Spatial econometrics for misaligned data

@article{Pouliot2021SpatialEF,
  title={Spatial econometrics for misaligned data},
  author={Guillaume Pouliot},
  journal={Journal of Econometrics},
  year={2021}
}
  • G. Pouliot
  • Published 1 July 2021
  • Economics
  • Journal of Econometrics
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4 Citations
Background Citations
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4 Citations

Essays on Public and Labor Economics

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References

SHOWING 1-10 OF 66 REFERENCES

QML estimation of spatial dynamic panel data models with endogenous time varying spatial weights matrices

Maximum likelihood estimation of a spatial autoregressive Tobit model

Regression with spatially misaligned data

We present a simple approach to the problem of estimating the regression slope parameter from spatially misaligned point data. We assume a linear regression model with errors and covariates from two…

Economic Shocks and Civil Conflict: An Instrumental Variables Approach

Estimating the impact of economic conditions on the likelihood of civil conflict is difficult because of endogeneity and omitted variable bias. We use rainfall variation as an instrumental variable…

What Do We Learn from the Weather? The New Climate-Economy Literature

A rapidly growing body of research applies panel methods to examine how temperature, precipitation, and windstorms influence economic outcomes. These studies focus on changes in weather realizations…

Maximum likelihood estimation of models for residual covariance in spatial regression

We describe the maximum likelihood method for fitting the linear model when residuals are correlated and when the covariance among the residuals is determined by a parametric model containing unknown…

Estimation and Inference in Two-Step Econometric Models

A commonly used procedure in a wide class of impirical applications is to impute unobserved regressors, such as expectations, from an auxiliary econometric model. This two-step (T-S) procedure fails…

Temperature Shocks and Economic Growth: Evidence from the Last Half Century

This paper uses historical fluctuations in temperature within countries to identify its effects on aggregate economic outcomes. We find three primary results. First, higher temperatures substantially…

On maximum likelihood estimation of parameters in incorrectly specified models of covariance for spatial data

The analysis of, and from, models of spatial data usually proceeds under the assumption, often implicit, that the correct model has been specified. However, any model identification procedures based…

Maximum Likelihood Estimation of Misspecified Models

This paper examines the consequences and detection of model misspecification when using maximum likelihood techniques for estimation and inference. The quasi-maximum likelihood estimator (QMLE)…
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