This paper is concerned with various issues related to inference in large dynamic panel data models (where both n and T increase without bound) in the presence of, possibly, strong cross-sectional dependence. Our first aim is to provide a Central Limit Theorem for estimators of the slope...

This paper examines a nonparametric CUSUM-type test for common trends in large panel data sets with individual fixed effects. We consider, as in Zhang, Su and Phillips (2012), a partial linear regression model with unknown functional form for the trend component, although our test does not...

A general limit theorem is established for time series regression estimates which include generalized least squares, in the presence of long range dependence in both errors and stochastic regressors. The setting and results differ significantly from earlier work on regression with long range...

The paper examines a Lagrange Multiplier type test for the constancy of the parameter in general models with dependent data without imposing any artificial choice of the possible location of the break. In order to prove the asymptotic behaviour of the test, we extend a strong approximation...

Nowadays it is very frequent that a practitioner faces the problem of modelling large data sets. Relevant examples include spatio-temporal or panel data models with large N and T. In these cases deciding a particular dynamic model for each individual/population, which plays a crucial role in...

The paper examines a Lagrange Multiplier type test for the constancy of the parameter in general models with dependent data without imposing any arti…cial choice of the possible location of the break. In order to prove the asymptotic behaviour of the test, we extend a strong approximation...

We consider an omnibus test for the correct speci…cation of the dynamics of a sequence fx (t)gt2Zd in a lattice. As it happens with causal models and d = 1, its asymptotic distribution is not pivotal and depends on the estimator of the unknown parameters of the model under the null hypothesis....

The paper examines a Lagrange Multiplier type test for the constancy of the parameter in general models with dependent data without imposing any arti…cial choice of the possible location of the break. In order to prove the asymptotic behaviour of the test, we extend a strong approximation...

The paper examines a Lagrange Multiplier type test for the constancy of the parameter in general models with dependent data without imposing any artificial choice of the possible location of the break. In order to prove the asymptotic behaviour of the test, we extend a strong approximation...

Smooth nonparametric kernel density and regression estimators are studied when the data is strongly dependent. In particular, we derive Central (and Noncentral) Limit Theorems for the kernel density estimator of a multivariate Gaussian process and infinite-order moving average of an independent...