485 research outputs found

    The Mundlak spatial estimator

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    The spatial Mundlak model first considered by Debarsy (2012) is an alternative to fixed effects and random effects estimation for spatial panel data models. Mundlak modelled the correlated random individual effects as a linear combination of the averaged regressors over time plus a random time-invariant error. This paper shows that if spatial correlation is present whether spatial lag or spatial error or both, the standard Mundlak result in panel data does not hold and random effects does not reduce to its fixed effects counterpart. However, using maximum likelihood one can still estimate these spatial Mundlak models and test the correlated random effects specification of Mundlak using Likelihood ratio tests as demonstrated by Debarsy for the Mundlak spatial Durbin model.</p

    The Mundlak Spatial Estimator

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    The spatial Mundlak model first considered by Debarsy (2012) is an alternative to fixed effects and random effects estimation for spatial panel data models. Mundlak modelled the correlated random individual effects as a linear combination of the averaged regressors over time plus a random time-invariant error. This paper shows that if spatial correlation is present whether spatial lag or spatial error or both, the standard Mundlak result in panel data does not hold and random effects does not reduce to its fixed effects counterpart. However, using maximum likelihood one can still estimate these spatial Mundlak models and test the correlated random effects specification of Mundlak using Likelihood ratio tests as demonstrated by Debarsy for the Mundlak spatial Durbin model

    The two-way Hausman and Taylor estimator

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     This paper reconsiders the two-way Hausman and Taylor (1981) estimator suggested by Wyhowski (1994). The two-way HT estimator allows some but not necessarily all the regressors to be correlated with the individual and time effects. It also allows the estimation of the effects of time-invariant as well as individual-invariant regressors which are wiped out by the two-way fixed effects estimator. Hausman type tests are proposed for this two-way HT regression to test the over-identification conditions implied by the choice of the uncorrelated regressors. This should prove useful for empirical work in this area. </p

    The Two-way Mundlak Estimator

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    Mundlak (1978) shows that the fixed effects estimator is equivalent to the random effects estimator in the one-way error component model once the random individual effects are modeled as a linear function of all the averaged regressors over time. In the spirit of Mundlak, this paper shows that this result also holds for the two-way error component model once this individual and time effects are modeled as linear functions of all the averaged regressors across time and across individuals. Woolridge (2021) also shows that the two-way fixed effects estimator can be obtained as a pooled OLS with the regressors augmented by the time and individual averages and calls it the two-way Mundlak estimator. While Mundlak (1978) used GLS rather than OLS on this augmented regression, we show that both estimators are equivalent for this augmented regression. This extends Baltagi’s (2006) results from the one-way to the two-way error component model. The F test suggested by Mundlak (1978) to test for this correlation between the random effects and the regressors generate a Hausman (1978) type test that is easily generalizable to the two-way Mundlak regression. In fact, the resulting F-tests for the two-way error component regression are related to the Hausman type tests proposed by Kang (1985) for the two-way error component model

    Consistent estimation of panel data sample selection models

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    Consistent estimation of panel data sample selection model

    Spatial wage curves for formal and informal workers in Turkey

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    This paper estimates spatial wage curves for formal and informal workers in Turkey using individual level data from the Turkish Household Labor Force Survey provided by TURKSTAT for the period 2008–2014. Unlike previous studies on wage curves for formal and informal workers, we extend the analysis to allow for spatial effects. We also consider household characteristics that would affect the selection into formal employment, informal employment, and non-employment. We find that the spatial wage curve relation holds both for formal and informal workers in Turkey for a variety of specifications. In general, the wages of informal workers are more sensitive to the unemployment rates of the same region and other regions than formal workers. We find that accounting for the selection into formal and informal employment affects the magnitudes but not the significance of the spatial wage curves for the formal and informal workers with the latter always being larger in absolute value than that for formal workers.</p

    Robust Dynamic Space-Time Panel Data Models Using ε- contamination: An Application to Crop Yields and Climate Change

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    This paper extends the Baltagi et al. (2018, 2021) static and dynamic ε-contamination papers to dynamic space-time models. We investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach de-parts from the standard Bayesian framework in two ways. First, we consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner (1986)’s g-priors for the variance-covariance matrices. We propose a general “toolbox” for a wide range of specifications which includes the dynamic space-time panel model with random effects, with cross-correlated effects à la Chamberlain, for the Hausman-Taylor world and for dynamic panel data models with homogeneous/heterogeneous slopes and cross-sectional dependence. Using an extensive Monte Carlo simulation study, we compare the finite sample properties of our proposed estimator to those of standard classical estimators. We illustrate our robust Bayesian estimator using the same data as in Keane and Neal (2020). We obtain short run as well as long run effects of climate change on corn producers in the United States

    Robust dynamic space-time panel data models using epsilon-contamination: an application to crop yields and climate change

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    This paper extends the Baltagi et al. (J Econom 202:108–123, 2018; Advances in econometrics, essays in honor of M. Hashem Pesaran, Emerald Publishing, Bingley, 2021) static and dynamic ε-contamination papers to dynamic space–time models. We investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in two ways. First, we consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner (Bayesian inference and decision techniques: essays in honor of Bruno de Finetti. Studies in Bayesian econometrics, vol 6, North-Holland, Amsterdam, pp 389–399, 1986)’s g-priors for the variance–covariance matrices. We propose a general “toolbox” for a wide range of specifications which includes the dynamic space–time panel model with random effects, with cross-correlated effects à la Chamberlain, for the Hausman–Taylor world and for dynamic panel data models with homogeneous/heterogeneous slopes and cross-sectional dependence. Using an extensive Monte Carlo simulation study, we compare the finite sample properties of our proposed estimator to those of standard classical estimators. We illustrate our robust Bayesian estimator using the same data as in Keane and Neal (Quant Econ 11:1391–1429, 2020). We obtain short-run as well as long-run effects of climate change on corn producers in the USA

    Cities in a Pandemic: Evidence from China

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    This paper studies the impact of urban density, city government efficiency, and medical resources on COVID-19 infection and death outcomes in China. We adopt a simultaneous spatial dynamic panel data model to account for (i) the simultaneity of infection and death outcomes, (ii) the spatial pattern of the transmission, (iii) the inter-temporal dynamics of the disease, and (iv) the unobserved city- and time-specific effects. We find that, while population density increases the level of infections, government efficiency significantly mitigates the negative impact of urban density. We also find that the availability of medical resources improves public health outcomes conditional on lagged infections. Moreover, there exists significant heterogeneity at different phases of the epidemiological cycle

    Bayesian estimation of multivariate panel probits with higher-order network interdependence and an application to firms' global market participation in Guangdong

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    This paper proposes a Bayesian estimation framework for panel data sets with binary dependent variables where a large number of cross-sectional units are observed over a short period of time and cross-sectional units are interdependent in more than a single network domain. The latter provides for a substantial degree of flexibility towards modeling the decay function in network neighborliness (e.g., by disentangling the importance of rings of neighbors) or towards allowing for several channels of interdependence whose relative importance is unknown ex ante. Besides the flexible parameterization of cross-sectional dependence, the approach allows for simultaneity of the equations. These features should make the approach interesting for applications in a host of contexts involving structural and reduced-form models of multivariate choice problems at micro-, meso-, and macro-economic levels. The paper outlines the estimation approach, illustrates its suitability by simulation examples, and provides an application to study exporting and foreign ownership among potentially interdependent firms in the specialized and transport machinery sector in the province of Guangdong
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