Florian M Hollenbach
Biographic Data
| ID | 4122552 |
|---|---|
| NAME | Florian M Hollenbach |
| GIVEN NAMES | Florian M |
| FAMILY NAME | Hollenbach |
| SIGNATURE | HOLLENBACH F M |
| AFFILIATIONS | Texas A&M University |
| ORCID | 0000-0002-9599-556X |
| VERIFIED | Yes |
| TOTAL WORKS | 13 |
| TOTAL CITATIONS | 254 |
| WORKS AS AUTHOR | 13 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2012 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 6 |
Capital Mobility and Taxation
Do more mobile firms pay lower taxes? Conventional wisdom argues that capital mobility creates downward pressure on corporate taxes, as firms can threaten to exit. Nevertheless, empirical findings are highly mixed and hard to reconcile, partly due to a lack of data at the microlevel. Using two comprehensive panel data sets with more than 780,000 Chinese firms over two decades, we find that firms with higher shares of mobile capital pay higher eff…
Bias from Network Misspecification Under Spatial Dependence
The prespecification of the network is one of the biggest hurdles for applied researchers in undertaking spatial analysis. In this letter, we demonstrate two results. First, we derive bounds for the bias in nonspatial models with omitted spatially-lagged predictors or outcomes. These bias expressions can be obtained without prior knowledge of the network, and are more informative than familiar omitted variable bias formulas. Second, we derive bou…
Multiple Imputation Using Gaussian Copulas
Missing observations are pervasive throughout empirical research, especially in the social sciences. Despite multiple approaches to dealing adequately with missing data, many scholars still fail to address this vital issue. In this article, we present a simple-to-use method for generating multiple imputations (MIs) using a Gaussian copula. The Gaussian copula for MI allows scholars to attain estimation results that have good coverage and small bi…
Spatial interdependence and instrumental variable models
Instrumental variable (IV) methods are widely used to address endogeneity concerns. Yet, a specific kind of endogeneity – spatial interdependence – is regularly ignored. We show that ignoring spatial interdependence in the outcome results in asymptotically biased estimates even when instruments are randomly assigned. The extent of this bias increases when the instrument is also spatially clustered, as is the case for many widely used instruments:…
Bayesian Versus Maximum Likelihood Estimation of Treatment Effects in Bivariate Probit Instrumental Variable Models
Bivariate probit models are a common choice for scholars wishing to estimate causal effects in instrumental variable models where both the treatment and outcome are binary. However, standard maximum likelihood approaches for estimating bivariate probit models are problematic. Numerical routines in popular software suites frequently generate inaccurate parameter estimates and even estimated correctly, maximum likelihood routines provide no straigh…
Fiscal Capacity and Inequality
We argue that wealthy elites in democracies can limit their taxes by constraining the fiscal capacity of the state. Corrupting local officials and undermining fiscal capacity are some of the mechanisms by which high-income earners can lower their own tax liabilities, even when voters favor higher de jure levels of taxation. The incentive to undermine fiscal capacity is especially compelling when inequality is high, as the median voter is likely t…
On the Use and Abuse of Spatial Instruments
Instruments based on realizations of the endogenous variable in other units—for instance, regional or global weighted averages—are commonly used in political science. Such spatial instruments have proved attractive: they are convenient to obtain, typically have power, and are plausibly exogenous. We argue that the assumptions underlying spatial instruments remain poorly understood and challenge whether spatial instruments can satisfy the conditio…
A re-assessment of reporting bias in event-based violence data with respect to cell phone coverage
This paper discusses the issue of possible reporting bias in media-based violent-event data and its relation to the role of communication technology in fostering collective action. We expand the work of Weidmann (2016), presenting several sensitivity analyses to determine the degree to which reporting bias may confound the relationship between communication technology and violence in a recent study that relies on event data for Africa. We find no…
Cabinet Formation and Portfolio Distribution in European Multiparty Systems
Government formation in multiparty systems is of self-evident substantive importance, and the subject of an enormous theoretical literature. Empirical evaluations of models of government formation tend to separate government formation per se from the distribution of key government pay-offs, such as cabinet portfolios, between members of the resulting government. Models of government formation are necessarily specified ex ante , absent any knowled…
Aggregation and Ensembles
Perhaps the major story in forecasting the 2012 election is the growing awareness of the benefits of aggregating multiple sources of data for improving prediction. Most prominently, polling analysts including Simon Jackman, Drew Linzer, and Nate Silver made the strong case, ultimately validated by the election results, that combining information from multiple polls gives a better picture of the electorate than any poll analyzed in isolation
Technology and Collective Action
The spread of cell phone technology across Africa has transforming effects on the economic and political sphere of the continent. In this paper, we investigate the impact of cell phone technology on violent collective action. We contend that the availability of cell phones as a communication technology allows political groups to overcome collective action problems more easily and improve in-group cooperation, and coordination. Utilizing novel, sp…
Improving Predictions using Ensemble Bayesian Model Averaging
We present ensemble Bayesian model averaging (EBMA) and illustrate its ability to aid scholars in the social sciences to make more accurate forecasts of future events. In essence, EBMA improves prediction by pooling information from multiple forecast models to generate ensemble predictions similar to a weighted average of component forecasts. The weight assigned to each forecast is calibrated via its performance in some validation period. The aim…
Ensemble Predictions of the 2012 US Presidential Election
For more than two decades, political scientists have created statistical models aimed at generating out-of-sample predictions of presidential elections. In 2004 and 2008, PS: Political Science and Politics published symposia of the various forecasting models prior to Election Day. This exercise serves to validate models based on accuracy by garnering additional support for those that most accurately foretell the ultimate election outcome. Implici…
Technology and Collective Action
The spread of cell phone technology across Africa has transforming effects on the economic and political sphere of the continent. In this paper, we investigate the impact of cell phone technology on violent collective action. We contend that the availability of cell phones as a communication technology allows political groups to overcome collective action problems more easily and improve in-group cooperation, and coordination. Utilizing novel, sp…
Improving Predictions using Ensemble Bayesian Model Averaging
We present ensemble Bayesian model averaging (EBMA) and illustrate its ability to aid scholars in the social sciences to make more accurate forecasts of future events. In essence, EBMA improves prediction by pooling information from multiple forecast models to generate ensemble predictions similar to a weighted average of component forecasts. The weight assigned to each forecast is calibrated via its performance in some validation period. The aim…
On the Use and Abuse of Spatial Instruments
Instruments based on realizations of the endogenous variable in other units—for instance, regional or global weighted averages—are commonly used in political science. Such spatial instruments have proved attractive: they are convenient to obtain, typically have power, and are plausibly exogenous. We argue that the assumptions underlying spatial instruments remain poorly understood and challenge whether spatial instruments can satisfy the conditio…
Spatial interdependence and instrumental variable models
Instrumental variable (IV) methods are widely used to address endogeneity concerns. Yet, a specific kind of endogeneity – spatial interdependence – is regularly ignored. We show that ignoring spatial interdependence in the outcome results in asymptotically biased estimates even when instruments are randomly assigned. The extent of this bias increases when the instrument is also spatially clustered, as is the case for many widely used instruments:…
Fiscal Capacity and Inequality
We argue that wealthy elites in democracies can limit their taxes by constraining the fiscal capacity of the state. Corrupting local officials and undermining fiscal capacity are some of the mechanisms by which high-income earners can lower their own tax liabilities, even when voters favor higher de jure levels of taxation. The incentive to undermine fiscal capacity is especially compelling when inequality is high, as the median voter is likely t…
Cabinet Formation and Portfolio Distribution in European Multiparty Systems
Government formation in multiparty systems is of self-evident substantive importance, and the subject of an enormous theoretical literature. Empirical evaluations of models of government formation tend to separate government formation per se from the distribution of key government pay-offs, such as cabinet portfolios, between members of the resulting government. Models of government formation are necessarily specified ex ante , absent any knowled…
Multiple Imputation Using Gaussian Copulas
Missing observations are pervasive throughout empirical research, especially in the social sciences. Despite multiple approaches to dealing adequately with missing data, many scholars still fail to address this vital issue. In this article, we present a simple-to-use method for generating multiple imputations (MIs) using a Gaussian copula. The Gaussian copula for MI allows scholars to attain estimation results that have good coverage and small bi…
A re-assessment of reporting bias in event-based violence data with respect to cell phone coverage
This paper discusses the issue of possible reporting bias in media-based violent-event data and its relation to the role of communication technology in fostering collective action. We expand the work of Weidmann (2016), presenting several sensitivity analyses to determine the degree to which reporting bias may confound the relationship between communication technology and violence in a recent study that relies on event data for Africa. We find no…
Capital Mobility and Taxation
Do more mobile firms pay lower taxes? Conventional wisdom argues that capital mobility creates downward pressure on corporate taxes, as firms can threaten to exit. Nevertheless, empirical findings are highly mixed and hard to reconcile, partly due to a lack of data at the microlevel. Using two comprehensive panel data sets with more than 780,000 Chinese firms over two decades, we find that firms with higher shares of mobile capital pay higher eff…
Bias from Network Misspecification Under Spatial Dependence
The prespecification of the network is one of the biggest hurdles for applied researchers in undertaking spatial analysis. In this letter, we demonstrate two results. First, we derive bounds for the bias in nonspatial models with omitted spatially-lagged predictors or outcomes. These bias expressions can be obtained without prior knowledge of the network, and are more informative than familiar omitted variable bias formulas. Second, we derive bou…
Bayesian Versus Maximum Likelihood Estimation of Treatment Effects in Bivariate Probit Instrumental Variable Models
Bivariate probit models are a common choice for scholars wishing to estimate causal effects in instrumental variable models where both the treatment and outcome are binary. However, standard maximum likelihood approaches for estimating bivariate probit models are problematic. Numerical routines in popular software suites frequently generate inaccurate parameter estimates and even estimated correctly, maximum likelihood routines provide no straigh…
Ensemble Predictions of the 2012 US Presidential Election
For more than two decades, political scientists have created statistical models aimed at generating out-of-sample predictions of presidential elections. In 2004 and 2008, PS: Political Science and Politics published symposia of the various forecasting models prior to Election Day. This exercise serves to validate models based on accuracy by garnering additional support for those that most accurately foretell the ultimate election outcome. Implici…
Improving Predictions using Ensemble Bayesian Model Averaging
We present ensemble Bayesian model averaging (EBMA) and illustrate its ability to aid scholars in the social sciences to make more accurate forecasts of future events. In essence, EBMA improves prediction by pooling information from multiple forecast models to generate ensemble predictions similar to a weighted average of component forecasts. The weight assigned to each forecast is calibrated via its performance in some validation period. The aim…
Ensemble Predictions of the 2012 US Presidential Election
For more than two decades, political scientists have created statistical models aimed at generating out-of-sample predictions of presidential elections. In 2004 and 2008, PS: Political Science and Politics published symposia of the various forecasting models prior to Election Day. This exercise serves to validate models based on accuracy by garnering additional support for those that most accurately foretell the ultimate election outcome. Implici…
Aggregation and Ensembles
Perhaps the major story in forecasting the 2012 election is the growing awareness of the benefits of aggregating multiple sources of data for improving prediction. Most prominently, polling analysts including Simon Jackman, Drew Linzer, and Nate Silver made the strong case, ultimately validated by the election results, that combining information from multiple polls gives a better picture of the electorate than any poll analyzed in isolation
Technology and Collective Action
The spread of cell phone technology across Africa has transforming effects on the economic and political sphere of the continent. In this paper, we investigate the impact of cell phone technology on violent collective action. We contend that the availability of cell phones as a communication technology allows political groups to overcome collective action problems more easily and improve in-group cooperation, and coordination. Utilizing novel, sp…
Cabinet Formation and Portfolio Distribution in European Multiparty Systems
Government formation in multiparty systems is of self-evident substantive importance, and the subject of an enormous theoretical literature. Empirical evaluations of models of government formation tend to separate government formation per se from the distribution of key government pay-offs, such as cabinet portfolios, between members of the resulting government. Models of government formation are necessarily specified ex ante , absent any knowled…
A re-assessment of reporting bias in event-based violence data with respect to cell phone coverage
This paper discusses the issue of possible reporting bias in media-based violent-event data and its relation to the role of communication technology in fostering collective action. We expand the work of Weidmann (2016), presenting several sensitivity analyses to determine the degree to which reporting bias may confound the relationship between communication technology and violence in a recent study that relies on event data for Africa. We find no…
On the Use and Abuse of Spatial Instruments
Instruments based on realizations of the endogenous variable in other units—for instance, regional or global weighted averages—are commonly used in political science. Such spatial instruments have proved attractive: they are convenient to obtain, typically have power, and are plausibly exogenous. We argue that the assumptions underlying spatial instruments remain poorly understood and challenge whether spatial instruments can satisfy the conditio…
Bayesian Versus Maximum Likelihood Estimation of Treatment Effects in Bivariate Probit Instrumental Variable Models
Bivariate probit models are a common choice for scholars wishing to estimate causal effects in instrumental variable models where both the treatment and outcome are binary. However, standard maximum likelihood approaches for estimating bivariate probit models are problematic. Numerical routines in popular software suites frequently generate inaccurate parameter estimates and even estimated correctly, maximum likelihood routines provide no straigh…
Fiscal Capacity and Inequality
We argue that wealthy elites in democracies can limit their taxes by constraining the fiscal capacity of the state. Corrupting local officials and undermining fiscal capacity are some of the mechanisms by which high-income earners can lower their own tax liabilities, even when voters favor higher de jure levels of taxation. The incentive to undermine fiscal capacity is especially compelling when inequality is high, as the median voter is likely t…
Spatial interdependence and instrumental variable models
Instrumental variable (IV) methods are widely used to address endogeneity concerns. Yet, a specific kind of endogeneity – spatial interdependence – is regularly ignored. We show that ignoring spatial interdependence in the outcome results in asymptotically biased estimates even when instruments are randomly assigned. The extent of this bias increases when the instrument is also spatially clustered, as is the case for many widely used instruments:…
Bias from Network Misspecification Under Spatial Dependence
The prespecification of the network is one of the biggest hurdles for applied researchers in undertaking spatial analysis. In this letter, we demonstrate two results. First, we derive bounds for the bias in nonspatial models with omitted spatially-lagged predictors or outcomes. These bias expressions can be obtained without prior knowledge of the network, and are more informative than familiar omitted variable bias formulas. Second, we derive bou…
Multiple Imputation Using Gaussian Copulas
Missing observations are pervasive throughout empirical research, especially in the social sciences. Despite multiple approaches to dealing adequately with missing data, many scholars still fail to address this vital issue. In this article, we present a simple-to-use method for generating multiple imputations (MIs) using a Gaussian copula. The Gaussian copula for MI allows scholars to attain estimation results that have good coverage and small bi…
Capital Mobility and Taxation
Do more mobile firms pay lower taxes? Conventional wisdom argues that capital mobility creates downward pressure on corporate taxes, as firms can threaten to exit. Nevertheless, empirical findings are highly mixed and hard to reconcile, partly due to a lack of data at the microlevel. Using two comprehensive panel data sets with more than 780,000 Chinese firms over two decades, we find that firms with higher shares of mobile capital pay higher eff…
Computer Science (10 works) · Econometrics (9 works) · Economics (8 works) · Mathematics (8 works) · Political science (7 works) · Statistics (5 works) · Fiscal Policy and Economic Growth (4 works) · Instrumental variable (4 works) · Variable (mathematics (4 works) · Data science (3 works)