Search results for "C52"

showing 10 items of 10 documents

Innovation Complementarity and Scale of Production

2006

We present an econometrically feasible model that uses the information contained in the innovation profile of each firm to test for the existence of complementarity among production and innovation strategies. Our approach is able to distinguish between complementarity and correlation induced by unobserved heterogeneity. We apply the model to analyze the Spanish ceramic tile industry where the adoption of the single firing furnace in the 1980's facilitated the introduction of new product designs as well as opening new ways of organizing production. Our econometric results show that there is significant complementarity between product and process innovation. Small firms tend to be more innova…

Economics and Econometricsbusiness.industryProduct innovationjel:C52Innovation processcomplementarity; supermodularity; non-observed heterogeneity; product innovation; process innovationGeneral Business Management and AccountingComplementarity (physics)jel:L20AccountingManufacturingNew product developmentEconomicsjel:O32MarketingbusinessProcess innovationIndustrial organization
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When does Regression discontinuity design work? Evidence from random election outcomes

2018

We use elections data in which a large number of ties in vote counts between candidates are resolved via a lottery to study the personal incumbency advantage. We benchmark non‐experimental regression discontinuity design (RDD) estimates against the estimate produced by this experiment that takes place exactly at the cutoff. The experimental estimate suggests that there is no personal incumbency advantage. In contrast, conventional local polynomial RDD estimates suggest a moderate and statistically significant effect. Bias‐corrected RDD estimates that apply robust inference are, however, in line with the experimental estimate. Therefore, state‐of‐the‐art implementation of RDD can meet the re…

H Social Sciences (General)Economics and EconometricskokeiluInferenceContext (language use)close electionsLotteryD72C52Benchmark (surveying)Political science0502 economics and businessReplication (statistics)ddc:330050602 political science & public administrationEconometricsCutoffregression discontinuity design050207 economicsestimointita112ta511experiment05 social sciencesContrast (statistics)0506 political scienceincumbency advantageRegression discontinuity designkokeet (tutkimustoiminta)C21vaalitvaalijärjestelmätQUANTITATIVE ECONOMICS
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Nature et impacts des effets spatiaux sur les valeurs immobilières : le cas de l'espace urbanisé francilien

2013

International audience

JEL: R - Urban Rural Regional Real Estate and Transportation Economics/R.R1 - General Regional Economics/R.R1.R14 - Land Use PatternsJEL: R - Urban Rural Regional Real Estate and Transportation Economics/R.R2 - Household Analysis/R.R2.R21 - Housing DemandJEL : C - Mathematical and Quantitative Methods/C.C5 - Econometric Modeling/C.C5.C52 - Model Evaluation Validation and SelectionJEL : R - Urban Rural Regional Real Estate and Transportation Economics/R.R2 - Household Analysis/R.R2.R21 - Housing Demand[SHS.STAT]Humanities and Social Sciences/Methods and statisticséconométrie spatialemodèle hédoniqueJEL: C - Mathematical and Quantitative Methods/C.C1 - Econometric and Statistical Methods and Methodology: General/C.C1.C12 - Hypothesis Testing: General[SHS.ECO]Humanities and Social Sciences/Economics and FinanceJEL: C - Mathematical and Quantitative Methods/C.C5 - Econometric Modeling/C.C5.C52 - Model Evaluation Validation and SelectionJEL : R - Urban Rural Regional Real Estate and Transportation Economics/R.R1 - General Regional Economics/R.R1.R14 - Land Use Patternseffets de voisinage[SHS.STAT] Humanities and Social Sciences/Methods and statisticsJEL : C - Mathematical and Quantitative Methods/C.C1 - Econometric and Statistical Methods and Methodology: General/C.C1.C12 - Hypothesis Testing: General[ SHS.ECO ] Humanities and Social Sciences/Economies and financesvaleurs immobilières[SHS.ECO] Humanities and Social Sciences/Economics and Finance[ SHS.STAT ] Humanities and Social Sciences/Methods and statisticsComputingMilieux_MISCELLANEOUS
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NEIGHBORHOOD EFFECTS IN SPATIAL HOUSING VALUE MODELS. THE CASE OF THE METROPOLITAN AREA OF PARIS (1999)

2009

In hedonic housing models, the spatial dimension of housing values are traditionally processed by the impact of neighborhood variables and accessibility variables. In this paper we show that spatial effects might remain once neighborhood effects and accessibility have been controlled for. We notably stress on three sides of neighborhood effects: social capital, social status and social externalities and consider the accessibility to the primary economic center as describing the urban spatial trend. Using spatial econometrics specifications of the hedonic equation, we estimate whether spatial effects impact the housing values. Our empirical case concerns the Metropolitan Area (MA) of Paris i…

JEL: R - Urban Rural Regional Real Estate and Transportation Economics/R.R1 - General Regional Economics/R.R1.R14 - Land Use PatternsJEL: R - Urban Rural Regional Real Estate and Transportation Economics/R.R2 - Household Analysis/R.R2.R21 - Housing DemandJEL : R - Urban Rural Regional Real Estate and Transportation Economics/R.R2 - Household Analysis/R.R2.R21 - Housing DemandJEL : C - Mathematical and Quantitative Methods/C.C5 - Econometric ModelingC520Modèle hédoniqueJEL: C - Mathematical and Quantitative Methods/C.C5 - Econometric ModelingJEL: C - Mathematical and Quantitative Methods/C.C2 - Single Equation Models • Single Variables/C.C2.C21 - Cross-Sectional Models • Spatial Models • Treatment Effect Models • Quantile Regressions[SHS.ECO]Humanities and Social Sciences/Economics and FinanceC120C520R140R210 [Hedonic modelhousing valueneighborhood effectsspatial econometricsModèle hédoniquevaleur immobilièreeffets de voisinageéconométrie spatiale JEL Classification]JEL : C - Mathematical and Quantitative Methods/C.C2 - Single Equation Models • Single Variables/C.C2.C21 - Cross-Sectional Models • Spatial Models • Treatment Effect Models • Quantile RegressionsR210JEL : R - Urban Rural Regional Real Estate and Transportation Economics/R.R1 - General Regional Economics/R.R1.R14 - Land Use Patternsspatial econometricsvaleur immobilièreeffets de voisinageneighborhood effectsHedonic model[ SHS.ECO ] Humanities and Social Sciences/Economies and financeshousing valueéconométrie spatiale JEL Classification : C120[SHS.ECO] Humanities and Social Sciences/Economics and FinanceR140
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On the Ambiguous Consequences of Omitting Variables

2015

This paper studies what happens when we move from a short regression to a long regression (or vice versa), when the long regression is shorter than the data-generation process. In the special case where the long regression equals the data-generation process, the least-squares estimators have smaller bias (in fact zero bias) but larger variances in the long regression than in the short regression. But if the long regression is also misspecified, the bias may not be smaller. We provide bias and mean squared error comparisons and study the dependence of the differences on the misspecification parameter.

Statistics::Machine LearningStatistics::TheoryC51C52BiasMisspecificationLeast-squares estimatorsddc:330Statistics::MethodologyC13Mean squared errorOmitted variablesStatistics::Computation
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On the ambiguous consequences of omitting variables

2015

This paper studies what happens when we move from a short regression to a long regression (or vice versa), when the long regression is shorter than the data-generation process. In the special case where the long regression equals the data-generation process, the least-squares estimators have smaller bias (in fact zero bias) but larger variances in the long regression than in the short regression. But if the long regression is also misspecified, the bias may not be smaller. We provide bias and mean squared error comparisons and study the dependence of the differences on the misspecification parameter.

Statistics::TheoryMean squared errorjel:C52Regression dilutionjel:C51Local regressionjel:C13Regression analysisOmitted-variable biasCross-sectional regressionStatistics::ComputationOmitted variables Misspecification Least-squares estimators Bias Mean squared errorStatistics::Machine LearningStatisticsEconometricsStatistics::MethodologyRegression diagnosticNonlinear regressionMathematics
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Multidimensional Health Modelling: Association between Socioeconomic Factors and Health in Latvia

2012

This paper proposes new approach for modelling self-assessed health. We find that the concept of health is too complicated to measure effects of health determinants using a one-dimensional econometric model. We apply two-dimensional stereotype logistic model that allows capturing nonmonotonicity in effects of factors and revealing significant effects that remain unrevealed if single dimension models, such as ordered logit or ordered probit, are used. Modelling self-assessed health using multi-dimensional stereotype logit provides higher model goodness of fit and quality measures in comparison to ordered probit model. Multi-dimensional stereotype logit is applied to estimate association betw…

jel:I18jel:C52Self-assessed health Socioeconomic determinants Nonmonotonicity Stereotype logitjel:I10Economic Research Guardian
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Association Between Self-Assessed Health and Attitude Towards Own Health

2014

This paper explores association between health and attitude towards own health in two dimensions – taking care of own health and lifestyle. We apply two-dimensional stereotype logit model to estimate association between self-assessed health and attitude towards health, after accounting for socioeconomic factors. We find evidence of strong positive association between health status and (perceived) taking care of own health and lifestyle. Analysis of perception of the two concepts – "taking care of own health" and "healthy lifestyle" – provides insights into possible reasons of not very good indicators of health behaviour among Latvian population.

jel:I18jel:C52jel:I10Self-assessed health Attitude towards health Lifestyle Stereotype logitEconomic Research Guardian
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Multidimensional health modeling: Association between socioeconomic and psychosocial factors and health in Latvia

2009

This research aims at estimating association between socioeconomic and psychosocial factors on the one hand and health in Latvia on the other hand. While information on association between socioeconomic determinants of population health in Latvia is scarce, effect of psychosocial resources on individual health in this country hasn’t been estimated before. We find empirical support for the association between different psychosocial factors and physical health in Latvia. This paper proposes new approach for modelling self-assessed health. We find that the concept of health is too complicated to measure effects of health determinants using a one-dimensional econometric model. We apply two-dime…

medicine.medical_specialtyjel:C52Public healthLogitOrdered probitPopulation healthLogistic regressionjel:I10Econometric modeljel:I18Political sciencemedicineEconometricsself-assessed health; socioeconomic determinants; psychosocial factors; nonmonotonicity; stereotype logitOrdered logitPsychosocial
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Hétérogénéité spatiale : principes et méthodes

2004

Spatial Heterogeneity : Principles and Methods This article has a dual purpose . First , it describes the main econometric specifications which can be used to represent spatial heterogeneity , reflected in an instability of parameters in space and / or a heteroscedasticity of error terms . Only the specifications valid in cross-section are examined . Second , it explains the links between spatial heterogeneity and autocorrelation , the other major feature of localised data , defined by the absence of independence between geographical observations . In particular , we look at the extent to which traditional tests of heteroscedasticity or instability need to be amended to take account of spat…

spatial econometrics ; spatial heterogeneity ; structural instability ; heteroscedasticity ; JEL Classification C51 - C52 - R15hétérogénéité spatiale ; économétrie spatiale ; Classification JEL C51 - C52 - R15 ; hétéroscédasticité ; instabilité structurelleGeographyBusiness and International ManagementGeneral Economics Econometrics and FinanceHumanitiesÉconomie & prévision
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