Suchergebnisse

Valid inference in an observational study requires a correct control specification, but a correct specification is never known. I introduce a method that constructs a control vector from the observed data that, when included in a linear regression, adjusts for several forms of bias. These include nonlinearities and interactions in the background covariates, biases induced by heterogeneous treatment effects, and specific forms of interference. The first is new to political science; the latter two are original contributions. I incorporate random effects, a set of diagnostics, and robust standard errors. With additional assumptions, the estimates allow for causal inference on both binary and continuous treatment variables. In total, the model provides a flexible means to adjust for biases commonly encountered in our data, makes minimal assumptions, returns efficient estimates, and can be implemented through publicly available software.

We introduce a Bayesian method, LASSOplus, that unifies recent contributions in the sparse modeling literatures, while substantially extending pre-existing estimators in terms of both performance and flexibility. Unlike existing Bayesian variable selection methods, LASSOplus both selects and estimates effects while returning estimated confidence intervals for discovered effects. Furthermore, we show how LASSOplus easily extends to modeling repeated observations and permits a simple Bonferroni correction to control coverage on confidence intervals among discovered effects. We situate LASSOplus in the literature on how to estimate subgroup effects, a topic that often leads to a proliferation of estimation parameters. We also offer a simple preprocessing step that draws on recent theoretical work to estimate higher-order effects that can be interpreted independently of their lower-order terms. A simulation study illustrates the method's performance relative to several existing variable selection methods. In addition, we apply LASSOplus to an existing study on public support for climate treaties to illustrate the method's ability to discover substantive and relevant effects. Software implementing the method is publicly available in theRpackagesparsereg.

We present a model of political networks that integrates both the choice of trade partners (the extensive margin) and trade volumes (the intensive margin). Our model predicts that regimes secure in their survival, including democracies as well as some consolidated authoritarian regimes, will trade more on the extensive margin than vulnerable autocracies, which will block trade in products that would expand interpersonal contact among their citizens. We apply a two-stage Bayesian LASSO estimator to detailed measures of institutional features and highly disaggregated product-level trade data encompassing 131 countries over a half century. Consistent with our model, we find that (a) political institutions matter for the extensive margin of trade but not for the intensive margin and (b) the effects of political institutions on the extensive margin of trade vary across products, falling most heavily on those goods that involve extensive interpersonal contact.

AbstractWe present a model of political networks that integrates both the choice of trade partners (the extensive margin) and trade volumes (the intensive margin). Our model predicts that regimes secure in their survival, including democracies as well as some consolidated authoritarian regimes, will trade more on the extensive margin than vulnerable autocracies, which will block trade in products that would expand interpersonal contact among their citizens. We apply a two-stage Bayesian LASSO estimator to detailed measures of institutional features and highly disaggregated product-level trade data encompassing 131 countries over a half century. Consistent with our model, we find that (a) political institutions matter for the extensive margin of trade but not for the intensive margin and (b) the effects of political institutions on the extensive margin of trade vary across products, falling most heavily on those goods that involve extensive interpersonal contact.

We introduce a model that extends the standard vote choice model to encompass text. In our model, votes and speech are generated from a common set of underlying preference parameters. We estimate the parameters with a sparse Gaussian copula factor model that estimates the number of latent dimensions, is robust to outliers, and accounts for zero inflation in the data. To illustrate its workings, we apply our estimator to roll call votes and floor speech from recent sessions of the US Senate. We uncover two stable dimensions: one ideological and the other reflecting to Senators' leadership roles. We then show how the method can leverage common speech in order to impute missing data, recovering reliable preference estimates for rank-and-file Senators given only leadership votes.

Many social processes are stable and smooth in general, with discrete jumps. We develop a sequential segmentation spline method that can identify both the location and the number of discontinuities in a series of observations with a time component, while fitting a smooth spline between jumps, using a modified Bayesian Information Criterion statistic as a stopping rule. We explore the method in a large-n, unbalanced panel setting with George W. Bush's approval data, a small-ntime series with median DW-NOMINATE scores for each Congress over time, and a series of simulations. We compare the method to several extant smoothers, and the method performs favorably in terms of visual inspection, residual properties, and event detection. Finally, we discuss extensions of the method.

AbstractWe introduce a method for scaling two datasets from different sources. The proposed method estimates a latent factor common to both datasets as well as an idiosyncratic factor unique to each. In addition, it offers a flexible modeling strategy that permits the scaled locations to be a function of covariates, and efficient implementation allows for inference through resampling. A simulation study shows that our proposed method improves over existing alternatives in capturing the variation common to both datasets, as well as the latent factors specific to each. We apply our proposed method to vote and speech data from the 112th U.S. Senate. We recover a shared subspace that aligns with a standard ideological dimension running from liberals to conservatives, while recovering the words most associated with each senator's location. In addition, we estimate a word-specific subspace that ranges from national security to budget concerns, and a vote-specific subspace with Tea Party senators on one extreme and senior committee leaders on the other.