Robert J. Tibshirani. 1993. An Introduction to the Bootstrap. Chapman; Hall.

Engle, Robert F. 1982. “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation.” Econometrica 50 (4): 987–1007.

Engle, Robert F., and C. W. J. Granger. 1987. “Co-Integration and Error Correction: Representation, Estimation, and Testing.” Econometrica 55 (2): 251–76.

Eurostat. 2024. ESS Guidelines on Seasonal Adjustment. Manuals and guidelines. Publications Office of the European Union.

Faust, Jon. 1998. “The Robustness of Identified VAR Conclusions about Money.” Carnegie-Rochester Conference Series on Public Policy 49: 207–44.

Faust, Jon, and Eric M. Leeper. 1997. “When Do Long-Run Identifying Restrictions Give Reliable Results?” Journal of Business & Economic Statistics 15 (3): 345–53.

Federal Open Market Committee. 2012. “Federal Reserve Issues FOMC Statement of Longer-Run Goals and Policy Strategy.” Board of Governors of the Federal Reserve System, January 25. https://www.federalreserve.gov/newsevents/pressreleases/monetary20120125c.htm.

Findley, David F., Brian C. Monsell, William R. Bell, Mark C. Otto, and Bor-Chung Chen. 1998. “New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program.” Journal of Business & Economic Statistics 16 (2): 127–52.

Friedman, Jerome H. 2001. “Greedy Function Approximation: A Gradient Boosting Machine.” Annals of Statistics 29 (5): 1189–232.

Fry, Renée, and Adrian Pagan. 2011. “Sign Restrictions in Structural Vector Autoregressions: A Critical Review.” Journal of Economic Literature 49 (4): 938–60.

Galí, Jordi. 1999. “Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?” The American Economic Review 89 (1): 249–71.

Gelman, Andrew, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin. 2013. Bayesian Data Analysis. 3rd ed. Chapman; Hall/CRC. https://sites.stat.columbia.edu/gelman/book/.

Gentzkow, Matthew, and Jesse M. Shapiro. 2014. Code and Data for the Social Sciences: A Practitioner’s Guide. University of Chicago. https://web.stanford.edu/~gentzkow/research/CodeAndData.pdf.

Gertler, Mark, and Peter Karadi. 2015. “Monetary Policy Surprises, Credit Costs, and Economic Activity.” American Economic Journal: Macroeconomics 7 (1): 44–76.

Giannone, Domenico, Michele Lenza, and Giorgio E. Primiceri. 2015. “Prior Selection for Vector Autoregressions.” The Review of Economics and Statistics 97 (2): 436–51.

Giannone, Domenico, Lucrezia Reichlin, and David Small. 2008. “Nowcasting: The Real-Time Informational Content of Macroeconomic Data.” Journal of Monetary Economics 55 (4): 665–76.

Gibbs, Isaac, and Emmanuel Candès. 2021. “Adaptive Conformal Inference Under Distribution Shift.” Advances in Neural Information Processing Systems 34.

Gómez, Víctor, and Agustín Maravall. 1996. Programs TRAMO and SEATS: Instructions for the User. Working Paper No. 9628. Banco de España.

Gonçalves, Sílvia, and Lutz Kilian. 2004. “Bootstrapping Autoregressions with Conditional Heteroskedasticity of Unknown Form.” Journal of Econometrics 123 (1): 89–120.

Goulet Coulombe, Philippe. 2025. “A Neural Phillips Curve and a Deep Output Gap.” Journal of Business & Economic Statistics 43 (3). https://doi.org/10.1080/07350015.2024.2421279.

Goulet Coulombe, Philippe, Maxime Leroux, Dalibor Stevanovic, and Stéphane Surprenant. 2022. “How Is Machine Learning Useful for Macroeconomic Forecasting?” Journal of Applied Econometrics 37 (5): 920–64.

Hamilton, James D. 1994. Time Series Analysis. Princeton University Press.

Hamilton, James D. 2018. “Why You Should Never Use the Hodrick-Prescott Filter.” The Review of Economics and Statistics 100 (5): 831–43.

Harris, Charles R., K. Jarrod Millman, Stéfan J. van der Walt, et al. 2020. “Array Programming with NumPy.” Nature 585 (7825): 357–62.

Harvey, Andrew C. 1989. Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.

Harvey, David, Stephen Leybourne, and Paul Newbold. 1997. “Testing the Equality of Prediction Mean Squared Errors.” International Journal of Forecasting 13 (2): 281–91.

Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2009. The Elements of Statistical Learning. 2nd ed. Springer.

Hodrick, Robert J., and Edward C. Prescott. 1997. “Postwar U.S. Business Cycles: An Empirical Investigation.” Journal of Money, Credit and Banking 29 (1): 1–16.

Hoerl, Arthur E., and Robert W. Kennard. 1970. “Ridge Regression: Biased Estimation for Nonorthogonal Problems.” Technometrics 12 (1): 55–67.

Hunter, John D. 2007. “Matplotlib: A 2D Graphics Environment.” Computing in Science & Engineering 9 (3): 90–95.

Ivanov, Ventzislav, and Lutz Kilian. 2005. “A Practitioner’s Guide to Lag Order Selection for VAR Impulse Response Analysis.” Studies in Nonlinear Dynamics & Econometrics 9 (1).

Johansen, Søren. 1995. Likelihood-Based Inference in Cointegrated Vector Autoregressive Models. Oxford University Press.

Jordà, Òscar. 2005. “Estimation and Inference of Impulse Responses by Local Projections.” The American Economic Review 95 (1): 161–82.

Kendall, M. G. 1954. “Note on Bias in the Estimation of Autocorrelation.” Biometrika 41 (3/4): 403–4.

Kilian, Lutz. 1998. “Small-Sample Confidence Intervals for Impulse Response Functions.” The Review of Economics and Statistics 80 (2): 218–30.

Kilian, Lutz, and Helmut Lütkepohl. 2017. Structural Vector Autoregressive Analysis. Cambridge University Press.

Lenza, Michele, and Giorgio E. Primiceri. 2022. “How to Estimate a Vector Autoregression After March 2020.” Journal of Applied Econometrics 37 (4): 688–99.

Leshno, Moshe, Vladimir Ya. Lin, Allan Pinkus, and Shimon Schocken. 1993. “Multilayer Feedforward Networks with a Nonpolynomial Activation Function Can Approximate Any Function.” Neural Networks 6 (6): 861–67.

Litterman, Robert B. 1986. “Forecasting with Bayesian Vector Autoregressions: Five Years of Experience.” Journal of Business & Economic Statistics 4 (1): 25–38.

Lütkepohl, Helmut. 2005. New Introduction to Multiple Time Series Analysis. Springer.

Lütkepohl, Helmut, and Fang Xu. 2012. “The Role of the Log Transformation in Forecasting Economic Variables.” Empirical Economics 42 (3): 619–38.

McCracken, Michael W., and Serena Ng. 2016. “FRED-MD: A Monthly Database for Macroeconomic Research.” Journal of Business & Economic Statistics 34 (4): 574–89.

McKinney, Wes. 2010. “Data Structures for Statistical Computing in Python.” Proceedings of the 9th Python in Science Conference, 56–61.

McKinney, Wes. 2022. Python for Data Analysis. 3rd ed. O’Reilly Media. https://wesmckinney.com/book/.

Medeiros, Marcelo C., Gabriel F. R. Vasconcelos, Álvaro Veiga, and Eduardo Zilberman. 2021. “Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods.” Journal of Business & Economic Statistics 39 (1): 98–119.

Mertens, Karel, and Morten O. Ravn. 2013. “The Dynamic Effects of Personal and Corporate Income Tax Changes in the United States.” The American Economic Review 103 (4): 1212–47.

Montiel Olea, José Luis, and Mikkel Plagborg-Møller. 2019. “Simultaneous Confidence Bands: Theory, Implementation, and an Application to SVARs.” Journal of Applied Econometrics 34 (1): 1–17.

Nelson, Charles R., and Charles I. Plosser. 1982. “Trends and Random Walks in Macroeconomic Time Series: Some Evidence and Implications.” Journal of Monetary Economics 10 (2): 139–62.

Park, Trevor, and George Casella. 2008. “The Bayesian Lasso.” Journal of the American Statistical Association 103 (482): 681–86.

Plagborg-Møller, Mikkel, and Christian K. Wolf. 2021. “Local Projections and VARs Estimate the Same Impulse Responses.” Econometrica 89 (2): 955–80.

Ramey, Valerie A. 2016. “Macroeconomic Shocks and Their Propagation.” In Handbook of Macroeconomics, edited by John B. Taylor and Harald Uhlig, vol. 2. Elsevier.

Rigobon, Roberto. 2003. “Identification Through Heteroskedasticity.” The Review of Economics and Statistics 85 (4): 777–92.

Sargent, Thomas J., and John Stachurski. n.d.-a. “Intermediate Quantitative Economics with Python.” QuantEcon. Accessed September 21, 2026. https://python.quantecon.org/intro.html.

Sargent, Thomas J., and John Stachurski. n.d.-b. “Python Programming for Economics and Finance.” QuantEcon. Accessed September 21, 2026. https://python-programming.quantecon.org/intro.html.

Sims, Christopher A. 1980. “Macroeconomics and Reality.” Econometrica 48 (1): 1–48.

Sims, Christopher A. 1992. “Interpreting the Macroeconomic Time Series Facts: The Effects of Monetary Policy.” European Economic Review 36 (5): 975–1000.

Sims, Christopher A., James H. Stock, and Mark W. Watson. 1990. “Inference in Linear Time Series Models with Some Unit Roots.” Econometrica 58 (1): 113–44.

Sims, Christopher A., and Tao Zha. 1999. “Error Bands for Impulse Responses.” Econometrica 67 (5): 1113–55.

Stock, James H., and Mark W. Watson. 2001. “Vector Autoregressions.” Journal of Economic Perspectives 15 (4): 101–15.

Stock, James H., and Mark W. Watson. 2002. “Macroeconomic Forecasting Using Diffusion Indexes.” Journal of Business & Economic Statistics 20 (2): 147–62.

Stock, James H., and Mark W. Watson. 2007. “Why Has U.S. Inflation Become Harder to Forecast?” Journal of Money, Credit and Banking 39 (s1): 3–33.

Stock, James H., and Mark W. Watson. 2018. “Identification and Estimation of Dynamic Causal Effects in Macroeconomics Using External Instruments.” The Economic Journal 128 (610): 917–48.

Strang, Gilbert. 2016. Introduction to Linear Algebra. 5th ed. Wellesley-Cambridge Press.

Tibshirani, Robert. 1996. “Regression Shrinkage and Selection via the Lasso.” Journal of the Royal Statistical Society, Series B 58 (1): 267–88.

Turrell, Arthur. n.d. “Coding for Economists.” Accessed September 21, 2026. https://aeturrell.github.io/coding-for-economists/.

Uhlig, Harald. 2005. “What Are the Effects of Monetary Policy on Output? Results from an Agnostic Identification Procedure.” Journal of Monetary Economics 52 (2): 381–419.

VanderPlas, Jake. 2016. Python Data Science Handbook. O’Reilly Media. https://jakevdp.github.io/PythonDataScienceHandbook/.

Virtanen, Pauli, Ralf Gommers, Travis E. Oliphant, et al. 2020. “SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python.” Nature Methods 17 (3): 261–72.

Wolf, Christian K. 2020. “SVAR (Mis)identification and the Real Effects of Monetary Policy Shocks.” American Economic Journal: Macroeconomics 12 (4): 1–32.