This paper considers the estimation of factor memories in the context of a high-dimensional factor model. Both factors and idiosyncratic error terms are potentially non-stationary fractional integrated processes. We propose a three-step procedure to estimate the latent factors. We then apply the...

The fixed effects (FE) panel model is one of the main econometric tools in empirical economic research. A major practical limitation is that the parameters on time-constant covariates are not identifiable. This paper presents a new approach to grouping FE in the linear panel model to reduce...

This paper is concerned with variable selection in linear high-dimensional frameworks when the covariates under consideration are highly correlated. Existing methods in the literature generally require that the degree of correlation among covariates be weak, yet, often in applied research,...

We study inference for threshold regression in the context of a large panel factor model with common stochastic trends. We develop a Least Squares estimator for the threshold level, deriving almost sure rates of convergence and proposing a novel, testing based, way of constructing confidence...

The Internet Appendix collects the proofs and additional results that support the main text. We show in simulations that our estimators perform well relative to alternative estimators and can be improved even further with an iterative approach. We also confirm that the distribution results,...

Generalized quantile regressions, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We develop a functional data analysis approach to...

This paper considers an alternative way of structuring stochastic variables in a dynamic programming framework where the model structure dictates that numerical methods of solution are necessary. Rather than estimating integrals within a Bellman equation using quadrature nodes, we use nodes...

This paper presents how the most recent improvements made on covariance matrix estimation and model order selection can be applied to the portfolio optimisation problem. The particular case of the Maximum Variety Portfolio is treated but the same improvements apply also in the other optimisation...

This paper develops an adaptive group lasso estimator for factor models with both global and group-specific factors. The global factors can affect all variables, whereas the group-specific factors are only allowed to affect the variables within a certain group. We propose a new method to...

In this paper, we study identification and estimation of a factor model in classic factor analysis. It is well-known that the factor loading matrix of a classic factor model can only be determined up to a multiplication of an orthogonal matrix on the right if one only considers the first and...