A Generalized Method of Moments Estimator for a Spatial Panel Model with an Endogenous Spatial Lag and Spatial Moving Average Errors

2006 ◽  
Author(s):  
Bernard Fingleton
Keyword(s):  
Method Of Moments ◽  
Moving Average ◽  
Generalized Method Of Moments ◽  
Spatial Lag ◽  
Spatial Panel ◽  
Panel Model ◽  
Spatial Panel Model ◽  
Method Of Moments Estimator ◽  
Spatial Moving Average ◽  
Moving Average Errors

Using information from singletons in fixed-effects estimation: xtfesing

2020 ◽  
Vol 20 (4) ◽  
pp. 965-975
Author(s):  
Laura Magazzini ◽  
Randolph Luca Bruno ◽  
Marco Stampini
Keyword(s):  
Panel Data ◽  
Method Of Moments ◽  
Fixed Effects ◽  
Generalized Method Of Moments ◽  
Panel Data Regression ◽  
Data Regression ◽  
Method Of Moments Estimator ◽  
Fixed Effects Estimation

In this article, we describe the xtfesing command. The command implements a generalized method of moments estimator that allows exploiting singleton information in fixed-effects panel-data regression as in Bruno, Magazzini, and Stampini (2020, Economics Letters 186: Article 108519).

scholarly journals The Effect of Manufacturing Agglomeration on Haze Pollution in China

2018 ◽  
Vol 15 (11) ◽  
pp. 2490 ◽  
Author(s):  
Jun Liu ◽  
Yuhui Zhao ◽  
Zhonghua Cheng ◽  
Huiming Zhang
Keyword(s):  
Industrial Structure ◽  
Spillover Effects ◽  
Eastern Region ◽  
Spatial Panel ◽  
Panel Model ◽  
Specific Analysis ◽  
Haze Pollution ◽  
Regional Haze ◽  
Spatial Panel Model ◽  
Development Levels

Based on panel data on 285 Chinese cities from 2003 to 2012, we use a dynamic spatial panel model to empirically analyze the effect of manufacturing agglomeration on haze pollution. The results show that when economic development levels, population, technological levels, industrial structure, transportation, foreign direct investment, and greening levels are stable, manufacturing agglomeration significantly aggravates haze pollution. However, region-specific analysis reveals that the effects of manufacturing agglomeration on inter-regional haze pollution depends on the region: the effect of manufacturing agglomeration on haze pollution is the largest in the Western region, followed by the Central region, and is the least in the Eastern region. Based on the above conclusions, we put forward several specific suggestions, such as giving full play to the technology and knowledge spillover effects of manufacturing agglomeration, guiding manufacturing agglomerations in a scientific and rational way, accelerating the transformation and upgrading of manufacturing industries in agglomeration regions.

scholarly journals A Crook is a Crook . . . But is He Still a Crook Abroad? On the Effect of Immigration on Destination-Country Corruption

2015 ◽  
Vol 16 (4) ◽  
pp. 464-489 ◽  
Author(s):  
Eugen Dimant ◽  
Margarete Redlin ◽  
Tim Krieger
Keyword(s):  
Method Of Moments ◽  
Fixed Effects ◽  
Generalized Method Of Moments ◽  
Destination Country ◽  
Estimation Methods ◽  
Econometric Methodology ◽  
Migration Flows ◽  
The Difference ◽  
Method Of Moments Estimator ◽  
The Impact

AbstractThis paper analyzes the impact of migration on destination-country corruption levels. Capitalizing on a comprehensive dataset consisting of annual immigration stocks of OECD countries from 207 countries of origin for the period 1984-2008, we explore different channels through which corruption might migrate. We employ different estimation methods using fixed effects and Tobit regressions in order to validate our findings. Moreover, we also address the issue of endogeneity by using the Difference- Generalized Method of Moments estimator. Independent of the econometric methodology, we consistently find that while general migration has an insignificant effect on the destination country’s corruption level, immigration from corruption-ridden origin countries boosts corruption in the destination country. Our findings provide a more profound understanding of the socioeconomic implications associated with migration flows.

scholarly journals The Determinants of Foreign Direct Investment in China: A Spatial Panel Analysis

2016 ◽  
Author(s):  
Chen-Chen Yong ◽  
Siew-Yong Yew ◽  
Xin Huang ◽  
Mui-Yin Chin
Keyword(s):  
Foreign Direct Investment ◽  
Direct Investment ◽  
Eastern Region ◽  
Panel Analysis ◽  
Fdi Inflows ◽  
Spatial Panel ◽  
Panel Model ◽  
Spatial Panel Model ◽  
Investment In China ◽  
Determinants Of Fdi

China is currently the major foreign direct investment (FDI) destination arising from her open door policies since 1978. FDI has become a large impetus to China’s economic growth. However, the geographical distribution of FDI in China is severely biased with 83% concentrated in the eastern region. This is a result of not only differences in locational advantages but also the result of the initiating policies and temporal differences of FDI inflows among the regions. This study aims to examine the determinants of FDI and examine empirically the possible coherent policies for the three regions of China (Eastern, Central and Western) using the spatial panel analysis for the data within the period of 1994 to 2008. The empirical results show that the determinants of FDI vary among the three regions, depending on the motives of the investor and the results of policy bias. The entrepreneurial nature of competition of FDI among the provinces revealed by the spatial FDI factor is a conclusion that cannot be ignored. A more coherent policy on FDI inflows into China is an urgent necessity, though the policies for each region must be, of necessity, different for each of the three regions.   Keywords: Foreign Direct Investment, China, Spatial panel model, spatial variables JEL: F14, C33

scholarly journals Returns to Scale for EU Regions – evidence from the spatial panel model

2017 ◽  
Vol 3 (329) ◽  
Author(s):  
Alicja Anna Olejnik
Keyword(s):  
Fixed Effects ◽  
Regional Growth ◽  
Returns To Scale ◽  
Increasing Returns ◽  
Increasing Returns To Scale ◽  
Spatial Panel ◽  
Panel Model ◽  
Eu Regions ◽  
Spatial Panel Model ◽  
The Eu

Recent findings emphasise the importance of localised returns to scale for the regional growth as well as for the agglomeration processes. However, it is still not well established whether returns to scale are constant or increasing, and to what extent. Therefore, in this study we apply specification which describes the productivity growth with the growth of output through the Verdoorn’s law. This study aims to provide some new estimates of the degree of returns to scale for EU regions. Our findings show that the hypothesis of increasing returns to scale is still valid in today’s EU economy. To test the hypothesis, we have employed the Multidimensional Spatial Panel Durbin Model with Spatial Fixed Effects. The research is conducted for 261 regions of the EU 28. The paper concludes that increasing returns to scale in EU regions are substantial.

Sign in / Sign up

Export Citation Format

Share Document