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Florian Huber
;
Gary Koop

subspace shrinkage in conjugate bayesian vector autoregressions (replication data)

For the empirical exercise we use quarterly macroeconomic data for the US, obtained from the FRED-QD database (https://research.stlouisfed.org/econ/mccracken/fred-databases/).

We provide the raw-data as .csv-file (makroUS_Q.csv) with the columns referring to the different variables. Additionally, we provide the already transformed dataset as .rda-object (makroUS_Q_16.rda). The .rda-object contains two objects. Here, "Xraw.stat" refers to the stationary data and contains the variables used for the forecasting exercise, while "Xraw.int" and "Xraw.mix" store the data in levels and mixed transformations. Both are not used.

Our dataset spans from 1960:Q1 up to 2020:Q2. In Appendix C we provide detailed informations on the transformation applied for each variable. We transform the data to stationarity, according to the suggestions of McCracken and Ng (2016). Moreover, Table 6 shows the specific set of variables included in each specification in Section 5.

We also include R scripts to estimate the conjugate VAR with a subspace shrinkage prior. The replication files include scripts that carry out the simulations (for a single draw from the DGP) and the forecasts (for a single time point in the hold out).

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Huber, Florian; Koop, Gary (2023): Subspace shrinkage in conjugate Bayesian vector autoregressions (replication data). Version: 1. Journal of Applied Econometrics. Dataset. http://dx.doi.org/10.15456/jae.2023031.1448252680

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