Anne Peguin-Feissolle

Persistent misalignments of the European exchange rates: some evidence from non-linear cointegration

The asymmetric and persistent adjustment of the European real exchange rates is investigated using the framework of non-linear cointegration. The episodes of slow mean-reversion dynamics over the period from 1979 to 1999 are explained. A test of unit root against STAR cointegration is proposed and some complete estimations and stochastic simulations of ESTAR models are presented. The presence of effective non-linear adjustment during the moving of the currencies to their long-run fundamental equilibrium exchange rate value is discussed.

Business cycles asymmetry and monetary policy: a further investigation using MRSTAR models

This paper investigates the asymmetric effects of monetary shocks when the impact of monetary policy on real activity works through state-dependent variables. We use a nonlinear model, the multiple regime smooth transition autoregressive model, that allows the effects of shocks to vary across the business cycles when monetary innovations modify both the endogenous and state variables. Our impulse response functions show a history-dependence property. Indeed, hitting the economy at a given time induces persistence and asymmetric responses across histories and shocks. The empirical application concerns the US over the period 1975:1–1998:2.

A comparison of the power of some tests for conditional heteroscedasticity

This paper compares the power in small samples of different tests for conditional heteroscedasticity. Two new tests, based on neural networks, are proposed: the main interest in them arises from the fact that they do not require the exact specification of the conditional variance under the alternative.

Testing the Granger Noncausality Hypothesis in Stationary Nonlinear Models of Unknown Functional Form

In this article, we propose a general method for testing the Granger noncausality hypothesis in stationary nonlinear models of unknown functional form. These tests are based on a Taylor expansion of the nonlinear model around a given point in the sample space. We study the performance of our tests by a Monte Carlo experiment and compare these to the most widely used linear test. Our tests appear to be well-sized and have reasonably good power properties.

Changing-regime volatility: A fractionally integrated SETAR model

This article presents a 2-regime SETAR model with different long-memory processes in both regimes. We briefly present the memory properties of this model and propose an estimation method. Such a process is applied to the absolute and squared returns of five stock indices. A comparison to simple ARFIMA models is made using some forecastibility criteria. Our empirical results suggest that our model offers an interesting alternative competing framework to describe the persistent dynamics in modelling the returns.

Long-run relationships between international stock prices: further evidence from fractional cointegration tests

The recent empirical literature supports the view that most of the international stock prices are not pairwise cointegrated. However, by using fractional cointegration techniques, this article shows that France, Germany, Hong Kong and Japans stock prices indices are pairwise fractionally cointegrated with US stock prices. Equilibrium errors are mean reverting with half-life lying between 2 and 12 days. It is worthwhile noting that emerging markets like Brazil and Argentina are not pairwise cointegrated with the US stock market. These new results have important implications for asset pricing and international portfolio strategy.

Bayesian estimation and forecasting in non-linear models application to an LSTAR model

This paper considers the Bayesian estimation and prediction in a non-linear model by means of Monte Carlo integration with importance sampling. The importance function is derived from a first-order Taylor series expansion of the non-linear conditional expectation of the endogenous variable. The method is applied to an LSTAR model with an artificial sample.

Econometric Modeling and Inference: Nonparametric Estimation of the Regression

Introduction In contrast to other fields, the economic theory rarely specifies functional forms but usually specifies only a list of relevant variables to explain a phenomenon. The specification of the form of the relationship largely results from the empirical study which yields a “good” model which “works well.” A first level of the analysis consists in writing a (linear, loglinear, nonlinear, …) model and in estimating it without taking the approximating nature of the model into account. This approach was explained in Chapters 7, 8, and 9. A second approach consists in specifying a parametric model whose incorrect specification is explicit. This leads for instance to correct the expression for the variances or to choose robust methods to the misspecification. This methodology was the topic of Chapter 9. Finally, it is possible to free oneself of all specification constraints by adopting a nonparametric approach to the estimation of the regression, an approach in which the data itself chooses the form of the function of interest. Various estimation methods of the nonparametric regression have been developed and are now commonly employed. We will present here the kernel method (corresponding to what we presented in Chapter 5). This method is simple but is dominated in some cases by other approaches (local polynomial, Fourier series expansion, wavelets …).