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Econometrics
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High-Dimensional Time Series in Macroeconomics and Finance
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High-Dimensional Time Series in Macroeconomics and Finance

A special issue of Econometrics (ISSN 2225-1146).

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 11612

Special Issue Editors

Prof. Dr. Benedikt Poetscher

E-Mail Website
Guest Editor
Department of Statistics and Operations Research, University of Vienna, 1090 Vienna, Austria
Interests: statistics and econometrics
Dr. Leopold Sögner

E-Mail Website
Guest Editor
Institute for Advanced Studies, 1080 Vienna, Austria
Interests: quantitative finance; applied econometrics; Bayesian econometrics; financial econometrics
Prof. Dr. Martin Wagner

E-Mail Website
Guest Editor
Department of Economics, University of Klagenfurt, 9020 Klagenfurt, Austria
Interests: econometrics; quantitative economics; transition economics; environmental economics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue primarily, but by no means not exclusively, intends to provide a publication outlet for papers presented at the 5th Vienna Workshop on High-Dimensional Time Series in Macroeconomics and Finance, which is due to take place from June 9 to 10, 2022 at the Institute for Advanced Studies in Vienna, Austria.

https://www.ihs.ac.at/events/conference-series/time-series-workshops/time-series-workshop-2021/

In the context of this workshop, this Special Issue aims provide a forum for exchanging ideas and recent results in high-dimensional time series analyses. Both theoretical papers as well as papers with a focus on macroeconomic or financial applications are most welcome. Furthermore, high-level survey papers that compare different approaches and well-crafted performance comparisons across methods are of interest. We expect this Special Issue to contain a selection of papers that reflect the current status and some promising research avenues for the future.

Prof. Dr. Benedikt Poetscher
Dr. Leopold Sögner
Prof. Dr. Martin Wagner
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Econometrics is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • time series analysis
  • factor models
  • system identification
  • macro-econometrics
  • financial econometrics

Published Papers (6 papers)

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Research

30 pages, 583 KiB  
Article
When It Counts—Econometric Identification of the Basic Factor Model Based on GLT Structures
by Sylvia Frühwirth-Schnatter, Darjus Hosszejni and Hedibert Freitas Lopes
Econometrics 2023, 11(4), 26; https://doi.org/10.3390/econometrics11040026 - 20 Nov 2023
Cited by 3 | Viewed by 1227
Abstract
Despite the popularity of factor models with simple loading matrices, little attention has been given to formally address the identifiability of these models beyond standard rotation-based identification such as the positive lower triangular (PLT) constraint. To fill this gap, we review the advantages [...] Read more.
Despite the popularity of factor models with simple loading matrices, little attention has been given to formally address the identifiability of these models beyond standard rotation-based identification such as the positive lower triangular (PLT) constraint. To fill this gap, we review the advantages of variance identification in simple factor analysis and introduce the generalized lower triangular (GLT) structures. We show that the GLT assumption is an improvement over PLT without compromise: GLT is also unique but, unlike PLT, a non-restrictive assumption. Furthermore, we provide a simple counting rule for variance identification under GLT structures, and we demonstrate that within this model class, the unknown number of common factors can be recovered in an exploratory factor analysis. Our methodology is illustrated for simulated data in the context of post-processing posterior draws in sparse Bayesian factor analysis. Full article
(This article belongs to the Special Issue High-Dimensional Time Series in Macroeconomics and Finance)
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11 pages, 313 KiB  
Article
Factorization of a Spectral Density with Smooth Eigenvalues of a Multidimensional Stationary Time Series
by Tamás Szabados
Econometrics 2023, 11(2), 14; https://doi.org/10.3390/econometrics11020014 - 31 May 2023
Viewed by 1065
Abstract
The aim of this paper to give a multidimensional version of the classical one-dimensional case of smooth spectral density. A spectral density with smooth eigenvalues and H∞ eigenvectors gives an explicit method to factorize the spectral density and compute the Wold representation [...] Read more.
The aim of this paper to give a multidimensional version of the classical one-dimensional case of smooth spectral density. A spectral density with smooth eigenvalues and H∞ eigenvectors gives an explicit method to factorize the spectral density and compute the Wold representation of a weakly stationary time series. A formula, similar to the Kolmogorov–Szego” formula, is given for the covariance matrix of the innovations. These results are important to give the best linear predictions of the time series. The results are applicable when the rank of the process is smaller than the dimension of the process, which occurs frequently in many current applications, including econometrics. Full article
(This article belongs to the Special Issue High-Dimensional Time Series in Macroeconomics and Finance)
15 pages, 492 KiB  
Article
Modeling COVID-19 Infection Rates by Regime-Switching Unobserved Components Models
by Paul Haimerl and Tobias Hartl
Econometrics 2023, 11(2), 10; https://doi.org/10.3390/econometrics11020010 - 03 Apr 2023
Viewed by 2441
Abstract
The COVID-19 pandemic is characterized by a recurring sequence of peaks and troughs. This article proposes a regime-switching unobserved components (UC) approach to model the trend of COVID-19 infections as a function of this ebb and flow pattern. Estimated regime probabilities indicate the [...] Read more.
The COVID-19 pandemic is characterized by a recurring sequence of peaks and troughs. This article proposes a regime-switching unobserved components (UC) approach to model the trend of COVID-19 infections as a function of this ebb and flow pattern. Estimated regime probabilities indicate the prevalence of either an infection up- or down-turning regime for every day of the observational period. This method provides an intuitive real-time analysis of the state of the pandemic as well as a tool for identifying structural changes ex post. We find that when applied to U.S. data, the model closely tracks regime changes caused by viral mutations, policy interventions, and public behavior. Full article
(This article belongs to the Special Issue High-Dimensional Time Series in Macroeconomics and Finance)
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30 pages, 504 KiB  
Article
Causal Vector Autoregression Enhanced with Covariance and Order Selection
by Marianna Bolla, Dongze Ye, Haoyu Wang, Renyuan Ma, Valentin Frappier, William Thompson, Catherine Donner, Máté Baranyi and Fatma Abdelkhalek
Econometrics 2023, 11(1), 7; https://doi.org/10.3390/econometrics11010007 - 24 Feb 2023
Viewed by 2072
Abstract
A causal vector autoregressive (CVAR) model is introduced for weakly stationary multivariate processes, combining a recursive directed graphical model for the contemporaneous components and a vector autoregressive model longitudinally. Block Cholesky decomposition with varying block sizes is used to solve the model equations [...] Read more.
A causal vector autoregressive (CVAR) model is introduced for weakly stationary multivariate processes, combining a recursive directed graphical model for the contemporaneous components and a vector autoregressive model longitudinally. Block Cholesky decomposition with varying block sizes is used to solve the model equations and estimate the path coefficients along a directed acyclic graph (DAG). If the DAG is decomposable, i.e., the zeros form a reducible zero pattern (RZP) in its adjacency matrix, then covariance selection is applied that assigns zeros to the corresponding path coefficients. Real-life applications are also considered, where for the optimal order p≥1 of the fitted CVAR(p) model, order selection is performed with various information criteria. Full article
(This article belongs to the Special Issue High-Dimensional Time Series in Macroeconomics and Finance)
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9 pages, 291 KiB  
Article
Manfred Deistler and the General-Dynamic-Factor-Model Approach to the Statistical Analysis of High-Dimensional Time Series
by Marc Hallin
Econometrics 2022, 10(4), 37; https://doi.org/10.3390/econometrics10040037 - 13 Dec 2022
Viewed by 1670
Abstract
For more than half a century, Manfred Deistler has been contributing to the construction of the rigorous theoretical foundations of the statistical analysis of time series and more general stochastic processes. Half a century of unremitting activity is not easily summarized in a [...] Read more.
For more than half a century, Manfred Deistler has been contributing to the construction of the rigorous theoretical foundations of the statistical analysis of time series and more general stochastic processes. Half a century of unremitting activity is not easily summarized in a few pages. In this short note, we chose to concentrate on a relatively little-known aspect of Manfred’s contribution that nevertheless had quite an impact on the development of one of the most powerful tools of contemporary time series and econometrics: dynamic factor models. Full article
(This article belongs to the Special Issue High-Dimensional Time Series in Macroeconomics and Finance)
26 pages, 395 KiB  
Article
Linear System Challenges of Dynamic Factor Models
by Brian D. O. Anderson, Manfred Deistler and Marco Lippi
Econometrics 2022, 10(4), 35; https://doi.org/10.3390/econometrics10040035 - 06 Dec 2022
Cited by 3 | Viewed by 1612
Abstract
A survey is provided dealing with the formulation of modelling problems for dynamic factor models, and the various algorithm possibilities for solving these modelling problems. Emphasis is placed on understanding requirements for the handling of errors, noting the relevance of the proposed application [...] Read more.
A survey is provided dealing with the formulation of modelling problems for dynamic factor models, and the various algorithm possibilities for solving these modelling problems. Emphasis is placed on understanding requirements for the handling of errors, noting the relevance of the proposed application of the model, be it for example prediction or business cycle determination. Mixed frequency problems are also considered, in which certain entries of an underlying vector process are only available for measurement at a submultiple frequency of the original process. Certain classes of processes are shown to be generically identifiable, and others not to have this property. Full article
(This article belongs to the Special Issue High-Dimensional Time Series in Macroeconomics and Finance)
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