Additional Endogenous Variables Research Articles

Spillover effects in spatial models: Generalizations and extensions

AbstractIn this paper, we consider two different cases of spillover effects: the first is a case where the model does not contain any additional endogenous regressors. Related to this, we give a variance formula that can be used to make inference for estimated spillover effects. In the second case, we extend the discussion of spillover effects to a model containing additional endogenous variables. We suggest approximations to the spillover effects that according to the results of our Monte Carlo experiment are quite accurate. However, our variance formula can not be determined unless the equations of the entire system are known. Suggestions for approximations in this case are left for future research.

Knowledge Technology Transfer: A Spatial Analysis within the Triad

The aim of this study is that of further exploring the knowledge spillover effects of Large International firms. In particular, we implement a spatial analysis in United States, Japan and Europe. We use technological vectors of firms to compute Jaffe proximity measure in such a way that we get knowledge externalities relative to different countries. To our end, we estimate the spatial-autoregressive model where we consider also additional endogenous variables. The findings demonstrate the significant positive effect predicted by the core literature about this topic.

Important overlooked IVs in spatial models

Spatial models often contain additional endogenous variables as regressors. The complete system determining these variables is typically not known to the researcher, and so maximum likelihood or Bayesian estimation methods are precluded. This leaves instrumental variable estimation. In all likelihood, the system may contain certain forms of nonlinearities. These nonlinearities might arise because of endogenous weighting matrices, functional form differences in the endogenous variables, etc. The existence of such nonlinearities strongly suggests the use of nonlinear forms of the instruments. Issues of this sort were pointed out in Kelejian and Piras (Spatial econometrics, Elsevier, Amsterdam, 2017) and Kelejian (Lett Spat Resour Sci 9(1):113–136, 2016). However, thus far Monte Carlo results relating to efficiencies gained by the use of nonlinear instrumental variables are not available. This is unfortunate because these efficiencies can be quite extensive. The purpose of this paper is to fill this void.

An assessment of energy production efficiency activity: a spatial analysis

The aim of this paper is to investigate the extent to which the environmental technological spillover effects on firms’ productivity are affected by the spatial dimension. To this end, we introduce a spatial Durbin model with additional endogenous variables for the energy production efficiency activity of large R&D-intensive firms located in three economic areas: the USA, Japan and Europe. To identify the technological proximity between the firms, we construct an original Mahalanobis environmental industry weight matrix, based on the construction of technological vectors for each firm, with European environmental patents distributed across more technology classes. The findings show a statistically negative impact of spatially distributed environmental spillovers on firms’ productivity in all the economic areas.

Modelling road traffic crashes using spatial autoregressive model with additional endogenous variable

Road traffic crashes have become a global issue of concern because of the number of deaths and injuries. The model of interest is a linear cross sectional Spatial Autoregressive (SAR) model with additional endogenous variables, exogenous variables and SAR disturbances. The focus is on RTC in Oyo state, Nigeria. The number of RTC in each LGA of the state is the dependent variable. A 33×33 weights matrix; travel density; land area and major road length of each LGA were used as exogenous variables and population was the IV. The objective is to determine the hotspots and examine whether the number of RTC cases in a given LGA is affected by the number of RTC cases of neighbouring LGAs and an instrumental variable. The hotspots include Oluyole, Ido, Akinyele, Egbeda, Atiba, Oyo East, and Ogbomosho South LGAs. The study concludes that the number of RTC in a given LGA is affected by the number of RTC in contiguous LGAs. The policy implication is that road safety and security measures must be administered simultaneously to LGAs with high concentration of RTC and their neighbours to achieve significant remedial effect.

Modelling Road Traffic Crashes Using Spatial Autoregressive Model With Additional Endogenous Variable

Abstract Road traffic crashes have become a global issue of concern because of the number of deaths and injuries. The model of interest is a linear cross sectional Spatial Autoregressive (SAR) model with additional endogenous variables, exogenous variables and SAR disturbances. The focus is on RTC in Oyo state, Nigeria. The number of RTC in each LGA of the state is the dependent variable. A 33×33 weights matrix; travel density; land area and major road length of each LGA were used as exogenous variables and population was the IV. The objective is to determine the hotspots and examine whether the number of RTC cases in a given LGA is affected by the number of RTC cases of neighbouring LGAs and an instrumental variable. The hotspots include Oluyole, Ido, Akinyele, Egbeda, Atiba, Oyo East, and Ogbomosho South LGAs. The study concludes that the number of RTC in a given LGA is affected by the number of RTC in contiguous LGAs. The policy implication is that road safety and security measures must be administered simultaneously to LGAs with high concentration of RTC and their neighbours to achieve significant remedial effect.

Does Regulatory Bank Oversight Impact Economic Activity? A Local Projections Approach

Existing research generally finds that the magnitude of the effect of supervisory rating shocks on real economic activity is small and short-lived. This is puzzling because corrective actions addressing weaknesses in underwriting and other practices frequently include lending restrictions and thus would be expected to have a stronger effect on real activity. As supervisory actions curb poorly underwritten or uneconomic loans, transmission of macroprudential policy throughout the macroeconomy should be evident in the dynamic responses of the real GDP and other measures of real activity. We use the local projections approach to estimate impulse responses from a vector autoregression (VAR) model. We show that the effect of supervisory stringency shocks is larger than the one estimated with the standard Cholesky structural VAR approach. We find that the effects are asymmetric: bank downgrades lead to a pronounced decline in real activity, while upgrades do not result in its increase. This would follow if the decrease is driven by poor lending practices that would not be expected to resume when the bank is upgraded. The linear framework averages out these effects, overstating the impact of upgrades, and understating that of downgrades. Furthermore, we document the presence of nonlinear effects for the downgrade shocks, as their impact increases disproportionately with its size. Such effects are not observed for upgrade shocks. Finally, we demonstrate that our results are robust to the inclusion of a variety of controls and additional endogenous variables.

Bootstrap J-Test for Panel Data Models with Spatially Dependent Error Components, a Spatial Lag and Additional Endogenous Variables

We develop a bootstrap J-test method for testing a panel model against one non-nested alternative when the competing specifications are estimated by Feasible Generalised Spatial Two Stage Least Squares/Generalised Method of Moments (FGS2SLS/GMM). Both models incorporate spatially correlated error components, thus accounting for spatial heterogeneity via random effects, and accommodate endogenous regressors other than the spatially lagged dependent variable. The proposed scheme is applied to a testing problem involving non-nested wage equations as motivated by the Wage Curve literature and the New Economic Geography theory. Results show that our bootstrap test is a reliable and effective procedure for correcting asymptotic reference critical values and distinguishing between the two rival hypotheses.

Critical issues in spatial models: error term specifications, additional endogenous variables, pre-testing, and Bayesian analysis

This paper is a polished and mildly extended version of the Getis-Ord Lecture I gave at the WRSA conference in Tucson Arizona, February 2015. The force of that lecture was to critically evaluate the literature, and in doing so, suggest alternative directions in our research. In some cases, these suggestions could greatly widen the scope of discussions on a number of important social issues. In sticking close to that lecture, this paper contains only a few references in the text so that it will read as the lecture that was presented. Relevant background references are included in the bibliography.

An Extension of the J‐Test to a Spatial Panel Data Framework

SummaryKelejian (Letters in Spatial and Resources Sciences; 1: 3–11) extended the J‐test procedure to a spatial framework. Although his suggested test was computationally simple and intuitive, it did not use the available information in an efficient manner. Kelejian and Piras (Regional Science and Urban Economics; 41: 281–292) generalized and modified Kelejian's test to account for all the available information. However, neither Kelejian () nor Kelejian & Piras () considered a panel data framework. In this paper we generalize these earlier works to a panel data framework with fixed effects and additional endogenous variables. We give theoretical as well as Monte Carlo results relating to our suggested tests. An empirical application on a crime model for North Carolina is also estimated. Copyright © 2015 John Wiley & Sons, Ltd.

A Command for Estimating Spatial-Autoregressive Models with Spatial-Autoregressive Disturbances and Additional Endogenous Variables

We describe the spivreg command, which estimates the parameters of linear cross-sectional spatial-autoregressive models with spatial-autoregressive disturbances, where the model may also contain additional endogenous variables as well as exogenous variables. spivreg uses results and the literature cited in Kelejian and Prucha (1998, Journal of Real Estate Finance and Economics 17: 99–121; 1999, International Economic Review 40: 509–533; 2004, Journal of Econometrics 118: 27–50; 2010, Journal of Econometrics 157: 53–67); Arraiz et al. (2010, Journal of Regional Science 50: 592–614); and Drukker, Egger, and Prucha (2013, Econometric Reviews 32: 686–733).

Estimating spatial models with endogenous variables, a spatial lag and spatially dependent disturbances: Finite sample properties

Abstract. This paper discusses estimation methods for models including an endogenous spatial lag, additional endogenous variables due to system feedback and an autoregressive or a moving average error process. It extends Kelejian and Prucha's, and Fingleton and Le Gallo's feasible generalized spatial two‐stage least squares estimators and also considers HAC estimation in a spatial framework as suggested by Kelejian and Prucha. An empirical example using real estate data illustrating the different estimators is proposed. The finite sample properties of the estimators are finally investigated by means of Monte Carlo simulation.Resumen. Este artículo discute métodos de estimación para modelos incluyendo un intervalo espacial endógeno, variables endógenas adicionales debido a retroalimentación del sistema y un proceso autorregresivo o uno de error de media móvil. Amplia Kelejian y Prucha's, y los estimadores de mínimos cuadrados bietápicos espaciales generalizados factibles de Fingleton y Le Gallo y considera también la estimación HAC en un marco espacial tal y como sugieren Kelejian y Prucha. Proponemos un ejemplo empírico utilizando datos de bienes inmuebles ilustrando los diferentes estimadores. Las propiedades en muestras finitas de los estimadores se estudian finalmente mediante simulación de Monte Carlo.

Estimation of a Model of Labor Supply, Fertility, and Wages of Married Women

The analysis of labor supply has been enriched in recent years by two developments in the theory of household behavior. One is the conceptualization of labor supply as being associated with decisions concerning a number of nonmarket activities, including childbearing, education, and marriage.' A second is the conceptualization of wage rates in the market and home as choice variables that depend on search, work, and investment activities of the household members.2 The theoretical advances have, in a word, endogenized a wide spectrum of what were previously considered exogenous determinants of labor supply. The analysis has been further complicated by other aspects of these developments: attention to the family context and to simultaneity in work and investment choices among family members; attention to such unobservable constraints as tastes and technology as relevant omitted variables (when they are not considered as additional endogenous variables!) ; 3 and various restrictive assumptions that are necessary to make particular applications of the