This paper considers estimation and inference for varying-coefficient models with nonstationary regressors. We propose a nonparametric estimation method using penalized splines, which achieves the same optimal convergence rate as kernel-based methods, but enjoys computation advantages. Utilizing...

Estimation theory in a nonstationary environment has been very popular in recent years. Existing studies focus on nonstationarity in parametric linear, parametric nonlinear and nonparametric nonlinear models. In this paper, we consider a partially linear model of nonstationary time series, in...

The predictability of long-term asset returns increases with the time horizon as estimated in regressions of aggregated-forward returns on aggregated-backward predictive variables. This previously established evidence is consistent with the presence of common slow-moving components that are...

Estimation of a nonparametric regression spectrum based on the periodogram is considered. Neither trend estimation nor smoothing of the periodogram are required. Alternatively, for cases where spectral estimation of phase shifts fails and the shift does not depend on frequency, a time domain...

This paper reviews recent advances in estimation and inference for nonparametric and semiparametric models with endogeneity. It first describes methods of sieves and penalization for estimating unknown functions identified via conditional moment restrictions. Examples include nonparametric...

This paper examines the sources of economic growth in Algeria, studying the key drivers of their slow and weak economic performance, during the period of 1979-2019 from the perspective of the augmented growth accounting framework and the growth regression method. More specifically, the paper...

Since an economic or financial variable may be affected by both stationary andnonstationary variables, this paper proposes a class of augmented cointegrating linear(ACL) models that accommodate these time series of different types. Moreover, thevariables are allowed to be strongly correlated in...

In this paper, we derive an optimal test for determining break positions in Gaussian linear regressions. The procedure is an admissible rule in a multiple decision theory setting and the results are exact and valid in small samples. The analysis indicates that regression design can have a very...