Download or read book Essays in Spatial Panel Econometrics written by Silvia Palombi and published by . This book was released on 2016 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: The third essay (Chapter 4) is an extension of the previous chapter, and provides conclusive evidence by implementing a more formal and rigorous approach to testing a null model against a non-nested alternative, i.e. the J-test. This is a well-established technique for choosing among non-nested rivals, and in this chapter I develop a version of the test for specifications (SARAR-RE models) which feature spatially correlated error components, thus accounting for interregional heterogeneity via random effects (also subjected, like the disturbances, to a spatially autoregressive process), as well as a spatial lag of the dependent variable and additional, potentially endogenous regressors. This chapter thus makes a valuable addition to the literature on non-nested hypotheses testing in the spatial panel context by extending the toolkit to random-effects models. I also provide Monte Carlo evidence showing that there are distributional issues associated with the asymptotic use of the J-test in small-to-medium samples, so another novelty of this chapter is the implementation of a Bootstrap scheme to construct a valid null reference distribution in finite samples when the null and alternative are SARAR-RE models estimated by S2SLS / GMM. In terms of the empirical application, bootstrap J-test results confirm the bootstrap ANM results from the previous chapter that the wage curve rejects NEG theory while UE theory is equally successful. Another finding, from the methodological angle, is that the bootstrap J-test is a reliable and effective procedure for correcting asymptotic reference critical values and distinguishing between competing hypotheses in all cases where one is not a reduced form of the other.The fourth and final essay (Chapter 5) is one of few to reconsider from a spatial panel econometric perspective an economic relationship - the ‘empirical law of economics’ known as Okun’s Law - which has been traditionally considered at macro level with no attention for sub-national phenomena; it is the first to do so for Great Britain, looking at the 128 British NUTS3 regions over the period 1985-2011. By means of specialist techniques recently devised for spatial data, I show that regional interdependencies have a prominent role in the unemployment-output relationship; the total Okun’s Law effect itself is close to the ‘law’ of -0.30 but more than two thirds of this are accounted for by the impact on local unemployment rate of real output variations in areas nearby, a finding suggesting that policy intervention at both national and regional level on a country’s labour market can be more effective if spatial effects are factored into the analysis and modelled / tested explicitly.

Download or read book Essays on Spatial Panel Econometrics written by Karen Miranda and published by . This book was released on 2018 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: Aquesta tesi proposa una metodologia per identificar i estimar externalitats espacials associades als efectes inviduals en models per a dades de panell. La clau d'aquest nou enfocament és l'ús d'una especificació d'efectes aleatoris correlacionats. Distingir entre els efectes individuals i les seves externalitats espacials pot proporcionar informació interessant sobre com les característiques no observades dels territoris veïns afecten la producció d'un cert territori i, viceversa, com les característiques no observades d'un territori afecten la producció dels territoris veïns. En els models de creixement, per exemple, es pot obtenir una mesura de la productivitat no observada de les regions a partir de l'estimació dels efectes individuals Islam (1995). Però com afecta el contagi espacial de la productivitat no observada d'aquestes regions al seu creixement econòmic? Aquesta tesi analitza diversos models economètrics i amb l'objectiu d'identificar i estimar aquest tipus d'efectes. A més, cada capítol proporciona evidència empírica relacionada. En particular, aquesta tesi proposa l'ús d'un model d'efectes aleatoris correlacionats (Mundlak, 1978; Chamberlain, 1982) per analitzar les externalitats espacials dels efectes individuals. En aquest sentit, els models analitzats en aquesta tesi tenen una estructura de l'error om els components estan ponderats espacialment, de manera molt similar a la del treball de Kapoor et al. (2007). En particular, el segon capítol considera una especificació d'efectes aleatoris correlacionats en un model de dades de panell espacial estàtic i el tercer ho fa en un model de dades de panell dinàmic espacial. El quart capítol fa ús de les troballes dels capítols anteriors en un model de creixement amb externalitats espacials.

Download or read book Essays in Spatial Econometrics written by Yang Yang (Econometrics researcher) and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spatial econometrics models, especially the spatial autoregressive (SAR) model and its extension to panel settings had been used widely in empirical research in several different fields, especially when we need to capture the effects from networks. However, more empirical researchers are focusing on new questions where the linear spatial econometrics models could not handle. My dissertation tries to extend traditional models to capture two types of effect: risk spillover through financial networks and heterogeneous peer effect through social networks, and develops likelihood approach to estimate these models. Chapter 1 tries to incorporate risk spillover effect with GARCH type models. By introducing both intra-temporal and inter-temporal risk spillover through network, we propose a new multivariate conditional volatility model. For stationary case, the model can capture the dynamic of conditional heteroskedasticity structure when there are long-run stable links among multiple markets, and it is easy to be estimated consistently by QMLE approach. By Monte Carlo simulations, we show good finite sample performance when n/T → 0. When applying the model to monthly stock return innovations of 11 eurozone countries from March 1999 to April 2021, by using geographical and institutional links to capture the network between the countries, the performance of our model dominates single variate GARCH(1,1), EGARCH(1,1) and multivariate GARCH with both constant correlation and dynamic conditional correlation settings by likelihood values and AIC criteria. ii Chapter 2 considers social interaction models with group fixed effects and observed heterogeneity among agents. By likelihood approach, with the control of group-level confounding effects of the common variables, both heterogeneous endogenous peer effects and exogenous contextual effects can be identified and estimated consistently. Under some regularity assumptions, we prove the consistency and asymptotic normality of the QMLE. Monte Carlo simulation results show that our QMLE has good finite sample performance. For an application, we investigate the China Education Panel Survey (CEPS) and focus on gender heterogeneity on academic achievement of Grade 8 students in junior high school. We capture significant gender disparities in peer effects from gender subgroups in a classroom. Besides, female students’ test scores are more subject to both female and male peers’ average achievement.

Download or read book Three Essays on Spatial Econometric Models with Missing Data written by Wei Wang and published by . This book was released on 2010 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: This dissertation is composed of three essays on spatial econometric models with missing data. Spatial models that have a long history in regional science and geography have received substantial attention in various areas of economics recently. Applications of spatial econometric models prevail in urban, developmental and labor economics among others. In practice, an issue that researchers often face is the missing data problem. Although many solutions such as list-wise deletion and EM algorithm can be found in literature, most of them are either not suited for spatial models or hard to apply due to technical difficulties. My research focuses on the estimation of the spatial econometric models in the presence of missing data problems. The first chapter develops a GMM method based on linear moments for the estimation of mixed regressive, spatial autoregressive (MRSAR) models with missing observations in the dependent variables. The estimation method uses the expectation of the missing data, as a function of the observed independent variables and the parameters to be estimated, to replace the missing data themselves in the estimation. The proposed GMM estimators are shown to be consistent and asymptotically normal. Feasible optimal weighting matrix for the GMM estimation is given. We extend our estimation method to MRSAR models with heteroskedastic disturbances, high order MRSAR models and unbalanced spatial panel data models with random effects as well. From these extensions, we see that the proposed GMM method has more compatibility, compared with the conventional EM algorithm. The second chapter considers a group interaction model first proposed by Lee (2006); this model is a special case of the spatial autoregressive (SAR) models. It is a first attempt to estimate the model in a more general random sample setting, i.e. a framework in which only a random sample rather than the whole population in a group is available. We incorporate group heteroskedasticity along with the endogenous, exogenous and group fixed effects in the model. We prove that, under some basic assumptions and certain identification conditions, the quasi maximum likelihood (QML) estimators are consistent and asymptotically normal when the functional form of the group heteroskedasticity is known. Two types of misspecifications are considered, and, under each, the estimators are inconsistent. We also propose IV estimation in the case that the group heteroskedasticity is unknown. A LM test of group heteroskedasticity is given at the end. The third chapter considers the same group interaction model as that in the second chapter, but focuses on the large group interaction case and uses a random effects setting for the group specific characters. A GMM estimation framework using moment conditions from both within and between equations is applied to the model. We prove that under some basic assumptions and certain identification conditions, the GMM estimators are consistent and asymptotically normal, and the convergence rates of the estimators are higher than those of the estimators derived from the within equations only. Feasible optimal GMM estimators are proposed.

Download or read book Three Essays on Spatial Econometrics and Empirical Industrial Organization written by Sang-Yeob Lee and published by . This book was released on 2008 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: The first essay explores the consequences of misspecified spatial interdependence structure in SAR models with a row-normalized weight matrix. I provide the analytical formulae for the asymptotic biases of the OLS estimator when a spatial weight matrix is over-specified, under-specified, or omitted in a simple linear regression model. I then design Monte Carlo experiments to study how a misspecified spatial weight matrix in the SAR model might impact the finite sample properties of the 2SLSE and MLE. The major finding is that an "over-specification" of the weight matrix causes less bias in 2SLSE and MLE as well as lower RMSE than an "under-specification." The results also strongly suggest that goodness of fit measures such as adjusted R-square and log-likelihood can serve as selection criteria for the choice of a spatial weight matrix. In the second essay, I consider the effectiveness of Wald, distance difference, minimum Chi-square, and gradient tests within GMM framework in selecting different specifications of spatial weights in SAR models. The two major results I obtain are (1) that for each of the five tests, GMM framework significantly improves the empirical power of the tests over 2SLS framework, and (2) that when performed in GMM framework, all five tests have suitable empirical size and power with similar performance outcomes. Finally, the third essay investigates the nature of competition in the retail gasoline market using a two year panel data of weekly prices for gas stations in San Diego County. I use IV methods to estimate several spatial autoregressive (SAR) models of stations' price reaction functions after specifying spatial weights based on distance between stations. By using the SAR model, I am able to identify that the brand of competing stations and their relative geographic proximity to the original station are important factors in explaining price variation across gasoline stations, as opposed to just the number of competing stations.

Download or read book Three Essays on Spatial Econometrics with an Emphasis on Testing written by Yu-Hsien Kao and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays on Spatial Panel Data Models with Common Factors written by Wei Shi and published by . This book was released on 2016 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: My dissertation research addresses issues in spatial panel data models, which study the interactions of economic units across space and time. Individuals interact with their neighbors and the outcomes are interdependent. The strength of the interaction depends on the distance between the individuals, which can be based on geography or constructed from economic theory. Accounting for spatial interactions allows one to quantify both the direct effect of a variable and its indirect effect through impacting neighbors. However, two issues often arise. First, spatial dependence can be alternatively generated from common unobserved factors (e.g. economy-wide shocks) where neighbors have similar responses. Second, the distance between economic units can be endogenous, and this will in fact be the case if the distance is constructed from variables that correlate with disturbances in the outcomes. The first chapter studies the estimation of a dynamic spatial panel data model with interactive individual and time effects with large n and T. The model has a rich spatial structure including contemporaneous spatial interaction and spatial heterogeneity. Dynamic features include individual time lag and spatial diffusion. In a standard two way fixed effects panel regression model, the unobservables contain an individual specific but time invariant component, and a component that is time variant but common across individuals. We generalize this model by allowing the interaction between time effects and individual effects. This chapter provides a tool for empirical researchers to guard against attributing correlated responses to common time effects as spatial effects. The interactive effects are treated as parameters, so as to allow correlations between the interactive effects and the regressors. We consider a quasi-maximum likelihood estimation and show estimator consistency and characterize its asymptotic distribution. The Monte Carlo experiment shows that the estimator performs well and the proposed bias correction is effective. The second chapter proposes a unified approach to model endogenous spatial dependences while accounting for common factors. The spatial weights matrices are constructed from variables that may correlate with the disturbances in the outcomes. We make minimal assumptions on the distributions of the factors and follow a fixed effects approach. We provide conditions under which the quasi-maximum likelihood estimator is consistent and asymptotically normal, under the asymptotics where both the cross section and time dimensions become large. The limiting distribution is normal but may not be centered for the estimates of the spatial interaction coefficient and the variances. An analytical bias correction is proposed to improve the inference. The Monte Carlo simulations demonstrate good finite sample properties of the bias corrected estimator. We illustrate the empirical relevance of the theory by applying the method to analyze the effect of house price dynamics on reverse mortgage origination rates.

Download or read book Three Essays in Spatial Econometrics written by and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays on Spatial Econometrics written by Saruta Benjanuvatra and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays in Spatial Econometrics written by and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays in Spatial Econometrics written by Fei Jin and published by . This book was released on 2013 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: This dissertation consists of three chapters covering the following topics in spatial econometrics: estimation, specification and the bootstrap.

Download or read book Essays on Spatial Econometrics written by Osman Dogan and published by . This book was released on 2015 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays on Spatial Econometrics of the Housing Market written by Bing Zhu and published by . This book was released on 2011 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Two Essays on Spatial Econometrics and an Essay on Pre employment Credit Checks written by Xin Yu and published by . This book was released on 2014 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation consists of three chapters. Chapter 1 and Chapter 2 discuss two topics in spatial econometrics. Chapter 3 discusses one topic on pre-employment credit checks in job markets.

Download or read book Three Essays on Spatial Econometrics written by Xiaoyi Han and published by . This book was released on 2014 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: My job market paper, "Bayesian Estimation of a Spatial Autoregressive Model with an Unobserved Endogenous Spatial Weight Matrix and Unobserved Factors", examines the specification and estimation of the SAR model with new features. Motivated by the spillover effects of state medicaid spending on welfare programs, we combine all these new features together for the first time in the SAR model. Specifically, we focus on two ways of defining neighborliness (a source of unobserved spatial weight matrix W): one based on geographical distance and the other on "economic" distance. In this particular application, endogeneity of W comes from the correlation of economic distance and the disturbances in the SAR equation. Unobserved factors are introduced to control for common shocks to all states. For the estimation of the model, the Bayesian MCMC method is employed, which is also supported by simulation results. We find that a dollar increase in a state's neighbors' Medicaid related spending will increase its own Medicaid related spending by about 52 cents. Both geographical and economic distances are shown to have significant effects on the interaction strength of state Medicaid related spending. Our results suggest that in the context of Medicaid spending, welfare motivated move and yardstick competition are both sources of strategic interactions among state governments.

Download or read book Essays on Spatial Dynamic Panel Data Model Theories and Applications written by Jihai Yu and published by . This book was released on 2007 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation is composed of three papers about the theories and application of spatial dynamic panel data model with fixed effects. The first paper investigates the asymptotic properties of quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both the number of individuals n and the number of time periods T are large. We consider the case where T is asymptotically large relative to n, the case where T is asymptotically proportional to n, and the case where n is asymptotically large relative to T. In the case where T is asymptotically large relative to n, the estimators are nT consistent and asymptotically normal, with the limit distribution centered around 0. When n is asymptotically proportional to T, the estimators are nT consistent and asymptotically normal, but the limit distribution is not centered around 0; and when n is large relative to T, the estimators are consistent with rate T, and have a degenerate limit distribution. We also propose a bias correction for our estimators. We show that when T grows faster than n1/3, the correction will asymptotically eliminate the bias and yield a centered confidence interval. The second paper covers a nonstationary case where there are units roots in the data generating process. When not all the roots in the DGP are unity, the estimators rate of convergence will be the same as the stationary case, and the estimators can be asymptotically normal. But for the estimators' asymptotic variance matrix, it will be driven by the nonstationary component into a singular matrix. Consequently, a linear combination of the spatial and dynamic effects can converge with a higher rate. We also propose a bias correction for our estimators. We show that when T grows faster than n 1/3, the correction will asymptotically eliminate the bias and yield a centered confidence interval. In the third paper, a spatial dynamic panel data approach is proposed to study growth convergence in the U.S. economy. In neoclassical model, countries are assumed to be independent from each other, which does not hold in the real world. We introduce technological spillovers and factor mobility into the neoclassical framework, showing that the convergence rate is higher and there is spatial correlation. Exploiting annual data on personal state income spanning period 1961-2000 for the 48 contiguous states, we obtain empirical results consistent with the model prediction.

Download or read book Essays on Theories and Applications of Spatial Econometric Models written by Xu Lin and published by . This book was released on 2006 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: As an effective method in analyzing interdependence among the observations, the spatial autoregressive (SAR) models have witnessed ever-increasing applications. This dissertation intends to enrich both the spatial econometrics theory and the social interaction estimations. In the first essay, a SAR model with group unobservables is applied to analyze peer effects in student academic achievement. Unlike the linear-in-means model in Manski (1993), the SAR model can identify both endogenous and contextual social effects due to variations in the peer measurements, thus resolving the "reflection problem". The group fixed effects term captures the confounding effects of the common variables faced by the same group members. I use datasets from the National Longitudinal Study of Adolescent Health (Add Health) survey and specify peer groups as friendship networks. I find evidence for both endogenous and contextual effects, even after controlling for school-grade fixed effects. The result indicates that students benefit from the presence of high quality peers, and that associating with peers living with both parents helps improve a student's GPA, while associating with peers whose mothers receive welfare has a negative effect. The second essay considers the GMM estimation of SAR models with unknown heteroskedasticity. We show that MLE is inconsistent whereas GMM estimators obtained from certain moment conditions are robust. Asymptotically valid inferences can be drawn from the consistent covariance matrix estimator. And efficiency can be improved by constructing the optimal weighted GMM estimation. We also propose some general tests for heteroskedasticity. In the Monte Carlo study, 2SLS estimators have large variances and biases in finite samples for cases where regressors do not have strong effects. The robust GMM estimator has desirable properties while the biases associated with MLE and non-robust GMM estimator may remain in large sample, especially, for the spatial effect coefficient and the intercept term. However, the magnitudes of biases are only moderate and those biases may be statistically insignificant with moderate large sample sizes. The various approaches are applied to the study of county teenage pregnancy rates. The results suggest a strong spatial convergence among county teenage pregnancy rates with a significant spatial effect.