Valuing Policy Alternatives: A Generalization

2003 ◽  
Vol 11 (4) ◽  
pp. 397-418 ◽  
Author(s):  
Nathan Dietz ◽  
Lawrence S. Rothenberg
Keyword(s):  
Political Science ◽  
Predictive Accuracy ◽  
Ideal Point ◽  
Standard Errors ◽  
Parameter Estimates ◽  
Policy Alternatives ◽  
Point Estimates ◽  
Utility Models ◽  
The Impact ◽  
Consistent Parameter

Those interested in political phenomena such as voting have found random utility models, originally developed for decisions such as transportation choice, especially attractive, as the underlying model can yield a statistical model with a few simple, realistic assumptions. Unfortunately, such models have proven difficult to apply to situations with more than two votes and three alternatives or an unknown cutpoint. Additionally, as we show, standard applications of such models to voting, while producing consistent parameter estimates, yield standard errors that are too small and, due to a failure to employ all relevant theoretical information, biased ideal point estimates. We specify a general model applicable to any number of votes and alternatives, with correct standard errors and unbiased ideal point estimates. We apply this model to a number of cases studied by previous scholars involving legislative voting over the minimum wage: (1) when there are two votes and two known cutpoints (K. Krehbiel and D. Rivers, American Journal of Political Science, 1988, 32, 1151–1174); (2) when there are three votes and three known cutpoints (J. Wilkerson, American Journal of Political Science, 1991, 35, 613–623); and (3) when there are three votes but where one cutpoint is unknown given a lack of knowledge about the impact of a policy (J. Wilkerson, American Journal of Political Science, 1991, 35, 613–623) or the possibility of sophisticated voting (C. Volden, Journal of Politics, 1998, 60, 149–173). We show that in various contexts our analysis improves on existing methods, yielding consistent and efficient ideal point estimates and a better-fitting model with improved predictive accuracy.

Measuring Bias and Uncertainty in DW-NOMINATE Ideal Point Estimates via the Parametric Bootstrap

2009 ◽  
Vol 17 (3) ◽  
pp. 261-275 ◽  
Author(s):  
Royce Carroll ◽  
Jeffrey B. Lewis ◽  
James Lo ◽  
Keith T. Poole ◽  
Howard Rosenthal
Keyword(s):  
Political Science ◽  
Ideal Point ◽  
Parametric Bootstrap ◽  
Standard Errors ◽  
Point Estimates ◽  
Ideal Points ◽  
Measuring Bias ◽  
The U.S ◽  
Over Time

DW-NOMINATE scores for the U.S. Congress are widely used measures of legislators' ideological locations over time. These scores have been used in a large number of studies in political science and closely related fields. In this paper, we extend the work of Lewis and Poole (2004) on the parametric bootstrap to DW-NOMINATE and obtain standard errors for the legislator ideal points. These standard errors are in the range of 1%–4% of the range of DW-NOMINATE coordinates.

Estimating a model of sportfishing trip expenditures using a quasi-maximum likelihood approach

2016 ◽  
Vol 23 (2) ◽  
pp. 448-459 ◽  
Author(s):  
Richard T. Melstrom
Keyword(s):  
Maximum Likelihood ◽  
Exponential Model ◽  
Parameter Estimates ◽  
Maximum Likelihood Approach ◽  
Likelihood Approach ◽  
Quasi Maximum Likelihood ◽  
Species Preferences ◽  
The Impact ◽  
Consistent Parameter

This article presents an exponential model of tourist expenditures estimated by a quasi-maximum likelihood (QML) technique. The advantage of this approach is that, unlike conventional OLS and Tobit estimators, it produces consistent parameter estimates under conditions of a corner solution at zero and heteroscedasticity. An application to sportfishing evaluates the role of socioeconomic demographics and species preferences on trip spending. The bias from an inappropriate estimator is illustrated by comparing the results from QML and OLS estimation, which shows that OLS significantly overstates the impact of trip duration on trip expenditures compared with the QML estimator. Both sets of estimates imply that trout and bass anglers spend significantly more on their fishing trips compared with other anglers.

Data Driven Simplified Three-Way Catalyst Health Diagnostic Models: Experimental Results

2007 ◽  
Author(s):  
Brandon M. Dawson ◽  
Matthew A. Franchek ◽  
Karolos Grigoriadis
Keyword(s):  
Test Procedure ◽  
Parameter Estimates ◽  
Simplified Models ◽  
Health Measure ◽  
Vehicle Data ◽  
Diagnostic Models ◽  
Useful Life ◽  
The Impact ◽  
Three Way Catalyst ◽  
Consistent Parameter

Presented is the identification of simplified three-way catalyst (TWC) models from vehicle data. The simplified models are developed for multiple TWCs from two different classes: full useful life and threshold. The full useful life (FUL) TWCs represent catalysts from vehicles with 100,000 miles whereas threshold TWCs represent catalysts from vehicles with over 150,000 miles. The results showed that these simplified models have consistent parameter estimates when identified in a passive monitoring mode as the vehicle experiences a driving cycle from the Federal Test Procedure (FTP). Moreover the parameters of the simplified TWC models contain TWC health/age information. The model input is the air mass flow rate (AM) into the engine and the model output is a proposed TWC health measure developed in this paper. The impact of TWC temperature will also be detailed.

scholarly journals Change We Can Believe In? Using Political Science to Predict Policy Change in the Obama Presidency

2009 ◽  
Vol 42 (02) ◽  
pp. 329-333 ◽  
Author(s):  
Jonathan Woon
Keyword(s):  
Political Science ◽  
Policy Change ◽  
Presidential Election ◽  
Ideal Point ◽  
Congressional Elections ◽  
Congressional Election ◽  
Partisan Polarization ◽  
Point Estimates

Based on the results of the 2008 presidential and congressional elections, an analysis using theories and methods of modern political science (pivotal politics theory, ideal point estimates, and bootstrap simulations) suggests that the conditions are ripe for real policy change. Specifically, we should expect policies to move significantly in a liberal direction, few or no policies should move in a conservative direction, and many of the outcomes will be moderate or somewhat to the left of center (rather than far left). Furthermore, the predictions depend as much on partisan polarization and the results of the congressional election as they do on the outcome of presidential election itself.

A Measure of Robustness to Misspecification

2015 ◽  
Vol 105 (5) ◽  
pp. 476-480 ◽  
Author(s):  
Susan Athey ◽  
Guido Imbens
Keyword(s):  
Standard Deviation ◽  
Statistical Model ◽  
Treatment Effect ◽  
Systematic Approach ◽  
Standard Errors ◽  
Parameter Estimates ◽  
Large Set ◽  
Point Estimates ◽  
Robustness Measure ◽  
Assessing Sensitivity

Researchers often report estimates and standard errors for the object of interest (such as a treatment effect) based on a single specification of a statistical model. We propose a systematic approach to assessing sensitivity to specification. We construct estimates of the object of interest for each of a large set of models. Our proposed robustness measure is the standard deviation of the point estimates over the set of models. Each member of the set is generated by splitting the sample into two subsamples based on covariate values, constructing separate parameter estimates for each subsample, and then combining the results.

Time Series Intervals and Statistical Inference: The Effects of Temporal Aggregation on Event Data Analysis

2004 ◽  
Vol 12 (1) ◽  
pp. 97-104 ◽  
Author(s):  
Stephen M. Shellman
Keyword(s):  
Data Analysis ◽  
Political Science ◽  
Statistical Inference ◽  
Science Studies ◽  
Parameter Estimates ◽  
Temporal Aggregation ◽  
Event Data ◽  
Political Analysis ◽  
The Impact ◽  
Event Data Analysis

While many areas of research in political science draw inferences from temporally aggregated data, rarely have researchers explored how temporal aggregation biases parameter estimates. With some notable exceptions (Freeman 1989, Political Analysis 1:61–98; Alt et al. 2001, Political Analysis 9:21–44; Thomas 2002, “Event Data Analysis and Threats from Temporal Aggregation”) political science studies largely ignore how temporal aggregation affects our inferences. This article expands upon others' work on this issue by assessing the effect of temporal aggregation decisions on vector autoregressive (VAR) parameter estimates, significance levels, Granger causality tests, and impulse response functions. While the study is relevant to all fields in political science, the results directly apply to event data studies of conflict and cooperation. The findings imply that political scientists should be wary of the impact that temporal aggregation has on statistical inference.

Statistical Backwards Induction: A Simple Method for Estimating Recursive Strategic Models

2007 ◽  
Vol 16 (1) ◽  
pp. 21-40 ◽  
Author(s):  
Muhammet Ali Bas ◽  
Curtis S. Signorino ◽  
Robert W. Walker
Keyword(s):  
Financial Markets ◽  
Standard Errors ◽  
Parameter Estimates ◽  
Extensive Form ◽  
Speculative Attacks ◽  
Simple Method ◽  
Extensive Form Games ◽  
Backwards Induction ◽  
Consistent Parameter

We present a simple method for estimating regressions based on recursive extensive-form games. Our procedure, which can be implemented in most standard statistical packages, involves sequentially estimating standard logits (or probits) in a manner analogous to backwards induction. We demonstrate that the technique produces consistent parameter estimates and show how to calculate consistent standard errors. To illustrate the method, we replicate Leblang's (2003) study of speculative attacks by financial markets and government responses to these attacks.

The Impact of Missing and Error-Prone Auxiliary Information on Sparse-Matrix Sub-Population Parameter Estimates

Methodology ◽  
2015 ◽  
Vol 11 (3) ◽  
pp. 89-99 ◽  
Author(s):  
Leslie Rutkowski ◽  
Yan Zhou
Keyword(s):  
Sparse Matrix ◽  
Small Body ◽  
Auxiliary Information ◽  
Poor Quality ◽  
Quality Data ◽  
Estimation Methods ◽  
Parameter Estimates ◽  
Population Parameter ◽  
Conditioning Model ◽  
The Impact

Abstract. Given a consistent interest in comparing achievement across sub-populations in international assessments such as TIMSS, PIRLS, and PISA, it is critical that sub-population achievement is estimated reliably and with sufficient precision. As such, we systematically examine the limitations to current estimation methods used by these programs. Using a simulation study along with empirical results from the 2007 cycle of TIMSS, we show that a combination of missing and misclassified data in the conditioning model induces biases in sub-population achievement estimates, the magnitude and degree to which can be readily explained by data quality. Importantly, estimated biases in sub-population achievement are limited to the conditioning variable with poor-quality data while other sub-population achievement estimates are unaffected. Findings are generally in line with theory on missing and error-prone covariates. The current research adds to a small body of literature that has noted some of the limitations to sub-population estimation.

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