IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1802.00793.html
   My bibliography  Save this paper

Structural analysis with mixed-frequency data: A MIDAS-SVAR model of US capital flows

Author

Listed:
  • Emanuele Bacchiocchi
  • Andrea Bastianin
  • Alessandro Missale
  • Eduardo Rossi

Abstract

We develop a new VAR model for structural analysis with mixed-frequency data. The MIDAS-SVAR model allows to identify structural dynamic links exploiting the information contained in variables sampled at different frequencies. It also provides a general framework to test homogeneous frequency-based representations versus mixed-frequency data models. A set of Monte Carlo experiments suggests that the test performs well both in terms of size and power. The MIDAS-SVAR is then used to study how monetary policy and financial market volatility impact on the dynamics of gross capital inflows to the US. While no relation is found when using standard quarterly data, exploiting the variability present in the series within the quarter shows that the effect of an interest rate shock is greater the longer the time lag between the month of the shock and the end of the quarter

Suggested Citation

  • Emanuele Bacchiocchi & Andrea Bastianin & Alessandro Missale & Eduardo Rossi, 2018. "Structural analysis with mixed-frequency data: A MIDAS-SVAR model of US capital flows," Papers 1802.00793, arXiv.org.
  • Handle: RePEc:arx:papers:1802.00793
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1802.00793
    File Function: Latest version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
    2. Forbes, Kristin J. & Warnock, Francis E., 2012. "Capital flow waves: Surges, stops, flight, and retrenchment," Journal of International Economics, Elsevier, vol. 88(2), pages 235-251.
    3. Roberto Rigobon & Brian Sack, 2003. "Measuring The Reaction of Monetary Policy to the Stock Market," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(2), pages 639-669.
    4. Christiano, Lawrence J. & Eichenbaum, Martin, 1987. "Temporal aggregation and structural inference in macroeconomics," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 26(1), pages 63-130, January.
    5. Segal, Gill & Shaliastovich, Ivan & Yaron, Amir, 2015. "Good and bad uncertainty: Macroeconomic and financial market implications," Journal of Financial Economics, Elsevier, vol. 117(2), pages 369-397.
    6. Evgenia Passari & Hélène Rey, 2015. "Financial Flows and the International Monetary System," Economic Journal, Royal Economic Society, vol. 0(584), pages 675-698, May.
    7. Bekaert, Geert & Hoerova, Marie & Lo Duca, Marco, 2013. "Risk, uncertainty and monetary policy," Journal of Monetary Economics, Elsevier, vol. 60(7), pages 771-788.
    8. Gian-Maria Milesi-Ferretti & Cédric Tille, 2011. "The great retrenchment: international capital flows during the global financial crisis [‘The great trade collapse: what caused it and what does it mean?’]," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 26(66), pages 289-346.
    9. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    10. Giovanni Angelini & Emanuele Bacchiocchi & Giovanni Caggiano & Luca Fanelli, 2019. "Uncertainty across volatility regimes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(3), pages 437-455, April.
    11. Jon Faust & John H. Rogers & Eric Swanson & Jonathan H. Wright, 2003. "Identifying the Effects of Monetary Policy Shocks on Exchange Rates Using High Frequency Data," Journal of the European Economic Association, MIT Press, vol. 1(5), pages 1031-1057, September.
    12. Carlo Favero & Francesco Giavazzi, 2008. "Should the Euro Area Be Run as a Closed Economy?," American Economic Review, American Economic Association, vol. 98(2), pages 138-145, May.
    13. Laurent Ferrara & Pierre Guérin, 2018. "What are the macroeconomic effects of high‐frequency uncertainty shocks?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(5), pages 662-679, August.
    14. Carmen M. Reinhart & Vincent R. Reinhart, 2009. "Capital Flow Bonanzas: An Encompassing View of the Past and Present," NBER International Seminar on Macroeconomics, University of Chicago Press, vol. 5(1), pages 9-62.
    15. Caggiano, Giovanni & Castelnuovo, Efrem & Groshenny, Nicolas, 2014. "Uncertainty shocks and unemployment dynamics in U.S. recessions," Journal of Monetary Economics, Elsevier, vol. 67(C), pages 78-92.
    16. Rey, Hélène, 2015. "Dilemma not Trilemma: The Global Financial Cycle and Monetary Policy Independence," CEPR Discussion Papers 10591, C.E.P.R. Discussion Papers.
    17. Emanuele Bacchiocchi & Luca Fanelli, 2015. "Identification in Structural Vector Autoregressive Models with Structural Changes, with an Application to US Monetary Policy," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(6), pages 761-779, December.
    18. Davidson, Russell & MacKinnon, James G, 1998. "Graphical Methods for Investigating the Size and Power of Hypothesis Tests," The Manchester School of Economic & Social Studies, University of Manchester, vol. 66(1), pages 1-26, January.
    19. Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 2005. "Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 1-45, February.
    20. Christiano, Lawrence J. & Eichenbaum, Martin & Evans, Charles L., 1999. "Monetary policy shocks: What have we learned and to what end?," Handbook of Macroeconomics, in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 2, pages 65-148, Elsevier.
    21. Kyle Jurado & Sydney C. Ludvigson & Serena Ng, 2015. "Measuring Uncertainty," American Economic Review, American Economic Association, vol. 105(3), pages 1177-1216, March.
    22. Bruno, Valentina & Shin, Hyun Song, 2015. "Capital flows and the risk-taking channel of monetary policy," Journal of Monetary Economics, Elsevier, vol. 71(C), pages 119-132.
    23. 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.
    24. Stijn Claessens & M. Ayhan Kose & Marco E. Terrones, 2009. "What happens during recessions, crunches and busts? [Business cycles for G-7 and European countries]," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 24(60), pages 653-700.
    25. Nicholas Bloom, 2009. "The Impact of Uncertainty Shocks," Econometrica, Econometric Society, vol. 77(3), pages 623-685, May.
    26. Blanchard, Olivier Jean, 1989. "A Traditional Interpretation of Macroeconomic Fluctuations," American Economic Review, American Economic Association, vol. 79(5), pages 1146-1164, December.
    27. Monika Piazzesi, 2002. "The Fed and Interest Rates - A High-Frequency Identification," American Economic Review, American Economic Association, vol. 92(2), pages 90-95, May.
    28. Christensen, Bent Jesper & Posch, Olaf & van der Wel, Michel, 2016. "Estimating dynamic equilibrium models using mixed frequency macro and financial data," Journal of Econometrics, Elsevier, vol. 194(1), pages 116-137.
    29. Barbara Rossi & Tatevik Sekhposyan, 2015. "Macroeconomic Uncertainty Indices Based on Nowcast and Forecast Error Distributions," American Economic Review, American Economic Association, vol. 105(5), pages 650-655, May.
    30. Andreou, Elena & Ghysels, Eric & Kourtellos, Andros, 2010. "Regression models with mixed sampling frequencies," Journal of Econometrics, Elsevier, vol. 158(2), pages 246-261, October.
    31. repec:dau:papers:123456789/14524 is not listed on IDEAS
    32. Bjørn Eraker & Ching Wai (Jeremy) Chiu & Andrew T. Foerster & Tae Bong Kim & Hernán D. Seoane, 2015. "Bayesian Mixed Frequency VARs," Journal of Financial Econometrics, Oxford University Press, vol. 13(3), pages 698-721.
    33. repec:zbw:bofrdp:2017_035 is not listed on IDEAS
    34. Rothenberg, Thomas J, 1971. "Identification in Parametric Models," Econometrica, Econometric Society, vol. 39(3), pages 577-591, May.
    35. Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009. "Real-Time Measurement of Business Conditions," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
    36. Claudia Foroni & Massimiliano Marcellino, 2014. "Mixed‐Frequency Structural Models: Identification, Estimation, And Policy Analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(7), pages 1118-1144, November.
    37. Òscar Jordà & Moritz Schularick & Alan M Taylor, 2011. "Financial Crises, Credit Booms, and External Imbalances: 140 Years of Lessons," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 59(2), pages 340-378, June.
    38. Ghysels, Eric, 2016. "Macroeconomics and the reality of mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 294-314.
    39. Olivier Blanchard & Roberto Perotti, 2002. "An Empirical Characterization of the Dynamic Effects of Changes in Government Spending and Taxes on Output," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 117(4), pages 1329-1368.
    40. Marcellino, Massimiliano, 1999. "Some Consequences of Temporal Aggregation in Empirical Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 129-136, January.
    41. Domenico Giannone & Lucrezia Reichlin & David Small, 2008. "Nowcasting: the real time informational content of macroeconomic data releases," ULB Institutional Repository 2013/6409, ULB -- Universite Libre de Bruxelles.
    42. Bernanke, Ben S., 1986. "Alternative explanations of the money-income correlation," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 25(1), pages 49-99, January.
    43. Reichlin, Lucrezia & Giannone, Domenico & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers.
    44. Taylor, John B., 1993. "Discretion versus policy rules in practice," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 39(1), pages 195-214, December.
    45. Silvia Miranda-Agrippino & Hélène Rey, 2020. "U.S. Monetary Policy and the Global Financial Cycle," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 87(6), pages 2754-2776.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Alexander Chudik & Georgios Georgiadis, 2022. "Estimation of Impulse Response Functions When Shocks Are Observed at a Higher Frequency Than Outcome Variables," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 965-979, June.
    2. Michael W. McCracken & Michael T. Owyang & Tatevik Sekhposyan, 2021. "Real-Time Forecasting and Scenario Analysis Using a Large Mixed-Frequency Bayesian VAR," International Journal of Central Banking, International Journal of Central Banking, vol. 17(71), pages 1-41, December.
    3. Sarah Goldman & Virginia Zhelyazkova, 2023. "CO2 Emissions and GDP: A Revisited Kuznets Curve Version via a Panel Threshold MIDAS-VAR Model in Europe for a Recent Period," Economic Research Guardian, Mutascu Publishing, vol. 13(2), pages 82-99, December.

    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. Bacchiocchi, Emanuele & Bastianin, Andrea & Missale, Alessandro & Rossi, Eduardo, 2020. "Structural analysis with mixed-frequency data: A model of US capital flows," Economic Modelling, Elsevier, vol. 89(C), pages 427-443.
    2. Georgiadis, Georgios & Jančoková, Martina, 2020. "Financial globalisation, monetary policy spillovers and macro-modelling: Tales from 1001 shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 121(C).
    3. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    4. Cimadomo, Jacopo & Giannone, Domenico & Lenza, Michele & Monti, Francesca & Sokol, Andrej, 2022. "Nowcasting with large Bayesian vector autoregressions," Journal of Econometrics, Elsevier, vol. 231(2), pages 500-519.
    5. 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.
    6. Maria Elena Bontempi & Michele Frigeri & Roberto Golinelli & Matteo Squadrani, 2021. "EURQ: A New Web Search‐based Uncertainty Index," Economica, London School of Economics and Political Science, vol. 88(352), pages 969-1015, October.
    7. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2015. "Markov-switching mixed-frequency VAR models," International Journal of Forecasting, Elsevier, vol. 31(3), pages 692-711.
    8. Gupta, Rangan & Ma, Jun & Risse, Marian & Wohar, Mark E., 2018. "Common business cycles and volatilities in US states and MSAs: The role of economic uncertainty," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 317-337.
    9. Stijn Claessens & M Ayhan Kose, 2018. "Frontiers of macrofinancial linkages," BIS Papers, Bank for International Settlements, number 95.
    10. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2018. "Using low frequency information for predicting high frequency variables," International Journal of Forecasting, Elsevier, vol. 34(4), pages 774-787.
    11. Foroni, Claudia & Marcellino, Massimiliano & Stevanović, Dalibor, 2018. "Mixed frequency models with MA components," Working Paper Series 2206, European Central Bank.
    12. Alessandri, Piergiorgio & Gazzani, Andrea & Vicondoa, Alejandro, 2023. "Are the effects of uncertainty shocks big or small?," European Economic Review, Elsevier, vol. 158(C).
    13. Ruey Yau & C. James Hueng, 2019. "Nowcasting GDP Growth for Small Open Economies with a Mixed-Frequency Structural Model," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 177-198, June.
    14. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Realtime nowcasting with a Bayesian mixed frequency model with stochastic volatility," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 837-862, October.
    15. Libero Monteforte & Valentina Raponi, 2019. "Short‐term forecasts of economic activity: Are fortnightly factors useful?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(3), pages 207-221, April.
    16. Scotti, Chiara, 2016. "Surprise and uncertainty indexes: Real-time aggregation of real-activity macro-surprises," Journal of Monetary Economics, Elsevier, vol. 82(C), pages 1-19.
    17. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2013. "Should Macroeconomic Forecasters Use Daily Financial Data and How?," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 240-251, April.
    18. Bacchiocchi, Emanuele & Dragomirescu-Gaina, Catalin, 2024. "Uncertainty spill-overs: When policy and financial realms overlap," Journal of International Money and Finance, Elsevier, vol. 143(C).
    19. Bjørn Eraker & Ching Wai (Jeremy) Chiu & Andrew T. Foerster & Tae Bong Kim & Hernán D. Seoane, 2015. "Bayesian Mixed Frequency VARs," Journal of Financial Econometrics, Oxford University Press, vol. 13(3), pages 698-721.
    20. Foroni, Claudia & Ravazzolo, Francesco & Rossini, Luca, 2019. "Forecasting daily electricity prices with monthly macroeconomic variables," Working Paper Series 2250, European Central Bank.

    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • 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:arx:papers:1802.00793. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.