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Special Issue "High-Dimensional Time Series in Macroeconomics and Finance"

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

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 7250

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Special Issue Editors

Benedikt Poetscher
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Guest Editor

Department of Statistics and Operations Research, University of Vienna, 1090 Vienna, Austria
Interests: statistics and econometrics

Leopold Sögner

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Guest Editor

Institute for Advanced Studies, 1080 Vienna, Austria
Interests: quantitative finance; applied econometrics; Bayesian econometrics; financial econometrics

Martin Wagner

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Guest Editor

Department of Economics, University of Klagenfurt, 9020 Klagenfurt, Austria
Interests: econometrics; quantitative economics; transition economics; environmental economics
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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 (5 papers)

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Research

Open AccessArticle

Factorization of a Spectral Density with Smooth Eigenvalues of a Multidimensional Stationary Time Series

Tamás Szabados

Econometrics 2023, 11(2), 14; https://doi.org/10.3390/econometrics11020014 - 31 May 2023

Viewed by 628

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^{\infty}

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^{\infty}

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–Szeg

\overset{”}{o}

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)

Open AccessArticle

Modeling COVID-19 Infection Rates by Regime-Switching Unobserved Components Models

Paul Haimerl

and

Tobias Hartl

Econometrics 2023, 11(2), 10; https://doi.org/10.3390/econometrics11020010 - 03 Apr 2023

Viewed by 1565

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 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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Open AccessArticle

Causal Vector Autoregression Enhanced with Covariance and Order Selection

and

Econometrics 2023, 11(1), 7; https://doi.org/10.3390/econometrics11010007 - 24 Feb 2023

Viewed by 1333

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 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 \geq 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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Figure 1

Open AccessArticle

Manfred Deistler and the General-Dynamic-Factor-Model Approach to the Statistical Analysis of High-Dimensional Time Series

Marc Hallin

Econometrics 2022, 10(4), 37; https://doi.org/10.3390/econometrics10040037 - 13 Dec 2022

Viewed by 1325

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 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)

Open AccessArticle

Linear System Challenges of Dynamic Factor Models

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 1 | Viewed by 1227

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 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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