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Simulation Smoothing for Nowcasting with Large Mixed-Frequency VARs
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.ORCID iD: 0000-0003-4415-8734
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2019 (English)In: Article in journal (Other academic) Submitted
Abstract [en]

There is currently an increasing interest in large vector autoregressive (VAR) models. VARs are popular tools for macro-economic 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.

Place, publisher, year, edition, pages
2019.
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-391495OAI: oai:DiVA.org:uu-391495DiVA, id: diva2:1345139
Available from: 2019-08-23 Created: 2019-08-23 Last updated: 2019-08-23
In thesis
1. VAR Models, Cointegration and Mixed-Frequency Data
Open this publication in new window or tab >>VAR Models, Cointegration and Mixed-Frequency Data
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis consists of five papers that study two aspects of vector autoregressive (VAR) modeling: cointegration and mixed-frequency data.

Paper I develops a method for estimating a cointegrated VAR model under restrictions implied by the economy under study being a small open economy. Small open economies have no influence on surrounding large economies. The method suggested by Paper I provides a way to enforce the implied restrictions in the model. The method is illustrated in two applications using Swedish data, and we find that differences in impulse responses resulting from failure to impose the restrictions can be considerable.

Paper II considers a Bayesian VAR model that is specified using a prior distribution on the unconditional means of the variables in the model. We extend the model to allow for the possibility of mixed-frequency data with variables observed either monthly or quarterly. Using real-time data for the US, we find that the accuracy of the forecasts is generally improved by leveraging mixed-frequency data, steady-state information, and a more flexible volatility specification.

The mixed-frequency VAR in Paper II is estimated using a state-space formulation of the model. Paper III studies this step of the estimation algorithm in more detail as the state-space step becomes prohibitive for larger models when the model is employed in real-time situations. We therefore propose an improvement of the existing sampling algorithm. Our suggested algorithm is adaptive and provides considerable improvements when the size of the model is large. The described approach makes the use of large mixed-frequency VARs more feasible for nowcasting.

Paper IV studies the estimation of large mixed-frequency VARs with stochastic volatility. We employ a factor stochastic volatility model for the error term and demonstrate that this allows us to improve upon the algorithm for the state-space step further. In addition, regression parameters can be sampled independently in parallel. We draw from the literature on large VARs estimated on single-frequency data and estimate mixed-frequency models with 20, 34 and 119 variables.

Paper V provides an R package for estimating mixed-frequency VARs. The package includes the models discussed in Paper II and IV as well as additional alternatives. The package has been designed with the intent to make the process of specification, estimation and processing simple and easy to use. The key functions of the package are implemented in C++ and are available for other packages to use and build their own mixed-frequency VARs.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2019. p. 45
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Social Sciences, ISSN 1652-9030 ; 170
Keywords
vector error correction, small open economy, mixed-frequency data, Bayesian, steady state, nowcasting, state-space model, large VARs, simulation smoothing, factor stochastic volatility, R
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:uu:diva-391500 (URN)978-91-513-0734-3 (ISBN)
Public defence
2019-10-11, Hörsal 2, Kyrkogårdsgatan 10, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2019-09-20 Created: 2019-08-23 Last updated: 2019-10-15

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Ankargren, Sebastian

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