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40
Under the hood: issues in the specification and interpretation of spatial regression models
 Agricultural Economics
, 2002
"... This paper reviews a number of conceptual issues pertaining to the implementation of an explicit “spatial ” perspective in applied econometrics. It provides an overview of the motivation for including spatial effects in regression models, both from a theorydriven as well as from a datadriven persp ..."
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Cited by 47 (1 self)
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This paper reviews a number of conceptual issues pertaining to the implementation of an explicit “spatial ” perspective in applied econometrics. It provides an overview of the motivation for including spatial effects in regression models, both from a theorydriven as well as from a datadriven perspective. Considerable attention is paid to the inferential framework necessary to carry out estimation and testing and the different assumptions, constraints and implications embedded in the various specifications available in the literature. The review combines insights from the traditional spatial econometrics literature as well as from geostatistics, biostatistics and medical image analysis.
2007): “Understanding Bias in Nonlinear Panel Models: Some Recent Developments
 Advances in Economics and Econometrics, Ninth World Congress
"... The purpose of this paper is to review recently developed biasadjusted methods of estimation of nonlinear panel data models with fixed effects. For some models, like static linear and logit regressions, there exist fixedT consistent estimators as n →∞. Fixed T consistency is a desirable property b ..."
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Cited by 27 (6 self)
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The purpose of this paper is to review recently developed biasadjusted methods of estimation of nonlinear panel data models with fixed effects. For some models, like static linear and logit regressions, there exist fixedT consistent estimators as n →∞. Fixed T consistency is a desirable property because for many panels T is much smaller than n.
Discrete Choices with Panel Data
 Investigaciones Económicas
, 2003
"... This paper reviews the existing approaches to deal with panel data binary choice models with individual effects. Their relative strengths and weaknesses are discussed. Much theoretical and empirical research is needed in this area, and the paper points to several aspects that deserve further investi ..."
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Cited by 26 (6 self)
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This paper reviews the existing approaches to deal with panel data binary choice models with individual effects. Their relative strengths and weaknesses are discussed. Much theoretical and empirical research is needed in this area, and the paper points to several aspects that deserve further investigation. In particular, I illustrate the usefulness of asymptotic arguments in providing both approximately unbiased moment conditions, and approximations to sampling distributions for panels of different sample sizes. JEL classification:C23.
The behavior of the maximum likelihood estimator of limited dependent variable models in the presence of fixed effects
 Econometrics Journal
, 2004
"... of fixed effects ..."
Fitting vast dimensional timevarying covariance models, Oxford Financial Research Centre, Financial Economics Working Paper n
, 2008
"... Building models for high dimensional portfolios is important in risk management and asset allocation. Here we propose a novel and fast way of estimating models of timevarying covariances that overcome an undiagnosed incidental parameter problem which has troubled existing methods when applied to hu ..."
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Cited by 18 (3 self)
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Building models for high dimensional portfolios is important in risk management and asset allocation. Here we propose a novel and fast way of estimating models of timevarying covariances that overcome an undiagnosed incidental parameter problem which has troubled existing methods when applied to hundreds or even thousands of assets. Indeed we can handle the case where the crosssectional dimension is larger than the time series one. The theory of this new strategy is developed in some detail, allowing formal hypothesis testing to be carried out on these models. Simulations are used to explore the performance of this inference strategy while empirical examples are reported which show the strength of this method. The out of sample hedging performance of various models estimated using this method are compared.
2007): “Fitting and testing vast dimensional timevarying covariance models
"... Building models for high dimensional portfolios is important in risk management and asset allocation. Here we propose a novel way of estimating models of timevarying covariances that overcome some of the computational problems and an undiagnosed incidental parameter problem which have troubled exis ..."
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Cited by 10 (0 self)
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Building models for high dimensional portfolios is important in risk management and asset allocation. Here we propose a novel way of estimating models of timevarying covariances that overcome some of the computational problems and an undiagnosed incidental parameter problem which have troubled existing methods when applied to hundreds or even thousands of assets. The theory of this new strategy is developed in some detail, allowing formal hypothesis testing to be carried out on these models. Simulations are used to explore the performance of this inference strategy while empirical examples are reported which show the strength of this method. The out of sample hedging performance of various models estimated using this method are compared.
Safer ratios, riskier portfolios: banks’ response to government aid. Unpublished working paper
, 2010
"... We study the effect of government assistance on bank risk taking. Using handcollected data on bank applications for government assistance under the Troubled Asset Relief Program (TARP), we investigate the effect of both application approvals and denials. To distinguish banks ’ risk taking behavior ..."
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Cited by 9 (1 self)
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We study the effect of government assistance on bank risk taking. Using handcollected data on bank applications for government assistance under the Troubled Asset Relief Program (TARP), we investigate the effect of both application approvals and denials. To distinguish banks ’ risk taking behavior from changes in economic conditions, we control for the volume and quality of credit demand based on microlevel data on home mortgages and corporate loans. Our differenceindifference analysis indicates that banks make riskier loans and shift investment portfolios toward riskier securities after being approved for government assistance. However, this shift in risk occurs mostly within the same asset class and, therefore, remains undetected by the closelymonitored capitalization levels, which indicate an improved capital position at approved banks. Consequently, these banks
ROBUST PRIORS IN NONLINEAR PANEL DATA MODELS
"... Many approaches to estimation of panel models are based on an average or integrated likelihood that assigns weights to di erent values of the individual e ects. Fixed e ects, random e ects, and Bayesian approaches all fall in this category. We provide a characterization of the class of weights (or p ..."
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Cited by 7 (0 self)
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Many approaches to estimation of panel models are based on an average or integrated likelihood that assigns weights to di erent values of the individual e ects. Fixed e ects, random e ects, and Bayesian approaches all fall in this category. We provide a characterization of the class of weights (or priors) that produce estimators that are rstorder unbiased. We show that such bias reducing weights will depend on the data in general unless an orthogonal reparameterization or an essentially equivalent condition is available. Two intuitively appealing weighting schemes are discussed. We argue that asymptotically valid con dence intervals can be read from the posterior distribution of the common parameters when N and T grow at the same rate. Next, we show that random e ects estimators are not bias reducing in general and discuss important exceptions. Moreover, the bias depends on the KullbackLeibler distance between the population distribution of the e ects and its best approximation in the random e ects family. Finally, we show that in general standard random e ects estimation of marginal e ects is inconsistent for large T, whereas the posterior mean of the marginal e ect is largeT consistent, and we provide conditions for bias reduction. Some examples and Monte Carlo experiments illustrate the results.
Splitpanel jackknife estimation of fixedeffect models
 Mimeo
, 2012
"... We propose a jackknife for reducing the order of the bias of maximum likelihood estimates of nonlinear dynamic fixed effects panel models. In its simplest form, the halfpanel jackknife, the estimator is just 2 θ − θ1/2, where θ is the MLE from the full panel and θ1/2 is the average of the two hal ..."
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Cited by 6 (0 self)
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We propose a jackknife for reducing the order of the bias of maximum likelihood estimates of nonlinear dynamic fixed effects panel models. In its simplest form, the halfpanel jackknife, the estimator is just 2 θ − θ1/2, where θ is the MLE from the full panel and θ1/2 is the average of the two halfpanel MLEs, each using T/2 time periods and all N crosssectional units. This estimator eliminates the firstorder bias of θ. The order of the bias is further reduced if two partitions of the panel are used, for example, two halfpanels and three 1/3panels, and the corresponding MLEs. On further partitioning the panel, any order of bias reduction can be achieved. The splitpanel jackknife estimators are asymptotically normal, centered at the true value, with variance equal to that of the MLE under asymptotics where T is allowed to grow slowly with N. In analogous fashion, the splitpanel jackknife reduces the bias of the profile likelihood and the bias of marginaleffect estimates. Simulations in fixedeffect dynamic discretechoice models with small T show that the splitpanel jackknife effectively reduces the bias of the MLE and yields confidence intervals with much better coverage.
Bias Corrections for TwoStep Fixed Effects Panel Data Estimators
"... This paper introduces biascorrected estimators for nonlinear panel data models with both time invariant and time varying heterogeneity. These include limited dependent variable models with both unobserved individual effects and endogenous explanatory variables, and sample selection models with unob ..."
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Cited by 6 (1 self)
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This paper introduces biascorrected estimators for nonlinear panel data models with both time invariant and time varying heterogeneity. These include limited dependent variable models with both unobserved individual effects and endogenous explanatory variables, and sample selection models with unobserved individual effects. Our twostep approach first estimates the reduced form by fixed effects procedures to obtain estimates of the time variant heterogeneity underlying the endogeneity/selection bias. We then estimate the primary equation by fixed effects including an appropriately constructed control function from the reduced form estimates as an additional explanatory variable. The fixed effects approach in this second step captures the time invariant heterogeneity while the control function accounts for the time varying heterogeneity. Since either or both steps might employ nonlinear fixed effects procedures it is necessary to bias adjust the estimates due to the incidental parameters problem. This problem is exacerbated by the two step nature of the procedure. As these two step approaches are not covered in the existing literature we derive the appropriate correction thereby extending the use of largeT bias adjustments to an important class of models. Simulation evidence indicates our approach works well in finite samples and an empirical example illustrates the applicability of our estimator. JEL Classification: C23; J31; J51.