Studies in Nonlinear Dynamics & Econometrics

Published by De Gruyter

Volume 4 Issue 3

October 2000

Issue of Studies in Nonlinear Dynamics & Econometrics

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Contents
Journal Overview

Article

October 1, 2000

p-Value Adjustments for Multiple Tests for Nonlinearity

Zacharias Psaradakis

Abstract

When the hypothesis of linearity of a univariate time series model is tested using a battery of tests for neglected nonlinearity, the probability of one or more tests' leading to a false rejection increases with the number of tests being performed. This paper discusses how this undesirable effect of multiple testing may be controlled by means of some simple and easily implemented procedures. Monte Carlo experiments are used to demonstrate the finite-sample effectiveness of the various methods, and an analysis of the nonlinearity properties of GDP data from five OECD countries is presented as an illustration.

October 1, 2000

On Nonlinear, Stochastic Dynamics in Economic and Financial Time Series

Christian Schittenkopf, Georg Dorffner, Engelbert J. Dockner

Abstract

The search for deterministic chaos in economic and financial time series has attracted much interest over the past decade. Evidence of chaotic structures is usually blurred, however, by large random components in the time series. In the first part of this paper, a sophisticated algorithm for estimating the largest Lyapunov exponent with confidence intervals is applied to artificially generated and real-world time series. Although the possibility of testing empirically for positivity of the estimated largest Lyapunov exponent is an advantage over other existing methods, the interpretability of the obtained results remains problematic. For instance, it is practically impossible to distinguish chaotic and periodic dynamics in the presence of dynamical noise even for simple dynamical systems. We conclude that the notion of sensitive dependence on initial conditions, as it has been developed for deterministic dynamics, can hardly be transferred into a stochastic context. Therefore, the second part of the paper aims to measure the dependencies of stochastic dynamics on the basis of a distributional characterization of the dynamics. For instance, the dynamics of financial return series are essentially captured by heteroskedastic models. We adopt a sensitivity measure proposed in literature and derive analytical expressions for the most important classes of stochastic dynamics. In practice, the sensitivity measure for the a priori unknown dynamics of a system can be calculated after estimating the conditional density of the system's state variable.

October 1, 2000

Nonlinear Models for U.K. Macroeconomic Time Series

Nadir Öcal

Abstract

This paper examines possible nonlinearities in growth rates of nine U.K. macroeconomic time series, namely gross domestic product, price, consumption, retail sales, personal disposable income, savings, investment, industrial production and unemployment, chosen as representative of series typically used to investigate business cycle fluctuations. By basing analysis on the class of smooth-transition autoregressive (STAR) models, it is assumed that the economy can be in one of two states with distinct dynamics or in transition between these states. Except for consumption, industrial production, and unemployment, I successfully estimate STAR models that pass a set of mis-specification tests. For the former three variables, the results indicate that two-threshold (three-regime) STAR models may be needed for a better description of their dynamics. The comparison of nonlinear models with their linear counterparts shows that although in most of the cases estimated nonlinear models yield lower residual variances and lower root-mean-square errors (RMSEs) in some cases, there is essentially no evidence of nonlinearity according to Diebold and Mariano's (1995) test of equal forecast accuracy. It is worthy of note that my modeling procedure with and without dummy variables introduced to account for abnormal observations suggests that these observations should not be overlooked within the context of STAR-type nonlinear modeling.

Algorithm

October 1, 2000

An Algorithm for Estimating Multivariate Catastrophe Models: GEMCAT II

Rense Lange, Terence A. Oliva, Sean R. McDade

Abstract

Following the framework in Oliva et al. 1987, GEMCAT II implements a flexible method to test catastrophe models containing multivariate (i.e., latent) variables while allowing for a priori variable specifications. The system uses an efficient hybrid minimization algorithm combining the Downhill Simplex and Powell's Conjugate Gradient method. GEMCAT II is compiled in Delphi V3.0 and is sufficiently fast to allow for the use of resampling methods (bootstrap as well as jackknife) to determine the statistical significance of latent variables' indicator weights. In addition, a Pseudo-R2 index of model fit is provided, together with a test of significance, and options are included to facilitate competitive model tests of nested and non-nested catastrophe models as well as linear models. Two simulation studies are reported. Based on 61,250 simulated data sets of varying sizes, the first study addressed the effects of indicator reliability on the quality of the weight estimations, and the second dealt with the problem of false positives in model identification. The results strongly support the viability of the GEMCAT II approach over a wide range of reasonable indicator reliabilities and sample sizes. Moreover, it proved possible to distinguish reliably between cusp catastrophes and linear models based on the Pseudo-R2 values. Finally, GEMCAT II is applied to actual market data in order to demonstrate its use in an economic context. Using 34 quarters of panel data, we examine the fit of a cusp catastrophe model of organizational product adoption as applied to competing software standards in the presence of network externalities. The results are consistent with economic theory and published work on network externalities. This example also illustrates GEMCAT II's bootstrap tests for indicator weights and its options for competitive model testing.

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 2023 for Studies in Nonlinear Dynamics and Econometrics, has been awarded to Thomas Lux for his paper: "Approximate Bayesian Inference for Agent-Based Models in Economics: A Case Study." Studies in Nonlinear Dynamics & Econometrics, vol. 27, no. 4, 2023, pp. 423-447.

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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