Simulation smoothing for nowcasting with large mixed-frequency VARs

Abstract

There is currently an increasing interest in large vector autoregressive (VAR) models. VARs are popular tools for macroeconomic forecasting and use of larger models has been demonstrated to often improve the forecasting ability compared to more traditional small-scale models. Mixed-frequency VARs deal with data sampled at different frequencies while remaining within the realms of VARs. Estimation of mixed-frequency VARs makes use of simulation smoothing, but using the standard procedure these models quickly become prohibitive in nowcasting situations as the size of the model grows.We propose two algorithms that alleviate the computational efficiency of the simulation smoothing algorithm. Our preferred choice is an adaptive algorithm, which augments the state vector as necessary to sample also monthly variables that are missing at the end of the sample. For large VARs, we find considerable improvements in speed using our adaptive algorithm. The algorithm therefore provides a crucial building block for bringing the mixed-frequency VARs to the high-dimensional regime.

A Flexible Mixed-Frequency Vector Autoregression with a Steady-State Prior

“…The above state-space model remains valid as long as t ≤ T b , implying that all of the monthly series are observed. To deal with ragged edges and unbalanced monthly data for t = T b + 1, we follow Ankargren and Jonéus (2019) and adaptively add the monthly series with missing data as appropriate. Contrary to Schorfheide and Song (2015), we thereby avoid use of the full companion form altogether.…”

“…To increase the computational efficiency, we implement it using the compact formulation for the balanced part of the sample as suggested by Schorfheide and Song (2015). For the unbalanced ragged edge, we instead leverage the adaptive procedure developed by Ankargren and Jonéus (2019). The simulation smoothing step is conducted based on the mean-adjusted data ỹt to produce a draw of zt .…”