💡NEW: all of it :)
Last Updated: Mar 28, 2019

C O D E & D A T A

This section collects data and MatLab codes that I have used in my papers. The content is organized per topic. Replication material for individual papers is marked within each topic.


  1. High-Frequency

    Informationally robust monetary policy surprises at monthly and at FOMC meeting frequency for the US as used in Miranda-Agrippino & Ricco (2016, 2018). All series control for central bank information effects; monthly series also control for autocorrelation in monetary surprises. Details on the construction of the series are in Miranda-Agrippino & Ricco (2016) “The Transmission of Monetary Policy Shocks

    🔸download series

    Monthly monetary policy surprises corrected for Central Bank internal forecasts for the US and the UK as used in Miranda-Agrippino (2016). Details on the construction of the series are in Miranda-Agrippino (2016) Unsurprising Shocks: Information, Premia and the Monetary Transmission

    🔸download series

    🔸download Online Appendix

  2. Narrative (extension of Romer & Romer 2004 work)

    Monthly and quarterly monetary policy surprises for the US constructed using narrative accounts of FOMC meetings since 1960. Series end at onset of ZLB. The folder contains data and documentation for the construction, and comparison with the original R&R series. Used in Miranda-Agrippino and Rey (2015), Miranda-Agrippino & Ricco (2016), Miranda-Agrippino, Hacioglu-Hoke & Bluwstein (2018)

    🔸download series & documentation


The folder contains replication files for Miranda-Agrippino & Ricco (2016) “The Transmission of Monetary Policy Shocks. The code estimates impulse response functions for a Bayesian Vector Autoregression with standard NIW priors, Local Projections, and Bayesian Local Projections. Identification can be one of either Cholesky or External Instruments (BSVAR-IV & BLP-IV)

🔸download BLP replication files (1.1 MB)


The folder contains MatLab code for estimation of impulse response functions (IRFs) in structural OLS VARs identified with external instruments and compares it with standard Cholesky ordering. In the SVAR-IV the two-step estimation and identification algorithm allows for different samples in the VAR innovations and selected IV. Standard errors are calculated using a Wild Bootstrap. The code replicates Figure IV of Miranda-Agrippino (2016) Unsurprising Shocks: Information, Premia and the Monetary Transmission

🔸download MatLab code (450 KB)


Replication data for Miranda-Agrippino & Rey (2015) “US Monetary Policy and the Global Financial Cycle

  • Global Factor In World Risky Asset Prices

    Global common factor estimated from world-wide cross section of risky asset prices. The data sheet contains two versions of the factor: 1) one estimated from 858 price series from 1990 to 2012; 2) one estimated from 3 from 1975 to 2010. All data are monthly. Details are in Miranda-Agrippino & Rey (2015)

    🔸download series


    The data sheets contain the data used in the VARs estimated in Miranda-Agrippino & Rey (2015). There are two versions of it. One (heavy) includes details on the construction of each series and interpolation of quarterly GFC variables into monthly equivalents. A lite version only includes data already transformed as they enter the GFC VARs. Details on composition and data construction are in the data appendix of Miranda-Agrippino & Rey (2015)

    🔸download GFC dataset (Lite, 300 KB)

    🔸download GFC dataset (Full, 1.5 MB)


The folder contains MatLab code for estimation of factor models under different modelling assumptions. 1) Static and Exact with spherical idiosyncratic variance (PC); 2) Static with diagonal idio variance (EM algorithm); 3) Dynamic Factor Model (EM algorithm). The code is associated with the Lecture Slides on Factor Models prepared for a guest lecture at the University of Surrey

🔸download MatLab code

🔸download Factor Models lecture slides


The data sheet contains the real-time mixed-frequency dataset for the UK used in Anesti, Galvao and Miranda-Agrippino (2018) “Uncertain Kingdom: Nowcasting GDP and its Revisions”. Original ONS vintages come from the archives of the BoE. Release dates/times are either gathered from official sources or from Bloomberg’s ECO Calendar

🔸download UK real-time dataset (1.2 MB)