A fundamental challenge facing applied time-series analysts is how to draw inferences about long-run relationships (LRR) when we are uncertain whether the data contain unit roots. Unit root tests are ...notoriously unreliable and often leave analysts uncertain, but popular extant methods hinge on correct classification. Webb, Linn, and Lebo (WLL; 2019) develop a framework for inference based on critical value bounds for hypothesis tests on the long-run multiplier (LRM) that eschews unit root tests and incorporates the uncertainty inherent in identifying the dynamic properties of the data into inferences about LRRs. We show how the WLL bounds procedure can be applied to any fully specified regression model to solve this fundamental challenge, extend the results of WLL by presenting a general set of critical value bounds to be used in applied work, and demonstrate the empirical relevance of the LRM bounds procedure in two applications.
Repeated cross-sectional (RCS) designs are distinguishable from true panels and pooled cross-sectional time series (PCSTS) since cross-sectional units (e.g., individual survey respondents) appear but ...once in the data. This poses two serious challenges. First, as with PCSTS, autocorrelation threatens inferences. However, common solutions like differencing and using a lagged dependent variable are not possible with RCS since lags for i cannot be used. Second, although RCS designs contain information that allows both aggregate- and individual-level analyses, available methods—from pooled ordinary least squares to PCSTS to time series—force researchers to choose one level of analysis. The PCSTS tool kit does not provide an appropriate solution, and we offer one here: double filtering with ARFIMA methods to account for autocorrelation in longer RCS followed by the use of multilevel modeling to estimate both aggregate- and individual-level parameters simultaneously. We use Monte Carlo experiments and three applied examples to explore the advantages of our framework.
While traditionally considered for non-stationary and cointegrated data, DeBoef and Keele suggest applying a General Error Correction Model (GECM) to stationary data with or without cointegration. ...The GECM has since become extremely popular in political science but practitioners have confused essential points. For one, the model is treated as perfectly flexible when, in fact, the opposite is true. Time series of various orders of integration–stationary, non-stationary, explosive, near- and fractionally integrated–should not be analyzed together but researchers consistently make this mistake. That is, without equation balance the model is misspecified and hypothesis tests and long-run-multipliers are unreliable. Another problem is that the error correction term's sampling distribution moves dramatically depending upon the order of integration, sample size, number of covariates, and the boundedness of Yt. This means that practitioners are likely to overstate evidence of error correction, especially when using a traditional t-test. We evaluate common GECM practices with six types of data, 746 simulations, and five paper replications.
To what extent is party loyalty a liability for incumbent legislators? Past research on legislative voting and elections suggests that voters punish members who are ideologically "out of step" with ...their districts. In seeking to move beyond the emphasis in the literature on the effects of ideological extremity on legislative vote share, we examine how partisan loyalty can adversely affect legislators' electoral fortunes. Specifically, we estimate the effects of each legislator's party unity—the tendency of a member to vote with his or her party on salient issues that divide the two major parties—on vote margin when running for reelection. Our results suggest that party loyalty on divisive votes can indeed be a liability for incumbent House members. In fact, we find that voters are not punishing elected representatives for being too ideological; they are punishing them for being too partisan.
Severe obesity is a rapidly growing global health threat. Although often attributed to unhealthy lifestyle choices or environmental factors, obesity is known to be heritable and highly polygenic; the ...majority of inherited susceptibility is related to the cumulative effect of many common DNA variants. Here we derive and validate a new polygenic predictor comprised of 2.1 million common variants to quantify this susceptibility and test this predictor in more than 300,000 individuals ranging from middle age to birth. Among middle-aged adults, we observe a 13-kg gradient in weight and a 25-fold gradient in risk of severe obesity across polygenic score deciles. In a longitudinal birth cohort, we note minimal differences in birthweight across score deciles, but a significant gradient emerged in early childhood and reached 12 kg by 18 years of age. This new approach to quantify inherited susceptibility to obesity affords new opportunities for clinical prevention and mechanistic assessment.
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•A genome-wide polygenic score can quantify inherited susceptibility to obesity•Polygenic score effect on weight emerges early in life and increases into adulthood•Effect of polygenic score can be similar to a rare, monogenic obesity mutation•High polygenic score is a strong risk factor for severe obesity and associated diseases
A genome-wide polygenic score quantifies inherited susceptibility to obesity, integrating information from 2.1 million common genetic variants to identify adults at risk of severe obesity.
Genetic variation can predispose to disease both through (i) monogenic risk variants that disrupt a physiologic pathway with large effect on disease and (ii) polygenic risk that involves many ...variants of small effect in different pathways. Few studies have explored the interplay between monogenic and polygenic risk. Here, we study 80,928 individuals to examine whether polygenic background can modify penetrance of disease in tier 1 genomic conditions - familial hypercholesterolemia, hereditary breast and ovarian cancer, and Lynch syndrome. Among carriers of a monogenic risk variant, we estimate substantial gradients in disease risk based on polygenic background - the probability of disease by age 75 years ranged from 17% to 78% for coronary artery disease, 13% to 76% for breast cancer, and 11% to 80% for colon cancer. We propose that accounting for polygenic background is likely to increase accuracy of risk estimation for individuals who inherit a monogenic risk variant.
Presidential approval is a desirable commodity for US presidents, one that bolsters re-election chances and the prospects of legislative success. An important question, then, is what shapes citizens’ ...approval of the executive. A large body of literature demonstrates that the president’s handling of issues, particularly the economy, is an important component. A similarly large literature confirms that evaluations of the president, like most political objects, are filtered through partisan lenses. Due to changes in the US political environment in the last few decades, we suspect that the relative importance of these components has changed over time. In particular, we argue that polarization has increased partisan motivated reasoning when it comes to evaluations of the president. We support this empirically by disaggregating approval ratings from Reagan to Obama into in- and out-partisans, finding that approval is increasingly detached from economic assessments. This is true for members opposite the president’s party earlier than it is for in-partisans. While the president has been over-attributed credit and blame for economic conditions, the increasing impact of partisanship on approval at the expense of economic sentiment has generally negative implications when it comes to electoral outcomes and democratic accountability.
Research in political psychology has shown the importance of motivated reasoning as a prism through which individuals view the political world. From this we develop the hypothesis that, with strong ...positive beliefs firmly in place, partisan groups ignore or discount information about the performance of political figures they like. We then speculate about how this tendency should manifest itself in presidential approval ratings and test our hypotheses using monthly presidential approval data disaggregated by party identification for the 1955-2005 period. Our results show that partisan groups generally do reward and punish presidents for economic performance, but only those presidents of the opposite party. We also develop a model of presidential approval for self-identified Independents and, finally, a model of the partisan gap, the difference in approval between Democrat and Republican identifiers.
Implementation of polygenic risk scores (PRS) may improve disease prevention and management but poses several challenges: the construction of clinically valid assays, interpretation for individual ...patients, and the development of clinical workflows and resources to support their use in patient care. For the ongoing Veterans Affairs Genomic Medicine at Veterans Affairs (GenoVA) Study we developed a clinical genotype array-based assay for six published PRS. We used data from 36,423 Mass General Brigham Biobank participants and adjustment for population structure to replicate known PRS-disease associations and published PRS thresholds for a disease odds ratio (OR) of 2 (ranging from 1.75 (95% CI: 1.57-1.95) for type 2 diabetes to 2.38 (95% CI: 2.07-2.73) for breast cancer). After confirming the high performance and robustness of the pipeline for use as a clinical assay for individual patients, we analyzed the first 227 prospective samples from the GenoVA Study and found that the frequency of PRS corresponding to published OR > 2 ranged from 13/227 (5.7%) for colorectal cancer to 23/150 (15.3%) for prostate cancer. In addition to the PRS laboratory report, we developed physician- and patient-oriented informational materials to support decision-making about PRS results. Our work illustrates the generalizable development of a clinical PRS assay for multiple conditions and the technical, reporting and clinical workflow challenges for implementing PRS information in the clinic.
Genomic sequencing (GS) for newborns may enable detection of conditions for which early knowledge can improve health outcomes. One of the major challenges hindering its broader application is the ...time it takes to assess the clinical relevance of detected variants and the genes they impact so that disease risk is reported appropriately.
To facilitate rapid interpretation of GS results in newborns, we curated a catalog of genes with putative pediatric relevance for their validity based on the ClinGen clinical validity classification framework criteria, age of onset, penetrance, and mode of inheritance through systematic evaluation of published evidence. Based on these attributes, we classified genes to guide the return of results in the BabySeq Project, a randomized, controlled trial exploring the use of newborn GS (nGS), and used our curated list for the first 15 newborns sequenced in this project.
Here, we present our curated list for 1,514 gene-disease associations. Overall, 954 genes met our criteria for return in nGS. This reference list eliminated manual assessment for 41% of rare variants identified in 15 newborns.
Our list provides a resource that can assist in guiding the interpretive scope of clinical GS for newborns and potentially other populations.Genet Med advance online publication 12 January 2017.
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