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Papers by Robert J Franzese
American Political Science Review, Nov 3, 2022
SAGE Publications Ltd eBooks, 2020
1. Introduction 2. The democratic commitment to social insurance 3. Financing the commitments - p... more 1. Introduction 2. The democratic commitment to social insurance 3. Financing the commitments - public debt 4. Monetary management of the macroeconomy 5. Comparative democrative political-economy and macroeconomic-policymaking.
Oxford Research Encyclopedia of Politics, Apr 26, 2019
RePEc: Research Papers in Economics, 1997
SAGE Publications Ltd eBooks, 2020
Political Science Research and Methods, 2018
Most agree that models of binary time-series-cross-sectional data in political science often poss... more Most agree that models of binary time-series-cross-sectional data in political science often possess unobserved unit-level heterogeneity. Despite this, there is no clear consensus on how best to account for these potential unit effects, with many of the issues confronted seemingly misunderstood. For example, one oft-discussed concern with rare events data is the elimination of no-event units from the sample when estimating fixed effects models. Many argue that this is a reason to eschew fixed effects in favor of pooled or random effects models. We revisit this issue and clarify that the main concern with fixed effects models of rare events data is not inaccurate or inefficient coefficient estimation, but instead biased marginal effects. In short, only evaluating event-experiencing units gives an inaccurate estimate of the baseline risk, yielding inaccurate (often inflated) estimates of predictor effects. As a solution, we propose a penalized maximum likelihood fixed effects (PML-FE)...
Political Science Quarterly, 2003
Social Science Research Network, 2004
Social Science Research Network, 2008
Social Science Research Network, 2012
ABSTRACT One of the central challenges to inference in the context of potentially interdependent ... more ABSTRACT One of the central challenges to inference in the context of potentially interdependent observations, known as Galton's Problem, is the difficulty distinguishing spatially correlated observations due to observed units exposure to spatially correlated shocks from spatial correlation in outcomes due to contagion (spillovers) between units. The applied researcher's first, and to date only, defense against confusing these substantively importantly different processes empirically has been to control as best possible with observable regressors and/or fixed effects for correlated-shocks processes when estimating contagion (spatial-autoregression). While specifying empirical models & measures as precisely and powerfully as possible remains as always optimal practice, these extant strategies cannot guard fully against the possibility of exposure to 'unobserved' exogenous shocks that are distributed spatially in manner not fully common to some set of units (fixed effects) or fully controlled by observable exogenous factors (control variables), but rather distributed across units more similarly to the pattern by which the outcome is contagious. Following the robust Lagrange-multiplier test strategy of Anselin, Bera, Florax, & Yoon (1996), which offered tests of spatial-autoregressive lag or of error against independence, robust to the presence of the other autoregressive process, we derive and evaluate the performance of a robust Lagrange-multiplier test for spatial-autoregression (contagion) against independence, which is robust to the presence of unobserved (uncontrolled/unmodeled) correlated-shocks distributed across units identically to the pattern of contagion (along with the symmetric robust test for spatially correlated shocks robust to autoregressive contagion). The test results are constructive and can be highly informative in offering direct & more-definitive answer than heretofore possible to the question posed by Galton's Problem, common shocks or contagion?.
Sage eBooks, 2015
VOLUME ONE Part One: Introduction: The Challenges of and an Approach to Empirical Analysis in Soc... more VOLUME ONE Part One: Introduction: The Challenges of and an Approach to Empirical Analysis in Social Science Multicausality, Context-Conditionality, and Endogeneity - Robert Franzese Puzzles, Proverbs, and Omega Matrices: The Scientific and Social Significance of Empirical Implications of Theoretical Models (EITM) - Jim Granato and Frank Scioli Statistical Backwards Induction: A Simple Method for Estimating Recursive Strategic Models - Muhammet Ali Bas, Curtis Signorino and Robert Walker Part Two: Measurement 2a. Measurement & Measurement Error, Missing Data: Toward Theories of Data: The State of Political Methodology - Christopher Achen Measurement - Simon Jackman Measurement Error across Disciplines - Robert Groves Enhancing the Validity and Cross-Cultural Comparability of Measurement in Survey Research - Gary King et al. Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation - Gary King et al. 2b. Measurement Applications: Extract from Congress: A Political-Economic History of Roll Call Voting - Keith Poole and Howard Rosenthal Democracy as a Latent Variable - Simon Jackman and Shawn Treier Dynamic Representation - James Stimson, Michael MacKuen and Robert Erikson VOLUME TWO Part Three: The Foundational Multivariate-Regression Model and Models for Limited & Qualitative Dependent Variables 3a. Use & Interpretation of Multivariate-Regression Models: Elementary Regression Theory and Social Science Practice - Christopher Achen 3b. Use & Interpretation of Limited & Qualitative Dependent-Variable Models: Extracts from Unifying Political Methodology - Gary King Extracts from Generalized Linear Models - Jeff Gill Making the Most of Statistical Analyses: Improving Interpretation and Presentation - Gary King, Michael Tomz and Jason Wittenberg 3c. Estimation and Inference in the Bayesian Paradigm: Single-Parameter Models - Jeff Gill Pooling Disparate Observations - Larry Bartels Estimation and Inference Are Missing Data Problems: Unifying Social Science Statistics via Bayesian Simulation - Simon Jackman Part Four: Heterogeneity and Heterogeneous Effects 4a. Unit & Period "Fixed Effects": Dirty Pool - Donald Green, Soo Yeon Kim and David Yoon Throwing Out the Baby with the Bath Water: A Comment on Green, Kim, and Yoon - Nathaniel Beck and Jonathan Katz Problematic Choices: Testing for Correlated Unit Specific Effects in Panel Data - Vera Troeger VOLUME THREE 4b. Interaction & Nonlinear Models: Theory to Practice - Cindy Kam and Robert Franzese Multiple Hands on the Wheel: Empirically Modeling Partial Delegation and Shared Control of Monetary Policy in the Open and Institutionalized Economy - Robert Franzese 4c. Random-Coefficient/Hierarchical/Multilevel Models: Causal Heterogeneity in Comparative Research: A Bayesian Hierarchical Modelling Approach - Bruce Western Modeling Multilevel Data Structures - Marco Steenbergen and Bradford Jones Bayesian Multilevel Estimation with Poststratification: State-Level Estimates from National Polls - David Park, Andrew Gelman and Joseph Bafumi Part Five: Dynamic Models Selections 5a. Models for Temporal Dependence: Comparing Dynamic Specifications: The Case of Presidential Approval - Nathaniel Beck Taking Time Seriously - Suzanna De Boef and Luke Keele Taking Time Seriously: Time-Series-Cross-Section Analysis with a Binary Dependent Variable - Nathaniel Beck, Jonathan Katz and Richard Tucker Back to the Future: Modeling Time Dependence in Binary Data - David Carter and Curtis Signorino Time Is of the Essence: Event History Models in Political Science - Janet Box-Steffensmeier and Bradford Jones VOLUME FOUR 5b. Models for Cross-UnitInterdependence: Empirical Models of Spatial Interdependence - Robert Franzese and Jude Hays Network Analysis and Political Science - Michael Ward, Katherine Stovel and Audrey Sacks Inferential Network Analysis with Exponential Random Graph Models - Skyler Cranmer and Bruce Desmarais Spatial- and Spatiotemporal-Autoregressive Probit Models of Interdependent Binary Outcomes - Robert Franzese, Jude Hays and Scott Cook 5c. Models for Time-Series-Cross-Section and Panel Data: Regression in Space and Time: A Statistical Essay - James Stimson Estimating Dynamic Panel Data Models in Political Science - Gregory Wawro Modeling Dynamics in Time-Series-Cross-Section Political Economy Data - Nathaniel Beck and Jonathan Katz Beyond Fixed versus Random Effects: A Framework for Improving Substantive and Statistical Analysis of Panel, Time-Series Cross-Sectional, and Multilevel Data - Brandon Bartels Part Six: Endogeneity and Causal Inference Selections 6a. Instrumental-Variables Methods: Instrumental and `Quasi-Instrumental' Variables - Larry Bartels Instrumental Variables Estimation in Political Science: A Readers' Guide - Allison Sovey and Donald Green Model Specification in Instrumental-Variables Regression - Thad Dunning VOLUME FIVE 6b. Full-Information Maximum-Likelihood (FIML) Methods: Endogeneity and Structural Equation…
Kluwer Academic Publishers eBooks, 2004
... Wage Bargaining Systems in the Single European Currency Area-David Soskice and Torben Iversen... more ... Wage Bargaining Systems in the Single European Currency Area-David Soskice and Torben Iversen 107-129 6. Strategic Wage Setting in a Monetary Union-Lila Cavillari 131-145 7. Interest Groups, Enlargement of the EMU and Labor Market Reform-Michael Neugart 147-164 ...
American Political Science Review, Nov 3, 2022
SAGE Publications Ltd eBooks, 2020
1. Introduction 2. The democratic commitment to social insurance 3. Financing the commitments - p... more 1. Introduction 2. The democratic commitment to social insurance 3. Financing the commitments - public debt 4. Monetary management of the macroeconomy 5. Comparative democrative political-economy and macroeconomic-policymaking.
Oxford Research Encyclopedia of Politics, Apr 26, 2019
RePEc: Research Papers in Economics, 1997
SAGE Publications Ltd eBooks, 2020
Political Science Research and Methods, 2018
Most agree that models of binary time-series-cross-sectional data in political science often poss... more Most agree that models of binary time-series-cross-sectional data in political science often possess unobserved unit-level heterogeneity. Despite this, there is no clear consensus on how best to account for these potential unit effects, with many of the issues confronted seemingly misunderstood. For example, one oft-discussed concern with rare events data is the elimination of no-event units from the sample when estimating fixed effects models. Many argue that this is a reason to eschew fixed effects in favor of pooled or random effects models. We revisit this issue and clarify that the main concern with fixed effects models of rare events data is not inaccurate or inefficient coefficient estimation, but instead biased marginal effects. In short, only evaluating event-experiencing units gives an inaccurate estimate of the baseline risk, yielding inaccurate (often inflated) estimates of predictor effects. As a solution, we propose a penalized maximum likelihood fixed effects (PML-FE)...
Political Science Quarterly, 2003
Social Science Research Network, 2004
Social Science Research Network, 2008
Social Science Research Network, 2012
ABSTRACT One of the central challenges to inference in the context of potentially interdependent ... more ABSTRACT One of the central challenges to inference in the context of potentially interdependent observations, known as Galton's Problem, is the difficulty distinguishing spatially correlated observations due to observed units exposure to spatially correlated shocks from spatial correlation in outcomes due to contagion (spillovers) between units. The applied researcher's first, and to date only, defense against confusing these substantively importantly different processes empirically has been to control as best possible with observable regressors and/or fixed effects for correlated-shocks processes when estimating contagion (spatial-autoregression). While specifying empirical models & measures as precisely and powerfully as possible remains as always optimal practice, these extant strategies cannot guard fully against the possibility of exposure to 'unobserved' exogenous shocks that are distributed spatially in manner not fully common to some set of units (fixed effects) or fully controlled by observable exogenous factors (control variables), but rather distributed across units more similarly to the pattern by which the outcome is contagious. Following the robust Lagrange-multiplier test strategy of Anselin, Bera, Florax, & Yoon (1996), which offered tests of spatial-autoregressive lag or of error against independence, robust to the presence of the other autoregressive process, we derive and evaluate the performance of a robust Lagrange-multiplier test for spatial-autoregression (contagion) against independence, which is robust to the presence of unobserved (uncontrolled/unmodeled) correlated-shocks distributed across units identically to the pattern of contagion (along with the symmetric robust test for spatially correlated shocks robust to autoregressive contagion). The test results are constructive and can be highly informative in offering direct & more-definitive answer than heretofore possible to the question posed by Galton's Problem, common shocks or contagion?.
Sage eBooks, 2015
VOLUME ONE Part One: Introduction: The Challenges of and an Approach to Empirical Analysis in Soc... more VOLUME ONE Part One: Introduction: The Challenges of and an Approach to Empirical Analysis in Social Science Multicausality, Context-Conditionality, and Endogeneity - Robert Franzese Puzzles, Proverbs, and Omega Matrices: The Scientific and Social Significance of Empirical Implications of Theoretical Models (EITM) - Jim Granato and Frank Scioli Statistical Backwards Induction: A Simple Method for Estimating Recursive Strategic Models - Muhammet Ali Bas, Curtis Signorino and Robert Walker Part Two: Measurement 2a. Measurement & Measurement Error, Missing Data: Toward Theories of Data: The State of Political Methodology - Christopher Achen Measurement - Simon Jackman Measurement Error across Disciplines - Robert Groves Enhancing the Validity and Cross-Cultural Comparability of Measurement in Survey Research - Gary King et al. Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation - Gary King et al. 2b. Measurement Applications: Extract from Congress: A Political-Economic History of Roll Call Voting - Keith Poole and Howard Rosenthal Democracy as a Latent Variable - Simon Jackman and Shawn Treier Dynamic Representation - James Stimson, Michael MacKuen and Robert Erikson VOLUME TWO Part Three: The Foundational Multivariate-Regression Model and Models for Limited & Qualitative Dependent Variables 3a. Use & Interpretation of Multivariate-Regression Models: Elementary Regression Theory and Social Science Practice - Christopher Achen 3b. Use & Interpretation of Limited & Qualitative Dependent-Variable Models: Extracts from Unifying Political Methodology - Gary King Extracts from Generalized Linear Models - Jeff Gill Making the Most of Statistical Analyses: Improving Interpretation and Presentation - Gary King, Michael Tomz and Jason Wittenberg 3c. Estimation and Inference in the Bayesian Paradigm: Single-Parameter Models - Jeff Gill Pooling Disparate Observations - Larry Bartels Estimation and Inference Are Missing Data Problems: Unifying Social Science Statistics via Bayesian Simulation - Simon Jackman Part Four: Heterogeneity and Heterogeneous Effects 4a. Unit & Period "Fixed Effects": Dirty Pool - Donald Green, Soo Yeon Kim and David Yoon Throwing Out the Baby with the Bath Water: A Comment on Green, Kim, and Yoon - Nathaniel Beck and Jonathan Katz Problematic Choices: Testing for Correlated Unit Specific Effects in Panel Data - Vera Troeger VOLUME THREE 4b. Interaction & Nonlinear Models: Theory to Practice - Cindy Kam and Robert Franzese Multiple Hands on the Wheel: Empirically Modeling Partial Delegation and Shared Control of Monetary Policy in the Open and Institutionalized Economy - Robert Franzese 4c. Random-Coefficient/Hierarchical/Multilevel Models: Causal Heterogeneity in Comparative Research: A Bayesian Hierarchical Modelling Approach - Bruce Western Modeling Multilevel Data Structures - Marco Steenbergen and Bradford Jones Bayesian Multilevel Estimation with Poststratification: State-Level Estimates from National Polls - David Park, Andrew Gelman and Joseph Bafumi Part Five: Dynamic Models Selections 5a. Models for Temporal Dependence: Comparing Dynamic Specifications: The Case of Presidential Approval - Nathaniel Beck Taking Time Seriously - Suzanna De Boef and Luke Keele Taking Time Seriously: Time-Series-Cross-Section Analysis with a Binary Dependent Variable - Nathaniel Beck, Jonathan Katz and Richard Tucker Back to the Future: Modeling Time Dependence in Binary Data - David Carter and Curtis Signorino Time Is of the Essence: Event History Models in Political Science - Janet Box-Steffensmeier and Bradford Jones VOLUME FOUR 5b. Models for Cross-UnitInterdependence: Empirical Models of Spatial Interdependence - Robert Franzese and Jude Hays Network Analysis and Political Science - Michael Ward, Katherine Stovel and Audrey Sacks Inferential Network Analysis with Exponential Random Graph Models - Skyler Cranmer and Bruce Desmarais Spatial- and Spatiotemporal-Autoregressive Probit Models of Interdependent Binary Outcomes - Robert Franzese, Jude Hays and Scott Cook 5c. Models for Time-Series-Cross-Section and Panel Data: Regression in Space and Time: A Statistical Essay - James Stimson Estimating Dynamic Panel Data Models in Political Science - Gregory Wawro Modeling Dynamics in Time-Series-Cross-Section Political Economy Data - Nathaniel Beck and Jonathan Katz Beyond Fixed versus Random Effects: A Framework for Improving Substantive and Statistical Analysis of Panel, Time-Series Cross-Sectional, and Multilevel Data - Brandon Bartels Part Six: Endogeneity and Causal Inference Selections 6a. Instrumental-Variables Methods: Instrumental and `Quasi-Instrumental' Variables - Larry Bartels Instrumental Variables Estimation in Political Science: A Readers' Guide - Allison Sovey and Donald Green Model Specification in Instrumental-Variables Regression - Thad Dunning VOLUME FIVE 6b. Full-Information Maximum-Likelihood (FIML) Methods: Endogeneity and Structural Equation…
Kluwer Academic Publishers eBooks, 2004
... Wage Bargaining Systems in the Single European Currency Area-David Soskice and Torben Iversen... more ... Wage Bargaining Systems in the Single European Currency Area-David Soskice and Torben Iversen 107-129 6. Strategic Wage Setting in a Monetary Union-Lila Cavillari 131-145 7. Interest Groups, Enlargement of the EMU and Labor Market Reform-Michael Neugart 147-164 ...