IDEAS home Printed from https://ideas.repec.org/p/fip/fedhwp/93029.html

A Hitchhiker’s Guide to Empirical Macro Models

Author

Listed:
  • Fabio Canova
  • Filippo Ferroni

Abstract

This paper describes a package which uses MATLAB functions and routines to estimate VARs, local projections and other models with classical or Bayesian methods. The toolbox allows a researcher to conduct inference under various prior assumptions on the parameters, to produce point and density forecasts, to measure spillovers and to trace out the causal effect of shocks using a number of identification schemes. The toolbox is equipped to handle missing observations, mixed frequencies and time series with large cross-section information (e.g. panels of VAR and FAVAR). It also contains a number of routines to extract cyclical information and to date business cycles. We describe the methodology employed and implementation of the functions with a number of practical examples.

Suggested Citation

  • Fabio Canova & Filippo Ferroni, 2021. "A Hitchhiker’s Guide to Empirical Macro Models," Working Paper Series WP-2021-15, Federal Reserve Bank of Chicago, revised 03 Oct 2021.
  • Handle: RePEc:fip:fedhwp:93029
    DOI: 10.21033/wp-2021-15
    as

    Download full text from publisher

    File URL: https://www.chicagofed.org/~/media/publications/working-papers/2021/wp2021-15-pdf.pdf
    File Function: full text
    Download Restriction: no

    File URL: https://libkey.io/10.21033/wp-2021-15?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Fabio Canova, 2007. "Bayesian Time Series and DSGE Models, from Methods for Applied Macroeconomic Research," Introductory Chapters, in: Methods for Applied Macroeconomic Research, Princeton University Press.
    2. Fabio Canova & Filippo Ferroni, 2022. "Mind the Gap! Stylized Dynamic Facts and Structural Models," American Economic Journal: Macroeconomics, American Economic Association, vol. 14(4), pages 104-135, October.
    3. Marianne Baxter & Robert G. King, 1999. "Measuring Business Cycles: Approximate Band-Pass Filters For Economic Time Series," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 575-593, November.
    4. Fabio Canova & Fernando J. Pérez Forero, 2015. "Estimating overidentified, nonrecursive, time‐varying coefficients structural vector autoregressions," Quantitative Economics, Econometric Society, vol. 6(2), pages 359-384, July.
    5. Altavilla, Carlo & Canova, Fabio & Ciccarelli, Matteo, 2020. "Mending the broken link: Heterogeneous bank lending rates and monetary policy pass-through," Journal of Monetary Economics, Elsevier, vol. 110(C), pages 81-98.
    6. Boivin, Jean & Kiley, Michael T. & Mishkin, Frederic S., 2010. "How Has the Monetary Transmission Mechanism Evolved Over Time?," Handbook of Monetary Economics, in: Benjamin M. Friedman & Michael Woodford (ed.), Handbook of Monetary Economics, edition 1, volume 3, chapter 8, pages 369-422, Elsevier.
    7. Ramey, V.A., 2016. "Macroeconomic Shocks and Their Propagation," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 71-162, Elsevier.
    8. Jonas D. M. Fisher, 2006. "The Dynamic Effects of Neutral and Investment-Specific Technology Shocks," Journal of Political Economy, University of Chicago Press, vol. 114(3), pages 413-451, June.
    9. Geweke, John & Koop, Gary & van Dijk, Herman (ed.), 2011. "The Oxford Handbook of Bayesian Econometrics," OUP Catalogue, Oxford University Press, number 9780199559084.
    10. Juan F. Rubio-Ramírez & Daniel F. Waggoner & Tao Zha, 2010. "Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 77(2), pages 665-696.
    11. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
    12. James H. Stock & Mark W. Watson, 2012. "Disentangling the Channels of the 2007-09 Recession," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 43(1 (Spring), pages 81-156.
    13. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    14. Stock, James H. & Watson, Mark W., 2014. "Estimating turning points using large data sets," Journal of Econometrics, Elsevier, vol. 178(P2), pages 368-381.
    15. repec:bge:wpaper:549 is not listed on IDEAS
    16. Karel Mertens & Morten O. Ravn, 2013. "The Dynamic Effects of Personal and Corporate Income Tax Changes in the United States," American Economic Review, American Economic Association, vol. 103(4), pages 1212-1247, June.
    17. Watson, Mark W. & Stock, James H., 2014. "Estimating turning points using large data sets," Scholarly Articles 33192198, Harvard University Department of Economics.
    18. Manuel Gonzalez-Astudillo & John M. Roberts, 2016. "When Can Trend-Cycle Decompositions Be Trusted?," Finance and Economics Discussion Series 2016-099, Board of Governors of the Federal Reserve System (U.S.).
    19. repec:bla:ecpoli:v:26:y:2011:i:68:p:555-598 is not listed on IDEAS
    20. Lutz Kilian & Daniel P. Murphy, 2012. "Why Agnostic Sign Restrictions Are Not Enough: Understanding The Dynamics Of Oil Market Var Models," Journal of the European Economic Association, European Economic Association, vol. 10(5), pages 1166-1188, October.
    21. Uhlig, Harald, 2005. "What are the effects of monetary policy on output? Results from an agnostic identification procedure," Journal of Monetary Economics, Elsevier, vol. 52(2), pages 381-419, March.
    22. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737, Enero-Abr.
    23. Lawrence J. Christiano & Terry J. Fitzgerald, 2003. "The Band Pass Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(2), pages 435-465, May.
    24. Silvia Miranda-Agrippino & Giovanni Ricco, 2021. "The Transmission of Monetary Policy Shocks," American Economic Journal: Macroeconomics, American Economic Association, vol. 13(3), pages 74-107, July.
    25. Fabio Canova & Evi Pappa, 2011. "Fiscal policy, pricing frictions and monetary accommodation [Expansionary fiscal consolidations in Europe: New evidence]," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 26(68), pages 555-598.
    26. Morten O. Ravn & Harald Uhlig, 2002. "On adjusting the Hodrick-Prescott filter for the frequency of observations," The Review of Economics and Statistics, MIT Press, vol. 84(2), pages 371-375.
    27. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2015. "Prior Selection for Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 436-451, May.
    28. Jonas E. Arias & Juan F. Rubio‐Ramírez & Daniel F. Waggoner, 2018. "Inference Based on Structural Vector Autoregressions Identified With Sign and Zero Restrictions: Theory and Applications," Econometrica, Econometric Society, vol. 86(2), pages 685-720, March.
    29. Mariano Kulish & Adrian Pagan, 2019. "Turning Point and Oscillatory Cycles," CAMA Working Papers 2019-74, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    30. Lucas, Robert E., 1977. "Understanding business cycles," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 5(1), pages 7-29, January.
    31. John Geweke, 1999. "Using simulation methods for bayesian econometric models: inference, development,and communication," Econometric Reviews, Taylor & Francis Journals, vol. 18(1), pages 1-73.
    32. Ben Zeev, Nadav, 2018. "What can we learn about news shocks from the late 1990s and early 2000s boom-bust period?," Journal of Economic Dynamics and Control, Elsevier, vol. 87(C), pages 94-105.
    33. Grant, Angelia L. & Chan, Joshua C.C., 2017. "Reconciling output gaps: Unobserved components model and Hodrick–Prescott filter," Journal of Economic Dynamics and Control, Elsevier, vol. 75(C), pages 114-121.
    34. Francis X. Diebold & Kamil Yilmaz, 2009. "Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets," Economic Journal, Royal Economic Society, vol. 119(534), pages 158-171, January.
    35. Òscar Jordà, 2005. "Estimation and Inference of Impulse Responses by Local Projections," American Economic Review, American Economic Association, vol. 95(1), pages 161-182, March.
    36. Juan Antolín-Díaz & Juan F. Rubio-Ramírez, 2018. "Narrative Sign Restrictions for SVARs," American Economic Review, American Economic Association, vol. 108(10), pages 2802-2829, October.
    37. Negro, Marco Del & Schorfheide, Frank, 2013. "DSGE Model-Based Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 57-140, Elsevier.
    38. Blanchard, Olivier Jean & Quah, Danny, 1989. "The Dynamic Effects of Aggregate Demand and Supply Disturbances," American Economic Review, American Economic Association, vol. 79(4), pages 655-673, September.
    39. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    40. Canova, Fabio & Paustian, Matthias, 2011. "Business cycle measurement with some theory," Journal of Monetary Economics, Elsevier, vol. 58(4), pages 345-361.
    41. Junior Maih, 2010. "Conditional forecasts in DSGE models," Working Paper 2010/07, Norges Bank.
    42. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    43. Dieppe, Alistair & van Roye, Björn & Legrand, Romain, 2016. "The BEAR toolbox," Working Paper Series 1934, European Central Bank.
    44. Fabio Canova & Evi Pappa, 2007. "Price Differentials in Monetary Unions: The Role of Fiscal Shocks," Economic Journal, Royal Economic Society, vol. 117(520), pages 713-737, April.
    45. James D. Hamilton, 2018. "Why You Should Never Use the Hodrick-Prescott Filter," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 831-843, December.
    46. J. B. Taylor & Harald Uhlig (ed.), 2016. "Handbook of Macroeconomics," Handbook of Macroeconomics, Elsevier, edition 1, volume 2, number 2.
    47. Christiane Baumeister & James D. Hamilton, 2015. "Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information," Econometrica, Econometric Society, vol. 83(5), pages 1963-1999, September.
    48. Harding, Don & Pagan, Adrian, 2002. "Dissecting the cycle: a methodological investigation," Journal of Monetary Economics, Elsevier, vol. 49(2), pages 365-381, March.
    49. John Geweke, 1999. "Using Simulation Methods for Bayesian Econometric Models," Computing in Economics and Finance 1999 832, Society for Computational Economics.
    50. Mark Gertler & Peter Karadi, 2015. "Monetary Policy Surprises, Credit Costs, and Economic Activity," American Economic Journal: Macroeconomics, American Economic Association, vol. 7(1), pages 44-76, January.
    51. Michele Lenza & Giorgio E. Primiceri, 2020. "How to Estimate a VAR after March 2020," NBER Working Papers 27771, National Bureau of Economic Research, Inc.
    52. Sims, Christopher A & Zha, Tao, 1998. "Bayesian Methods for Dynamic Multivariate Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 949-968, November.
    53. George-Marios Angeletos & Fabrice Collard & Harris Dellas, 2020. "Business-Cycle Anatomy," American Economic Review, American Economic Association, vol. 110(10), pages 3030-3070, October.
    54. Stock, James H. & Watson, Mark W., 1999. "Business cycle fluctuations in us macroeconomic time series," Handbook of Macroeconomics, in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 1, pages 3-64, Elsevier.
    55. Marco Del Negro & Giorgio E. Primiceri, 2015. "Time Varying Structural Vector Autoregressions and Monetary Policy: A Corrigendum," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(4), pages 1342-1345.
    56. Ferre Graeve & Alexei Karas, 2014. "Evaluating Theories Of Bank Runs With Heterogeneity Restrictions," Journal of the European Economic Association, European Economic Association, vol. 12(4), pages 969-996, August.
    57. Frank Schorfheide & Dongho Song, 2015. "Real-Time Forecasting With a Mixed-Frequency VAR," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 366-380, July.
    58. Andrew Binning, 2013. "Underidentified SVAR models: A framework for combining short and long-run restrictions with sign-restrictions," Working Paper 2013/14, Norges Bank.
    59. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
    60. Borio, Claudio, 2014. "The financial cycle and macroeconomics: What have we learnt?," Journal of Banking & Finance, Elsevier, vol. 45(C), pages 182-198.
    61. Gerhard Rünstler & Marente Vlekke, 2018. "Business, housing, and credit cycles," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(2), pages 212-226, March.
    62. Cover, James Peery & Enders, Walter & Hueng, C. James, 2006. "Using the Aggregate Demand-Aggregate Supply Model to Identify Structural Demand-Side and Supply-Side Shocks: Results Using a Bivariate VAR," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(3), pages 777-790, April.
    63. Filipa Sá & Pascal Towbin & Tomasz Wieladek, 2014. "Capital Inflows, Financial Structure And Housing Booms," Journal of the European Economic Association, European Economic Association, vol. 12(2), pages 522-546, April.
    64. Günes Kamber & James Morley & Benjamin Wong, 2018. "Intuitive and Reliable Estimates of the Output Gap from a Beveridge-Nelson Filter," The Review of Economics and Statistics, MIT Press, vol. 100(3), pages 550-566, July.
    65. Canova, Fabio & de Nicolo, Gianni, 2003. "On the sources of business cycles in the G-7," Journal of International Economics, Elsevier, vol. 59(1), pages 77-100, January.
    66. Canova, Fabio, 2020. "FAQ: How do I extract the output gap?," Working Paper Series 386, Sveriges Riksbank (Central Bank of Sweden).
    67. James H. Stock & Mark W. Watson, 2012. "Disentangling the Channels of the 2007-09 Recession," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 44(1 (Spring), pages 81-156.
    68. Petrova, Katerina, 2019. "A quasi-Bayesian local likelihood approach to time varying parameter VAR models," Journal of Econometrics, Elsevier, vol. 212(1), pages 286-306.
    69. Daniel F. Waggoner & Tao Zha, 1999. "Conditional Forecasts In Dynamic Multivariate Models," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 639-651, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    2. Ramey, V.A., 2016. "Macroeconomic Shocks and Their Propagation," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 71-162, Elsevier.
    3. Mikkel Plagborg‐Møller & Christian K. Wolf, 2021. "Local Projections and VARs Estimate the Same Impulse Responses," Econometrica, Econometric Society, vol. 89(2), pages 955-980, March.
    4. Dominik Bertsche, 2019. "The effects of oil supply shocks on the macroeconomy: a Proxy-FAVAR approachThe effects of oil supply shocks on the macroeconomy: a Proxy-FAVAR approach," Working Paper Series of the Department of Economics, University of Konstanz 2019-06, Department of Economics, University of Konstanz.
    5. Robin Braun & Ralf Brüggemann, 2017. "Identification of SVAR Models by Combining Sign Restrictions With External Instruments," Working Paper Series of the Department of Economics, University of Konstanz 2017-07, Department of Economics, University of Konstanz.
    6. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2018. "Bayesian Vector Autoregressions," The Warwick Economics Research Paper Series (TWERPS) 1159, University of Warwick, Department of Economics.
    7. Jursa, Lukáš & Janků, Jan, 2025. "From the core to the European periphery: Spillover effects of financial cycles," Emerging Markets Review, Elsevier, vol. 68(C).
    8. Nadav Ben Zeev, 2019. "Is There A Single Shock That Drives The Majority Of Business Cycle Fluctuations?," Working Papers 1906, Ben-Gurion University of the Negev, Department of Economics.
    9. Silvia Miranda-Agrippino & Giovanni Ricco, 2021. "The Transmission of Monetary Policy Shocks," American Economic Journal: Macroeconomics, American Economic Association, vol. 13(3), pages 74-107, July.
    10. Gabor Pinter, 2018. "Macroeconomic Shocks and Risk Premia," Discussion Papers 1812, Centre for Macroeconomics (CFM).
    11. Herwartz, Helmut & Rohloff, Hannes & Wang, Shu, 2022. "Proxy SVAR identification of monetary policy shocks - Monte Carlo evidence and insights for the US," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
    12. Berger, Tino & Richter, Julia & Wong, Benjamin, 2022. "A unified approach for jointly estimating the business and financial cycle, and the role of financial factors," Journal of Economic Dynamics and Control, Elsevier, vol. 136(C).
    13. Lukas Boer & Andrea Pescatori & Martin Stuermer, 2021. "Energy Transition Metals," Discussion Papers of DIW Berlin 1976, DIW Berlin, German Institute for Economic Research.
    14. Frank Schorfheide & Dongho Song, 2015. "Real-Time Forecasting With a Mixed-Frequency VAR," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 366-380, July.
    15. repec:spo:wpmain:info:hdl:2441/27od5pb99881folvtfs8s3k16l is not listed on IDEAS
    16. Francesco Furlanetto & Antoine Lepetit & Ørjan Robstad & Juan Rubio-Ramírez & Pål Ulvedal, 2025. "Estimating Hysteresis Effects," American Economic Journal: Macroeconomics, American Economic Association, vol. 17(1), pages 35-70, January.
    17. Dominik Bertsche & Robin Braun, 2022. "Identification of Structural Vector Autoregressions by Stochastic Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 328-341, January.
    18. Yasuharu Iwata & Hirokuni IIboshi, 2023. "The Nexus between Public Debt and the Government Spending Multiplier: Fiscal Adjustments Matter," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(4), pages 830-858, August.
    19. Nguyen, Lam, 2025. "Bayesian inference in proxy SVARs with incomplete identification: Re-evaluating the validity of monetary policy instruments," Journal of Monetary Economics, Elsevier, vol. 155(C).
    20. Antolín-Díaz, Juan & Petrella, Ivan & Rubio-Ramírez, Juan F., 2021. "Structural scenario analysis with SVARs," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 798-815.
    21. Matthew Read, 2022. "Algorithms for inference in SVARs identified with sign and zero restrictions [Identification and inference with ranking restrictions]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 699-718.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:fip:fedhwp:93029. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Lauren Wiese (email available below). General contact details of provider: https://edirc.repec.org/data/frbchus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.