Sieve Bootstrap for Strongly Dependent Stationary Processes

This paper studies the properties of the sieve bootstrap for a class of linear processes which exhibit strong dependence. The sieve bootstrap scheme is based on residual resampling from autoregressive approximations the order of which increases slowly with the sample size. The first-order asymptotic validity of the sieve bootstrap is established in the case of the sample mean and sample autocovariances. The finite-sample properties of the method are also investigated by means of Monte Carlo experiments.Autoregressive approximation, Linear process, Strong dependence, Sieve bootstrap, Stationary process

Markov-Switching Models with state-dependent time-varying transition probabilities

This paper proposes a model which allows for discrete stochastic breaks in the time-varying transition probabilities of Markov-switching models with autoregressive dynamics. An extensive simulation study is undertaken to examine the properties of the maximum-likelihood estimator and related statistics, and to investigate the implications of misspecification due to unaccounted changes in the parameters of the Markov transition mechanism. An empirical application that examines the relationship between Argentinian sovereign bond spreads and output growth is also discussed

Semiparametric Sieve-Type GLS Inference in Regressions with Long-Range Dependence

This paper considers the problem of statistical inference in linear regression models whose stochastic regressors and errors may exhibit long-range dependence. A time-domain sieve-type generalized least squares (GLS) procedure is proposed based on an autoregressive approximation to the generating mechanism of the errors. The asymptotic properties of the sieve-type GLS estimator are established. A Monte Carlo study examines the finite-sample properties of the method for testing regression hypotheses.Autoregressive approximation, Generalized least squares, Linear regression, Long-range dependence, Spectral density

Cross-Sectional Aggregation and Persistence in Conditional Variance

This paper explores the interactions between cross-sectional aggregation and persistence of volatility shocks. We derive the ARMA-GARCH representation that linear aggregates of ARMA processes with GARCH errors admit, and establish conditions under which persistence in volatility of the aggregate series is higher than persistence in the volatility of the individual series. The practical implications of the results are illustrated empirically in the context of an option pricing exercise.ARMA process; Cross-sectional aggregation; GARCH process; Volatility persistence.

A distance test of normality for a wide class of stationary processes

A distance test for normality of the one-dimensional marginal distribution of stationary fractionally integrated processes is considered. The test is implemented by using an autoregressive sieve bootstrap approximation to the null sampling distribution of the test statistic. The bootstrap-based test does not require knowledge of either the dependence parameter of the data or of the appropriate norming factor for the test statistic. The small-sample properties of the test are examined by means of Monte Carlo experiments. An application to real-world data is also presented

Contemporaneous-threshold smooth transition GARCH models

This paper proposes a contemporaneous-threshold smooth transition GARCH (or C-STGARCH)model for dynamic conditional heteroskedasticity. The C-STGARCH model is a generalization tosecond conditional moments of the contemporaneous smooth transition threshold autoregressive model of Dueker et al. (2007) in which the regime weights depend on the ex ante probability that a contemporaneous latent regime-specific variable exceeds a threshold value. A key feature of the C-STGARCH model is that its transition function depends on all the parameters of the model as well as on the data. The structural properties of the model are investigated, in addition to the finite-sample properties of the maximum likelihood estimator of its parameters. An application to U.S. stock returns illustrates the practical usefulness of the C-STGARCH model

A simple method for testing cointegration subject to regime changes

In this paper, we propose a simple method for testing cointegration in models that allow for multiple shifts in the long run relationship. The procedure consists of computing conventional residual-based tests with standardized residuals from Markov switching estimation. No new critical values are needed. An empirical application to the present value model of stock prices is presented, complemented by a small Monte Carlo experiment.Cointegration; Markov Switching; Standardized residuals.

Using Triples to assess symmetry under weak dependence

The problem of assessing symmetry about an unspecified center of the one-dimensional marginal distribution of strictly stationary random processes is considered. A well-known U-statistic based on data triples is used to detect deviations from symmetry, allowing the underying process to satisfy suitable mixing or near-epoch dependence conditions. We suggest using subsampling for inference on the target parameter, establish the asymptotic validity of the method in our setting, and discuss data-driven rules for selecting the size of subsamples. The small-sample properties of the proposed inference procedures are examined by means of Monte Carlo simulations and an application to time series of real output growth is also presented

A distance test of normality for a wide class of stationary processes

This paper considers a distance test for normality of the one-dimensional marginal distribution of stationary fractionally integrated processes. The test is implemented by using an autoregressive sieve bootstrap approximation to the null sampling distribution of the test statistic. The bootstrap-based test does not require knowledge of either the dependence parameter of the data or of the appropriate norming factor for the test statistic. The small-sample properties of the test are examined by means of Monte Carlo experiments. An application to real-world data is also presented

Residual-based tests for cointegration and multiple regime shifts

In this paper we examine the properties of several cointegration tests when long run parameters are subject to multiple shifts, resorting to Monte Carlo methods. We assume that the changes in cointegration regimes are governed by a unobserved Markov chain process. This specification has the considerable advantage of allowing for an unspecified number of stochastic breaks, unlike previous works that consider a single, deterministic break. Our Monte Carlo analysis reveals that testing cointegration with the usual procedures is a quite unreliable task, since the performance of the tests is poor for a number of plausible regime shifts parameterizations.Cointegration; Tests; Structural change; Markov Switching; Monte Carlo