scholarly journals Power of Moran’s I Test for Spatial Dependence in Panel Data Models with Time Varying Spatial Weights Matrices

2015 ◽  
Vol 3 (5) ◽  
pp. 463-471 ◽  
Author(s):  
Bianling Ou ◽  
Xin Zhao ◽  
Mingxi Wang
Keyword(s):  
Panel Data ◽  
Spatial Dependence ◽  
Moran’S I ◽  
Panel Data Models ◽  
Time Varying ◽  
Moran's I ◽  
Spatial Weights ◽  
Spatial Weights Matrix ◽  
Time Invariant ◽  
Over Time

AbstractThe spatial weights matrix is usually specified to be time invariant. However, when it are constructed with economic/socioeconomic distance, trade /demographic/climatic characteristics, these characteristics might be changing over time, and then the spatial weights matrix substantially varies over time. This paper focuses on power of Moran’s I test for spatial dependence in panel data models with where spatial weights matrices can be time varying (TV-Moran). Compared with Moran’s I test with time invariant spatial weights matrices (TI-Moran), the empirical power of TV-Moran test for spatial dependence are evaluated. Our extensive Monte Carlo simulation results indicate that Moran’s I test with misspecified time invariant spatial weights matrices is questionable; Instead, TV-Moran test has shown superiority in higher power, especially for cases with negative spatial correlation parameters and the large time dimension.

scholarly journals Spatial distribution of coffee crop productivity in Minas Gerais over time

2020 ◽  
Vol 12 ◽  
pp. 1-10
Author(s):  
Renan Serenini ◽  
Patrícia de Siqueira Ramos ◽  
Lincoln Frias
Keyword(s):  
Spatial Distribution ◽  
Spatial Dependence ◽  
Crop Productivity ◽  
Minas Gerais ◽  
The State ◽  
Moran’S I ◽  
Coffee Production ◽  
Moran's I ◽  
High Productivity ◽  
Over Time

Brazil is the world's largest coffee producer and the state of Minas Gerais is responsible for half of the Brazilian production. However, productivity is unevenly distributed throughout the state. Therefore, the purpose of this study is to analyze the spatial distribution of coffee productivity in Minas Gerais from 2002 to 2017, a valuable information to identify regions where coffee production may be more promising in the future. This paper investigates the existence of spatial dependence of productivity between regions (using Moran's I), its dynamics throughout the period and the presence of clusters of high and low productivity (using local Moran's I). The results show that the spatial dependence of productivity was stronger from 2002 to 2009 than between 2010 and 2017. Some regions with small coffee areas but high productivity have stopped producing the crop whereas some of those with large areas but low productivity increased their productivity levels. Therefore, there is a tendency of homogenization of productivity in Minas Gerais, with values close to 30 bags per hectare.

A study on spatial autocorrelation: Case of Russian regional inflation

2021 ◽  
Vol 64 (4) ◽  
pp. 5-22
Author(s):  
Andrew Kirillov ◽  
Keyword(s):  
Time Series ◽  
Data Structure ◽  
Panel Data ◽  
Spatial Autocorrelation ◽  
Spatial Dependence ◽  
Quantitative Measure ◽  
Moran’S I ◽  
Food Services ◽  
Moran's I

We apply APLE statistic to explore spatial autocorrelation of Russian regional inflationary processes. APLE is discussed to be the fine alternative to Moran’s I. To conduct this study we modify statistics of spatial dependence for panel data structure. We use time series of Russian regional CPIs (i.e. quantitative measure of inflation) of food, non-food, services baskets. We find evidence to confirm the hypothesis of the existence of spatial autocorrelation of regional inflationary processes on the horizon of our study.

scholarly journals Estimating latent group structure in time-varying coefficient panel data models

2019 ◽  
Author(s):  
Jia Chen
Keyword(s):  
Panel Data ◽  
Data Models ◽  
Panel Data Models ◽  
Time Varying ◽  
Clustering Method ◽  
Varying Coefficient ◽  
Varying Coefficients ◽  
Time Varying Coefficients ◽  
Latent Group ◽  
Group Structures

Summary This paper studies the estimation of latent group structures in heterogeneous time-varying coefficient panel data models. While allowing the coefficient functions to vary over cross-sections provides a good way to model cross-sectional heterogeneity, it reduces the degree of freedom and leads to poor estimation accuracy when the time-series length is short. On the other hand, in a lot of empirical studies, it is not uncommon to find that heterogeneous coefficients exhibit group structures where coefficients belonging to the same group are similar or identical. This paper aims to provide an easy and straightforward approach for estimating the underlying latent groups. This approach is based on the hierarchical agglomerative clustering (HAC) of kernel estimates of the heterogeneous time-varying coefficients when the number of groups is known. We establish the consistency of this clustering method and also propose a generalised information criterion for estimating the number of groups when it is unknown. Simulation studies are carried out to examine the finite-sample properties of the proposed clustering method as well as the post-clustering estimation of the group-specific time-varying coefficients. The simulation results show that our methods give comparable performance to the penalised-sieve-estimation-based classifier-LASSO approach by Su et al. (2018), but are computationally easier. An application to a panel study of economic growth is also provided.

GMM estimation of linear panel data models with time-varying individual effects

2001 ◽  
Vol 101 (2) ◽  
pp. 219-255 ◽  
Author(s):  
Seung Chan Ahn ◽  
Young Hoon Lee ◽  
Peter Schmidt
Keyword(s):  
Panel Data ◽  
Data Models ◽  
Panel Data Models ◽  
Time Varying ◽  
Gmm Estimation ◽  
Individual Effects
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