journal: Studies in Nonlinear Dynamics & Econometrics

Impact Factor: 0.8

Published since April 1, 1996

Studies in Nonlinear Dynamics & Econometrics

ISSN: 1558-3708

Edited by: Bruce Mizrach, Jeremy Piger

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About this journal

Objective
A peer-reviewed journal since 1996, Studies in Nonlinear Dynamics & Econometrics (SNDE) is at the forefront of statistical and theoretical approaches to economics. The journal studies ways in which econometrics and dynamical systems theory increase our understanding of economic and financial markets. The journal disseminates authors' algorithms, programs, and data sets, allowing other scholars to replicate empirical results. Authors include econometricians such as Clive Granger, James Hamilton, and Halbert White, and theorists Jess Benhabib, Alan Kirman, and Kazuo Nishimura.

Best Paper Award
Since 2015, the Society has awarded $500 each year to the best paper published in the Society’s journal: Studies in Nonlinear Dynamics and Econometrics.

The Best Paper in 2022 for Studies in Nonlinear Dynamics and Econometrics, has been awarded to Szabolcs Blazsek, Alvaro Escribano and Adrian Licht. for their paper: "Multivariate Markov-switching score-driven models: an application to the global crude oil market" Studies in Nonlinear Dynamics & Econometrics, vol. 26, no. 3, 2022, pp. 313-335.

The Best Paper in 2021 for Studies in Nonlinear Dynamics and Econometrics, has been awarded to Eric Mark Aldrich and Howard Kung for their paper: “Computational Methods for Production-Based Asset Pricing Models with Recursive Utility”, published in Volume 25, Issue 1.

The Best Paper in 2020 for Studies in Nonlinear Dynamics and Econometrics, has been awarded to Gregor Bäurle, Daniel Kaufmann, Sylvia Kaufmann and Rodney Strachan for their paper: “Constrained interest rates and changing dynamics at the zero lower bound”, published in Volume 24, Issue 2.

The Best Paper in 2019 for Studies in Nonlinear Dynamics and Econometrics, has been awarded to Chang-Jin Kim and Yunmi Kim for their paper: “A unified framework jointly explaining business conditions, stock returns, volatility and “volatility feedback news” effects”, published in Volume 23, Issue 2.

The Best Paper in 2018 for Studies in Nonlinear Dynamics and Econometrics, has been awarded to Luiggi Donayre, Yunjong Eo and James Morley for their paper: “Improving likelihood-ratio-based confidence intervals for threshold parameters in finite samples”,published in Volume 22, Issue 1 (Feb 2018)

The Best Paper in 2017 for Studies in Nonlinear Dynamics and Econometrics has been awarded to Julien Chevallier and Stephane Goutte for their paper: “On the estimation of regime-switching Levy models”, published in Volume 21, Issue 1 (Feb 2017).

The Best Paper in 2016 for Studies in Nonlinear Dynamics and Econometrics has been awarded to Mark J. Jensen for his paper: “Robust estimation of nonstationary, fractionally integrated, autoregressive, stochastic volatility?”, published in Volume 20, Issue 4 (Sept. 2016).

The Best Paper in 2015 for Studies in Nonlinear Dynamics and Econometrics has been awarded to Markus Jochmann and Gary Koop for their paper: “Regime-switching cointegration?”, published in Volume 19, Issue 1 (Feb 2015).

Topics

Probability
Statistics
Macroeconomics
Finance
Forecasting
Econometrics

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Research articles

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Your Benefits

Your benefits:

Theoretical and applied studies that characterize and motivate nonlinear phenomena
State-of the-art research results that increase understanding of economic and financial markets
Novel tools that allow replication of empirical results
High quality peer-review
International and renowned editorial board

History

Content available since 1996 (Volume 1, Issue 1)

Abstracting and Indexing

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Volume 27 Issue 4

September 2023

Publicly Available September 28, 2023

Frontmatter

Page range: i-ii

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Research Articles

June 27, 2022

Approximate Bayesian inference for agent-based models in economics: a case study

Thomas Lux

Page range: 423-447

Abstract

Estimation of agent-based models in economics and finance confronts researchers with a number of challenges. Typically, the complex structures of such models do not allow to derive closed-form likelihood functions so that either numerical approximations to the likelihood or moment-based estimators have to be used for parameter inference. However, all these approaches suffer from extremely high computational demands as they typically work with simulations (of the agent-based model) embedded in (Monte Carlo) simulations conducted for the purpose of parameter identification. One approach that is very generally applicable and that has the potential of alleviating the computational burden is Approximate Bayesian Computation (ABC). While popular in other areas of agent-based modelling, it seems not to have been used so far in economics and finance. This paper provides an introduction to this methodology and demonstrates its potential with the example of a well-studied model of speculative dynamics. As it turns out, ABC appears to make more efficient use of moment-based information than frequentist SMM (Simulated Method of Moments), and it can be used for sample sizes of an order far beyond the reach of numerical likelihood methods.

Open Access October 10, 2022

Anticipating extreme losses using score-driven shape filters

Astrid Ayala, Szabolcs Blazsek, Alvaro Escribano

Page range: 449-484

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Abstract

We suggest a new value-at-risk (VaR) framework using EGARCH (exponential generalized autoregressive conditional heteroskedasticity) models with score-driven expected return, scale, and shape filters. We use the EGB2 (exponential generalized beta of the second kind), NIG (normal-inverse Gaussian), and Skew-Gen- t (skewed generalized- t ) distributions, for which the score-driven shape parameters drive the skewness, tail shape, and peakedness of the distribution. We use daily data on the Standard & Poor’s 500 (S&P 500) index for the period of February 1990 to October 2021. For all distributions, likelihood-ratio (LR) tests indicate that several EGARCH models with dynamic shape are superior to the EGARCH models with constant shape. We compare the realized volatility with the conditional volatility estimates, and we find two Skew-Gen- t specifications with dynamic shape, which are superior to the Skew-Gen- t specification with constant shape. The shape parameter dynamics are associated with important events that affected the stock market in the United States (US). VaR backtesting is performed for the dot.com boom (January 1997 to October 2020), the 2008 US Financial Crisis (October 2007 to March 2009), and the coronavirus disease (COVID-19) pandemic (January 2020 to October 2021). We show that the use of the dynamic shape parameters improves the VaR measurements.

September 28, 2022

Does real interest rate parity really work? Historical evidence from a discrete wavelet perspective

Mahdi Ghaemi Asl, Giorgio Canarella, Stephen M. Miller, Hamid Reza Tavakkoli

Page range: 485-518

Abstract

We test for real interest rate parity using data from six European countries (France, Germany, Italy, the Netherlands, Spain, and the United Kingdom), Japan, and the United States over a period of more than two centuries. Our contribution is threefold. First, we implement a wavelet-based analysis, which examines both frequency and time information contained in a time series. Second, we employ the United States, the United Kingdom, and Germany as alternative base countries in the wavelet regressions to ascertain the sensitivity of the results to the choice of the base country. Third, we test the real interest rate parity over the entire period (1800–2018) and for several non-contiguous subperiods that hold historical significance and relative importance. Three subperiods link to the three globalization waves (1870–1914, 1944–1971, and 1989–2018), and four subperiods connect to the exchange rate regimes. The wavelet-based results suggest that the validity of the real interest rate parity is scale-dependent. The specific evidence in most cases supports the parity at lower frequencies but not at higher frequencies, which is consistent with the idea that the purchasing power parity and uncovered interest parity, the two main ingredients of the real interest rate parity, are mostly valid in the long run.

September 12, 2022

The impact of forward guidance and large-scale asset purchase programs on commodity markets

Pedro Gomis-Porqueras, Shuddhasattwa Rafiq, Wenying Yao

Page range: 519-551

Abstract

This paper investigates how different commodity prices are affected by unconventional monetary policies (UMP) implemented by the Federal Reserve of the United States as a response to the Global Financial Crisis. We analyze impulse responses using local projections and identify UMP shocks through high-frequency identification strategy. We show that forward guidance (FG) and large-scale asset purchase (LSAP) shocks lead to distinct responses when analyzing commodity prices. We find that asset-like commodities, such as gold and silver, respond to these UMP shocks most aggressively. While an easing FG shock leads to increases in their prices, an easing LSAP shock has the opposite effect. This differential response suggests that these asset-like commodities are being used as inflation and exchange rate hedges. In contrast, production-like and agricultural commodities respond to UMP shocks in the same way as conventional monetary policy shocks. Consistent with previous literature, we find that easing LSAP shocks, to some extent, signal a negative economic outlook. Policymakers can exploit the responses from the various commodity classes examined in this paper when evaluating the effectiveness of monetary policy in different sectors of the economy.

September 6, 2022

Middle-income traps and complexity in economic development

Takao Asano, Akihisa Shibata, Masanori Yokoo

Page range: 553-565

Abstract

In this paper, we develop a simple multi-technology overlapping generations model that exhibits a wide variety of economic development patterns. In particular, our numerical simulations demonstrate that for a given set of parameter values, various types of development patterns such as the middle-income trap, the poverty trap, periodic or chaotic fluctuations, and high-income paths can coexist, and which pattern is realized depends only on the initial value of capital. For another set of parameter values, we show that, due to the pinball effect, an economy starting at a middle-income level can take off to the high-income state or get caught in the poverty trap in a seemingly random way after undergoing transient chaotic motions. Our results can explain observed complicated patterns of economic development in a unified manner.

November 2, 2022

Bayesian inference for order determination of double threshold variables autoregressive models

Xiaobing Zheng, Qiang Xia, Rubing Liang

Page range: 567-587

Abstract

The reversible-jump Markov chain Monte Carlo (RJMCMC) algorithm can generate a jump Markov chain in the parameter space of different dimensions, and select a suitable model effectively. In this paper, when the order of the double threshold variables autoregressive (DT-AR) is unknown, the RJMCMC method is designed to identify the order of the DT-AR model in this paper. The simulation experiments and the real example show that the proposed method works well in identifying the order and estimating the parameters of the DT-AR model simultaneously.

July 21, 2022

Score-driven multi-regime Markov-switching EGARCH: empirical evidence using the Meixner distribution

Szabolcs Blazsek, Michel Ferreira Cardia Haddad

Page range: 589-634

Abstract

In this paper, statistical and volatility forecasting performances of the non-path-dependent score-driven multi-regime Markov-switching (MS) exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models are explored. Three contributions to the existing literature are provided. First, we use all relevant score-driven distributions from the literature - namely, the Student’s t -distribution, general error distribution (GED), skewed generalized t -distribution (Skew-Gen- t ), exponential generalized beta distribution of the second kind (EGB2), and normal-inverse Gaussian (NIG) distribution. We then introduce the score-driven Meixner (MXN) distribution-based EGARCH model to the literature on score-driven models. Second, proving the sufficient conditions of the asymptotic properties of the maximum likelihood (ML) estimator for non-path-dependent score-driven MS-EGARCH models is an unsolved problem. We provide a partial solution to that problem by proving necessary conditions for the asymptotic theory of the ML estimator. Third, to the best of our knowledge, this work includes the largest number of international stock indices from the G20 countries in the literature, covering the period of 2000–2022. We provide a discussion on the major events which caused common or non-common switching to the high-volatility regime for the G20 countries. The statistical performance and volatility forecasting results support the adoption of score-driven MS-EGARCH for the G20 countries.

Ahead of Print / Just Accepted

Ahead of Print / Just Accepted

Volume 27 (2023)

Volume 26 (2022)

Volume 25 (2021)

Volume 24 (2020)

Volume 23 (2019)

Volume 22 (2018)

Volume 21 (2017)

Volume 20 (2016)

Volume 19 (2015)

Volume 18 (2014)

Volume 17 (2013)

Volume 16 (2012)

Volume 15 (2011)

Volume 14 (2010)

Volume 13 (2009)

Volume 12 (2008)

Volume 11 (2007)

Volume 10 (2006)

Volume 9 (2005)

Volume 8 (2004)

Volume 7 (2003)

Volume 6 (2003)

Volume 5 (2002)

Volume 4 (2001)

Volume 3 (1999)

Volume 2 (1998)

Volume 1 (1997)

Journal Impact Factor	0.8	2022, Journal Citation Reports (Clarivate, 2023)
5-year Journal Impact Factor	0.9	2022, Journal Citation Reports (Clarivate, 2023)
Journal Citation Indicator	0.29	2022, Journal Citation Reports (Clarivate, 2023)
CiteScore	1.4	2022, Scopus (Elsevier B.V., 2023)
SCImago Journal Rank	0.467	2022, SJR (Scimago Lab, 2023; Data Source: Scopus)
Source Normalized Impact per Paper	0.721	2022, CWTS Journal Indicators (CWTS B.V., 2023; Data Source: Scopus)
Mathematical Citation Quotient	0.06

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Editorial

Editor-in-Chief

Bruce Mizrach, Rutgers University
Jeremy Piger, University of Oregon

Associate Editors
Heather Anderson, Monash University
Robert A. Becker, Indiana University
Hilde C. Bjørnland, BI Norwegian Business School
Francesco Ravazzolo, University of Bolzano
Yoosoon Chang, Indiana University
Robert de Jong, Ohio State University
Cees Diks, CeNDEF, U. of Amsterdam
Alvaro Escribano, Universidad Carlos III de Madrid
Massimo Guidolin, Bocconi University
Dimitris Korobilis, University of Glasgow
Tae-Hwy Lee, University of California-Riverside
Jun Ma, Northeastern University
Vance Martin, University of Melbourne
J. Isaac (Zack) Miller, University of Missouri
Stefan Mittnik, University of Munich
James Morley, University of New South Wales
Michael T. Owyang, Assistant Vice President, Federal Reserve Bank of St. Louis
Philip Rothman, East Carolina University
Tatevik Sekhposyan, Texas A&M University
Martin Sola, Birkbeck College
Gerhard Sorger, University of Vienna
Ruey Tsay, University of Chicago
Dick van Dijk, Erasmus University
Anastasios Xepapadeas, Athens University of Economics and Business

Advisory Panel

Jess Benhabib, New York University
William A. Brock, University of Wisconsin-Madison
Jean-Michel Grandmont, CREST-CNRS
James D. Hamilton, University of California-San Diego
Jose Scheinkman, Princeton University

(Deutsch)

Online ISSN: 1558-3708
Type: Journal
Language: English
Publisher: De Gruyter
First published: April 1, 1996
Publication Frequency: 5 Issues per Year
Audience: Researchers and professionals in the field of econometrics, economic and financial markets