Showing papers on "Multidimensional panel data published in 2015"
Book•
17 Nov 2015
TL;DR: In this paper, the authors present an introduction to economic economics and introduce the concept of time series models, and present a set of models for time series modeling in the context of data aggregation.
Abstract: PART I: INTRODUCTION TO ECONOMETRICS PART II: STATISTICAL THEORY PART III: STOCHASTIC PROCESSES PART IV: UNIVARIATE TIME SERIES MODELS PART V: MULTIVARIATE TIME SERIES MODELS PART VI: PANEL DATA ECONOMETRICS PART VII: APPENDICES
676 citations
TL;DR: In this paper, the authors extend the matrix exponential spatial specification to panel data models, and present a maximum likelihood approach to the estimation of this spatial model specification and compare the results with the fixed effects spatial autoregressive panel model.
Abstract: This paper extends the matrix exponential spatial specification to panel data models. The matrix exponential spatial panel specification produces estimates and inferences comparable to those from conventional spatial panel models, but has computational advantages. We present maximum likelihood approach to the estimation of this spatial model specification and compare the results with the fixed effects spatial autoregressive panel model.
8 citations
TL;DR: In this paper, the integration of variables with a time-series dimension and a cross-sectional dimension as well as panel cointegration relationships in the Japanese demand system was investigated, and it was shown that the variables follow a nonstationary I(1) process, and their linear combinations form panel co-integration relationship.
Abstract: This study investigates the integration of variables with a time-series dimension and a cross-sectional dimension as well as panel cointegration relationships in the Japanese demand system. We find that the variables follow a nonstationary I(1) process, and their linear combinations form panel cointegration relationships. In addition, we show that the consistency between the demand theory and data holds in the long run. These results indicate that our demand system economically constructs stable relationships not only with timeseries data but also with panel data.
1 citations
Posted Content•
TL;DR: In this paper, 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.
1 Jan 2015
TL;DR: In this article, the authors analyze the properties of the estimation techniques for panel data models with additive and multiplicative error structures and discuss the relative merits of the maximum likelihood estimators in dynamic panel data model.
Abstract: Panel data are repeated observations on the same cross section unit, typically of individuals or firms (in microeconomic applications), observed for several time periods. The use of panel data has been increasingly popular in empirical macroeconomic and (especially) microeconomic studies and there are several reasons behind the success story. This thesis analyses the properties of the estimation techniques for panel data models with additive and multiplicative error structures. First, this thesis discusses the relative merits of the maximum likelihood estimators in dynamic panel data models. Second, it provides an in-depth analysis of genuine and pseudo panel data models with unobserved interactive effects.