Studies in Nonlinear Dynamics and Econometrics 

About: Studies in Nonlinear Dynamics and Econometrics is an academic journal published by De Gruyter. The journal publishes majorly in the area(s): Volatility (finance) & Estimator. It has an ISSN identifier of 1081-1826. Over the lifetime, 701 publications have been published receiving 13824 citations. The journal is also known as: SNDE & Nonlinear dynamics and econometrics.

Inference in TAR Models

Abstract: A distribution theory is developed for least-squares estimates of the threshold in Threshold Autoregressive (TAR) models. We find that if we let the threshold effect (the difference in slopes between the two regimes) become small as the sample size increases, then the asymptotic distribution of the threshold estimator is free of nuisance parameters (up to scale). Similarly, the likelihood ratio statistic for testing hypotheses concerning the unknown threshold is asymptotically free of nuisance parameters. These asymptotic distributions are nonstandard, but are available in closed form, so critical values are readily available. To illustrate this theory, we report an application to the U.S. unemployment rate. We find statistically significant threshold effects.

The Decomposition of Economic Relationships by Time Scale Using Wavelets: Expenditure and Income

Abstract: Economists have long known that time scale matters, in that the structure of decisions as to the relevant time horizon, degree of time aggregation, strength of relationship, and even the relevant variables differ by time scale. Unfortunately, until recently it was difficult to decompose economic time series into orthogonal time-scale components except for the short and long run, in which the former is dominated by noise. This paper uses wavelets to produce an orthogonal decomposition of some economic variables by time scale over six different time scales. The relationship of interest is the permanent income hypothesis. We confirm that time-scale decomposition is very important for analyzing economic relationships and that a number of anomalies previously noted in the literature are explained by these means. The analysis indicates the importance of recognizing variations in phase between variables when investigating the economic relationships.

The Long Memory of the Efficient Market

Abstract: For the London Stock Exchange we demonstrate that the signs of orders obey a long-memory process. The autocorrelation function decays roughly as a power law with an exponent of 0.6, corresponding to a Hurst exponent H = 0.7. This implies that the signs of future orders are quite predictable from the signs of past orders; all else being equal, this would suggest a very strong market inefficiency. We demonstrate, however, that fluctuations in order signs are compensated for by anti-correlated fluctuations in transaction size and liquidity, which are also long-memory processes that act to make the returns whiter. We show that some institutions display long-range memory and others dont.

Energy Shocks and Financial Markets: Nonlinear Linkages

Abstract: This paper examines the dynamic linkages between oil prices and the stock market. Prior work argues that daily oil futures price changes and the S&P 500 stock index movements are not related. This conclusion could be due to the fact that only linear linkages have been examined. Relying on nonlinear causality tests, this study provides evidence that oil shocks affect stock index returns, which is consistent with the documented influence of oil on economic output. Moreover, the study finds that the linkage between oil prices and the stock market was stronger in the 1990s.

A Practitioner's Guide to Lag Order Selection For VAR Impulse Response Analysis

Abstract: It is common in empirical macroeconomics to fit vector autoregressive (VAR) models to construct estimates of impulse responses. An important preliminary step in impulse response analysis is the selection of the VAR lag order. In this paper, we compare the six lag-order selection criteria most commonly used in applied work. Our metric is the mean-squared error (MSE) of the implied pointwise impulse response estimates normalized relative to their MSE based on knowing the true lag order. Based on our simulation design we conclude that for monthly VAR models, the Akaike Information Criterion (AIC) tends to produce the most accurate structural and semi-structural impulse response estimates for realistic sample sizes. For quarterly VAR models, the Hannan-Quinn Criterion (HQC) appears to be the most accurate criterion with the exception of sample sizes smaller than 120, for which the Schwarz Information Criterion (SIC) is more accurate. For persistence profiles based on quarterly vector error correction models with known cointegrating vector, our results suggest that the SIC is the most accurate criterion for all realistic sample sizes.