Out of Control: The (Over)Use of Controls in Accounting Research

2021 ◽  
Author(s):  
Robert L. Whited ◽  
Quinn T. Swanquist ◽  
Jonathan E. Shipman ◽  
James R. Moon
Keyword(s):  
Measurement Error ◽  
Fixed Effects ◽  
Causal Effects ◽  
Methodological Issues ◽  
Control Variables ◽  
Treatment Assignment ◽  
Accounting Research ◽  
Empirical Tests ◽  
Selection Of

In the absence of random treatment assignment, the selection of appropriate control variables is essential to designing well-specified empirical tests of causal effects. However, the importance of control variables seems underappreciated in accounting research relative to other methodological issues. Despite the frequent reliance on control variables, the accounting literature has limited guidance on how to select them. We evaluate the evolution in use of control variables in accounting research and discuss some of the issues that researchers should consider when choosing control variables. Using simulations, we illustrate that more control is not always better and that some control variables can introduce bias into an otherwise well-specified model. We also demonstrate other issues with control variables including the effects of measurement error and complications associated with fixed effects. Lastly, we provide practical suggestions for future accounting research.

Accounting research production and evaluation: The view of the professionals

2017 ◽  
pp. 75-80
Author(s):  
Orazio Vagnozzi
Keyword(s):  
The Other ◽  
Methodological Issues ◽  
Accounting Research ◽  
Research Production ◽  
Accounting Practice ◽  
Mutual Knowledge ◽  
The One

The existence of a gap between accounting research and accounting practice has been extensively described in literature. In order to be able to publish a research in a high-ranked accounting journal, it seems that methodological issues are more important than those related to the relevance of the topics covered. To improve research and accounting practice and to avoid the risk of accounting research becoming selfreferential, every effort should be made to bridge the current gap between research and accounting practice. To this end, the development of mutual knowledge of the agenda of researchers and practitioners on the one hand, and participation in joint projects on the other, could represent possible future solutions to be pursued.

scholarly journals Posterior predictive treatment assignment methods for causal inference in the context of time-varying treatments

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Shirley X. Liao ◽  
Lucas Henneman ◽  
Cory Zigler
Keyword(s):  
American Statistical Association ◽  
Treatment Patterns ◽  
Average Treatment Effect ◽  
Causal Effects ◽  
Time Varying ◽  
Medicare Beneficiaries ◽  
Finite Sample ◽  
Treatment Assignment ◽  
Power Plant Emissions ◽  
Plant Emissions

AbstractMarginal structural models (MSM) with inverse probability weighting (IPW) are used to estimate causal effects of time-varying treatments, but can result in erratic finite-sample performance when there is low overlap in covariate distributions across different treatment patterns. Modifications to IPW which target the average treatment effect (ATE) estimand either introduce bias or rely on unverifiable parametric assumptions and extrapolation. This paper extends an alternate estimand, the ATE on the overlap population (ATO) which is estimated on a sub-population with a reasonable probability of receiving alternate treatment patterns in time-varying treatment settings. To estimate the ATO within an MSM framework, this paper extends a stochastic pruning method based on the posterior predictive treatment assignment (PPTA) (Zigler, C. M., and M. Cefalu. 2017. “Posterior Predictive Treatment Assignment for Estimating Causal Effects with Limited Overlap.” eprint arXiv:1710.08749.) as well as a weighting analog (Li, F., K. L. Morgan, and A. M. Zaslavsky. 2018. “Balancing Covariates via Propensity Score Weighting.” Journal of the American Statistical Association 113: 390–400, https://doi.org/10.1080/01621459.2016.1260466.) to the time-varying treatment setting. Simulations demonstrate the performance of these extensions compared against IPW and stabilized weighting with regard to bias, efficiency, and coverage. Finally, an analysis using these methods is performed on Medicare beneficiaries residing across 18,480 ZIP codes in the U.S. to evaluate the effect of coal-fired power plant emissions exposure on ischemic heart disease (IHD) hospitalization, accounting for seasonal patterns that lead to change in treatment over time.

Individual effects in variation analysis: Model, software, and research design

2013 ◽  
Vol 25 (1) ◽  
pp. 89-118 ◽  
Author(s):  
John C. Paolillo
Keyword(s):  
Research Design ◽  
Fixed Effects ◽  
Selection Criteria ◽  
Current Approach ◽  
Variation Analysis ◽  
Analysis Model ◽  
Individual Level ◽  
Mixed Effects Modeling ◽  
Variationist Sociolinguistics ◽  
Selection Of

AbstractIndividual-level variation is a recurrent issue in variationist sociolinguistics. One current approach recommends addressing this via mixed-effects modeling. This paper shows that a closely related model with fixed effects for individual speakers can be directly estimated using Goldvarb. The consequences of employing different approaches to speaker variation are explored by using different model selection criteria. We conclude by discussing the relation of the statistical model to the assumptions of the research design, pointing out that nonrandom selection of speakers potentially violates the assumptions of models with random effects for speaker, and suggesting that a model with fixed effects for speakers may be a better alternative in these cases.

Morehead & Ingram (1973) Revisited

2002 ◽  
Vol 45 (3) ◽  
pp. 559-563 ◽  
Author(s):  
David Ingram ◽  
Donald Morehead
Keyword(s):  
Language Impairment ◽  
Research Note ◽  
Past Tense ◽  
Inflectional Morphology ◽  
Typically Developing ◽  
Methodological Issues ◽  
Typically Developing Children ◽  
Marked Contrast ◽  
Third Person ◽  
Selection Of

The finding in Morehead and Ingram (1973) that children with a language impairment do better in the use of inflectional morphology than MLU-matched typically developing children has been in marked contrast to several subsequent studies that have found the opposite relationship (cf. review in Leonard, 1998). This research note presents a reanalysis of a subset of the original Morehead and Ingram data in an attempt to reconcile these contradictory findings. The reanalysis revealed that the advantage on inflectional morphology for children with language impairment was only on the progressive suffix, not on plural and possessive or on the verbal morphemes third-person present tense and past tense. The results of the reanalysis are in line with more recent research (e.g., Rice, Wexler, & Cleave, 1995). The resolution of these discrepant results highlights the critical roles that methodological issues play—specifically, how subjects are matched on MLU, how inflectional morphology is measured, and the selection of subjects with regard to age.

scholarly journals Panel experiments and dynamic causal effects: A finite population perspective

2021 ◽  
Vol 12 (4) ◽  
pp. 1171-1196 ◽  
Author(s):  
Iavor Bojinov ◽  
Ashesh Rambachan ◽  
Neil Shephard
Keyword(s):  
Fixed Effects ◽  
Serial Correlation ◽  
Finite Population ◽  
Relative Effectiveness ◽  
Randomization Test ◽  
Causal Effects ◽  
Probability Limit ◽  
Population Probability ◽  
Potential Outcomes Framework ◽  
Randomization Distribution

In panel experiments, we randomly assign units to different interventions, measuring their outcomes, and repeating the procedure in several periods. Using the potential outcomes framework, we define finite population dynamic causal effects that capture the relative effectiveness of alternative treatment paths. For a rich class of dynamic causal effects, we provide a nonparametric estimator that is unbiased over the randomization distribution and derive its finite population limiting distribution as either the sample size or the duration of the experiment increases. We develop two methods for inference: a conservative test for weak null hypotheses and an exact randomization test for sharp null hypotheses. We further analyze the finite population probability limit of linear fixed effects estimators. These commonly‐used estimators do not recover a causally interpretable estimand if there are dynamic causal effects and serial correlation in the assignments, highlighting the value of our proposed estimator.

scholarly journals Selection of a suitable data set and model for the genetic evaluation of the linear description of conformation and type description in Old Kladruber horses

2012 ◽  
Vol 55 (2) ◽  
pp. 105-112
Author(s):  
L. Vostrý ◽  
K. Mach ◽  
J. Přibyl
Keyword(s):  
Random Effects ◽  
Fixed Effects ◽  
Genetic Parameters ◽  
Suitable Model ◽  
Residual Variance ◽  
Linear Type ◽  
Colour Variant ◽  
Repeated Evaluation ◽  
Estimation Of Genetic Parameters ◽  
Selection Of

Abstract. The objective of this paper was to select a suitable data subset and statistical model for the estimation of genetic parameters for 36 traits of the linear type in 977 Old Kladruber horses. Two subsets were tested to identify a suitable subset for analysis. One subset included repeated evaluation of certain individuals, whereas the other did not. The most suitable subset included repeated evaluation (n=1 390). The selection of a suitable model was made from 4 candidate models. These models comprised a number of random effects (direct individual effect and animal permanent environmental effect of the animal) and a number of fixed effects (colour variant, stud, colour variant × stud interaction, sex, age at description, year of birth, year of description). The model was selected based on the Akaike information criterion (AIC, Akaike 1974), residual variance and heritability coefficient. The model that included colour variant, stud, colour variant × stud interaction, sex, age at description, and year of description as fixed effects and direct individual and animal permanent environment as random effects was the most suitable model for the estimation of genetic parameters and for the subsequent estimation of breeding values.

scholarly journals Novel insights into the consequences of obesity: a phenotype-wide Mendelian randomization study

2022 ◽  
Author(s):  
Chang He ◽  
Miaoran Zhang ◽  
Jiuling Li ◽  
Yiqing Wang ◽  
Lanlan Chen ◽  
...  
Keyword(s):  
Instrumental Variables ◽  
Fixed Effects ◽  
Mendelian Randomization ◽  
Portion Size ◽  
Causal Effects ◽  
Calorie Intake ◽  
Smoking Behaviors ◽  
Prostate Cancers ◽  
The Uk ◽  
Consequences Of Obesity

AbstractObesity is thought to significantly impact the quality of life. In this study, we sought to evaluate the health consequences of obesity on the risk of a broad spectrum of human diseases. The causal effects of exposing to obesity on health outcomes were inferred using Mendelian randomization (MR) analyses using a fixed effects inverse-variance weighted model. The instrumental variables were SNPs associated with obesity as measured by body mass index (BMI) reported by GIANT consortium. The spectrum of outcome consisted of the phenotypes from published GWAS and the UK Biobank. The MR-Egger intercept test was applied to estimate horizontal pleiotropic effects, along with Cochran’s Q test to assess heterogeneity among the causal effects of instrumental variables. Our MR results confirmed many putative disease risks due to obesity, such as diabetes, dyslipidemia, sleep disorder, gout, smoking behaviors, arthritis, myocardial infarction, and diabetes-related eye disease. The novel findings indicated that elevated red blood cell count was inferred as a mediator of BMI-induced type 2 diabetes in our bidirectional MR analysis. Intriguingly, the effects that higher BMI could decrease the risk of both skin and prostate cancers, reduce calorie intake, and increase the portion size warrant further studies. Our results shed light on a novel mechanism of the disease-causing roles of obesity.

18 Genetic and Phenotypic Parameters for Aleutian Disease Tests and Their Correlations with Pelt Quality, Reproductive Performance, Packed-cell Volume, and Harvest Length in Mink

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 8-9
Author(s):  
Guoyu Hu ◽  
Duy Ngoc Do ◽  
Karim Karimi ◽  
Younes Miar
Keyword(s):  
Cell Volume ◽  
Reproductive Performance ◽  
Packed Cell Volume ◽  
Fixed Effects ◽  
Complex Disease ◽  
Genetic Correlations ◽  
Study Data ◽  
Economic Losses ◽  
Enzyme Linked Immunosorbent Assay ◽  
Selection Of

Abstract Aleutian disease (AD) is an untreatable immune complex disease in mink and brings tremendous economic losses to the mink industry globally. The ineffectiveness of culling, immunoprophylaxis, and medical treatment in controlling AD have urged mink farmers to select AD-resilient mink based on the AD tests. However, the genetic analysis of these tests and their correlations with AD-resilient traits have not been investigated. In this study, data on 5,824 mink were used to estimate the genetic and phenotypic parameters of four AD tests, including two systems of enzyme-linked immunosorbent assay (ELISA), counterimmunoelectrophoresis (CIEP), and iodine agglutination test (IAT), and their genetic and phenotypic correlations with pelt quality, reproductive performance, packed-cell volume (PCV), and harvest length (HL). Significance (P < 0.05) of fixed effects (sex, year, color type, the number of mating, and dam age), covariates (age at blood sampling and age at harvest), and random effects (additive genetic, permanent environmental, and maternal effects) were determined using univariate models. The genetic and phenotypic parameters for all traits were estimated under bivariate models using ASReml 4.1. Estimated heritabilities (±SE) were 0.39±0.05, 0.61±0.07, 0.11±0.07, and 0.26±0.05 for antigen-based ELISA (ELISA-G), virus capsid protein-based ELISA, CIEP, and IAT, respectively. The ELISA-G showed moderate repeatability (0.58±0.04) and significant (P < 0.05) negative genetic correlations (±SE) with reproductive performance traits (from -0.41±0.16 to -0.49±0.12), PCV (-0.53±0.09), and HL (-0.45±0.16). These results indicated that the selection of mink with a lower ELISA-G score could not only decrease the anti-AMDV antibody level and the extent of anemia but also improve the female reproductive performance and the harvest length of mink without causing adverse influences on the pelt quality. Hence, ELISA-G could be applied as an indicator for genetic selection of AD-resilient mink and help mink farmers reduce the adverse effects of AD.

The Causal Effects of Proximity on Investment: Evidence from Flight Introductions

2019 ◽  
Vol 55 (6) ◽  
pp. 1978-2004 ◽  
Author(s):  
Jesse Ellis ◽  
Leonardo Madureira ◽  
Shane Underwood
Keyword(s):  
Mutual Funds ◽  
Fixed Effects ◽  
Investment Decisions ◽  
Relevant Information ◽  
Causal Effects ◽  
Superior Performance ◽  
Firm Level ◽  
Exogenous Shock ◽  
And Performance ◽  
Fund Investment

We use the introduction of direct flights as an exogenous shock to the travel time between mutual funds and firms to estimate the causal effects of proximity on fund investment decisions and performance. We find that a fund invests significantly more in firms that become more proximate following the introduction of direct flights and that these more proximate investments exhibit superior performance. Our findings are robust to including a variety of fixed effects and potential confounders such as firm-level shocks, fund-level shocks, and time trends. Collectively, our results indicate that proximity enhances investors’ ability to acquire value-relevant information about firms.

Sign in / Sign up

Export Citation Format

Share Document