Showing papers on "Multidimensional panel data published in 2014"
TL;DR: In this article, a new approach to the forecasting of macroeconomic variables based on a dynamic factor state space analysis is proposed. But the approach is not suitable for forecasting of a large number of variables.
72 citations
TL;DR: In this article, an exact maximum likelihood method is developed for the estimation of parameters in a nonlinear non-Gaussian dynamic panel data model with unobserved random individual-specific and time-varying effects.
21 citations
TL;DR: In this article, a new class of residual-based tests is proposed for checking the validity of dynamic panel data models with both large cross-sectional units and time series dimensions, which can detect a wide range of model misspecifications in the conditional mean of a dynamic data model, including functional form and lag misspecification.
16 citations
Posted Content•
TL;DR: In this article, the appropriate within estimators for the most frequently used three-dimensional fixed effects panel data models are introduced, and the behavior of these estimators in the cases of no self-flow data, unbalanced data, and dynamic autoregressive models are analyzed.
Abstract: The paper introduces the appropriate within estimators for the most frequently used three-dimensional fixed effects panel data models. It analyzes the behavior of these estimators in the cases of no self-flow data, unbalanced data, and dynamic autoregressive models. The main results are then generalized for higher dimensional panel data sets as well.
9 citations
Posted Content•
1 Jan 2014
TL;DR: In this paper, a literature survey about the panel smooth transition regression models has been introduced, where the studies in chronological order have been explained with respect to problems which they have solved, and the categories of different models have been analyzed under the panel categorization.
Abstract: In this study we are introducing a literature survey about the panel smooth transition regression models. This type of modeling has been emerged from two different strand of literature where the first one is nonlinear time series the other is the panel data analysis. Both of these fields have tackled with different type of biases in estimation process. Therefore, combining these two fields constitutes different problems in estimation. Hence, instead of giving the studies in chronological order, we preferred to explain papers with respect to problems which they have solved. In this order, first we analyze the categories of different models. For example, there are several categorizations in the panel data estimation with respect to time and cross-section dimension. Therefore every category has its own biases depending on the time and cross-section dimension. On the other hand the dynamic structure of the panel data is another important determinant in which we can classify the biases. Hence, the static and dynamic panel smooth transition models are also discussed separately in this study. Finally, smooth transition models has its’ own categories, hence we are giving these categories under the panel categorization as well.
8 citations