GM Estimation of Higher Order Spatial Autoregressive Processes in Panel Data Error Component Models

This paper presents a generalized moments (GM) approach to estimating an R-th order spatial regressive process in a panel data error component model. We derive moment conditions to estimate the parameters of the higher order spatial regressive process and the optimal weighting matrix required to achieve asymptotic efficiency. We prove consistency of the proposed GM estimator and provide Monte Carlo evidence that it performs well also in reasonably small samples.spatial models, panel data models, error component models

Intra- and Inter-Industry Productivity Spillovers in OECD Manufacturing: A Spatial Econometric Perspective

We adopt a spatial econometric approach to estimate intra- and inter-industry productivity spillovers in total factor productivity transmitted through input-output relations in a sample of 13 OECD countries and 15 manufacturing industries. Both R&D spillovers as well as remainder, input-output-related linkage effects are accounted for, the latter of which we model by a spatial regressive error process. We find that knowledge spillovers occur both horizontally and vertically, whereas remainder spillovers are primarily of intra-industry type. Notably, these intra-industry remainder spillovers turn out economically more significant than R&D spillovers.intra-industry spillovers, inter-industry spillovers, productivity, spatial econometrics, research and development

Estimation of Higher-Order Spatial Autoregressive Panel Data Error Component Models

This paper develops an estimator for higher-order spatial autoregressive panel data error component models with spatial autoregressive disturbances, SARAR(R,S). We derive the moment conditions and optimal weighting matrix without distributional assumptions for a generalized moments (GM) estimation procedure of the spatial autoregressive parameters of the disturbance process and define a generalized two-stages least squares estimator for the regression parameters of the model. We prove consistency of the proposed estimators, derive their joint asymptotic distribution, and provide Monte Carlo evidence on their small sample performance.higher-order spatial dependence, generalized moments estimation, two-stages least squares, asymptotic statistics

Horizontal versus Vertical Interdependence in Multinational Activity

Recent research in international economics highlights the role of interdependencies of investment decisions and sales of multinational firms. Previous work focused on and provided evidence for aggregate flows or stocks of foreign direct investment, showing that interdependence declines in geographical distance among host countries. This could be interpreted as implicit evidence for export-platform foreign direct investment—an activity which creates a complementary relationship between (potential) host markets through final goods exports of foreign subsidiaries to third countries. This paper sheds light on interdependencies that are brought about by (horizontal) trade in final goods and (vertical) trade in intermediate goods (within and between host countries). For this, we use a panel data set of U.S. foreign affiliate sales to 16 developed countries in 7 industries over the period 1983-2000. As one of the first studies on that matter, we explicitly distinguish between horizontal and vertical interdependence in MNE activity and allow for both market size (demand) related as well as remainder linkage effects. The latter are captured by a second order spatial regressive error process. Overall, there is evidence for mainly vertical as opposed to horizontal interdependence and, hence, mainly vertical motives of multinational activity.multinational firms, foreign affiliate sales, spatial econometrics, generalized method of moments estimation, panel data analysis

Intra- and inter-industry productivity spillovers in OECD manufacturing: a spatial econometric perspective

We adopt a spatial econometric approach to estimate intra- and inter-industry productivity spillovers in total factor productivity transmitted through input-output relations in a sample of 13 OECD countries and 15 manufacturing industries. Both R&D spillovers as well as remainder, input-output-related linkage effects are accounted for, the latter of which we model by a spatial regressive error process. We find that knowledge spillovers occur both horizontally and vertically, whereas remainder spillovers are primarily of intra-industry type. Notably, these intra-industry remainder spillovers turn out economically more significant than R&D spillovers

Horizontal versus vertical interdependence in multinational activity

Recent research in international economics highlights the role of interdependencies of investment decisions and sales of multinational firms. Previous work focused on and provided evidence for aggregate flows or stocks of foreign direct investment, showing that interdependence declines in geographical distance among host countries. This could be interpreted as implicit evidence for export-platform foreign direct investmentan activity which creates a complementary relationship between (potential) host markets through final goods exports of foreign subsidiaries to third countries. This paper sheds light on interdependencies that are brought about by (horizontal) trade in final goods and (vertical) trade in intermediate goods (within and between host countries). For this, we use a panel data set of U.S. foreign affiliate sales to 16 developed countries in 7 industries over the period 1983-2000. As one of the first studies on that matter, we explicitly distinguish between horizontal and vertical interdependence in MNE activity and allow for both market size (demand) related as well as remainder linkage effects. The latter are captured by a second order spatial regressive error process. Overall, there is evidence for mainly vertical as opposed to horizontal interdependence and, hence, mainly vertical motives of multinational activity

Spacey Parents and Spacey Hosts in Foreign Direct Investment

Shocks on FDI of some parent country in a host affect the same parent's FDI in other hosts. Shocks on a parent's FDI in some host affect other parents' FDI in the same host. In general equilibrium, shocks on FDI between any country pair will affect all country pairs' FDI. Using cross-sectional data on FDI among 22 OECD countries in 2000, we use a spatial estimation framework to allow for all three modes of interdependence simultaneously, thereby distinguishing between market-size-related and remainder interdependence. Our results highlight the complexity of multinational enterprises' investment strategies and the interconnectedness of the world investment system

GM estimation of higher order spatial autoregressive processes in panel data error component models

This paper presents a generalized moments (GM) approach to estimating an R-th order spatial regressive process in a panel data error component model. We derive moment conditions to estimate the parameters of the higher order spatial regressive process and the optimal weighting matrix required to achieve asymptotic efficiency. We prove consistency of the proposed GM estimator and provide Monte Carlo evidence that it performs well also in reasonably small samples

Estimation and Testing of Higher-Order Spatial Autoregressive Panel Data Error Component Models

This paper develops an estimator for higher-order spatial autoregressive panel data error component models with spatial autoregressive disturbances, SARAR(R,S). We derive the moment conditions and optimal weighting matrix without distributional assumptions for a generalized moments (GM) estimation procedure of the spatial autoregressive parameters of the disturbance process and define a generalized two-stage least squares estimator for the regression parameters of the model. We prove consistency of the proposed estimators, derive their joint asymptotic distribution, and provide Monte Carlo evidence on their small sample performance

GM estimation of higher-order spatial autoregressive processes in cross-section models with heteroskedastic disturbances

This paper generalizes the approach to estimating a first-order spatial autoregressive model with spatial autoregressive disturbances (SARAR(1,1)) in a cross-section with heteroskedastic innovations by Kelejian and Prucha (2008) to the case of spatial autoregressive models with spatial autoregressive disturbances of arbitrary (finite) order (SARAR(R,S)). We derive the moment conditions and the optimal weighting matrix for a generalized moments (GM) estimation procedure of the spatial regressive parameters of the disturbance process and define a generalized two-stages least squares estimator for the regression parameters of the model. We prove consistency of the proposed estimators, derive their (joint) asymptotic distribution, and provide Monte Carlo evidence on their small sample performance