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Journal articles on the topic 'Model Misspecification'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Tarpey, Thaddeus, Dong Yun, and Eva Petkova. "Model misspecification." Statistical Modelling: An International Journal 8, no. 2 (2008): 199–218. http://dx.doi.org/10.1177/1471082x0800800204.

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2

Plümper, Thomas, and Vera E. Troeger. "Not so Harmless After All: The Fixed-Effects Model." Political Analysis 27, no. 1 (2018): 21–45. http://dx.doi.org/10.1017/pan.2018.17.

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The fixed-effects estimator is biased in the presence of dynamic misspecification and omitted within variation correlated with one of the regressors. We argue and demonstrate that fixed-effects estimates can amplify the bias from dynamic misspecification and that with omitted time-invariant variables and dynamic misspecifications, the fixed-effects estimator can be more biased than the ‘naïve’ OLS model. We also demonstrate that the Hausman test does not reliably identify the least biased estimator when time-invariant and time-varying omitted variables or dynamic misspecifications exist. Accor

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3

Yuan, Ke-Hai, Linda L. Marshall, and Peter M. Bentler. "8. Assessing the Effect of Model Misspecifications on Parameter Estimates in Structural Equation Models." Sociological Methodology 33, no. 1 (2003): 241–65. http://dx.doi.org/10.1111/j.0081-1750.2003.00132.x.

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Model misspecifications may have a systematic effect on parameters, causing biases in their estimates. In the application of structural equation models, every interesting model is fallible. When simultaneously evaluating a model, it is of interest to study whether all parameters are affected by a misspecification. This paper provides three procedures for evaluating such an effect: (1) analyzing the path, (2) using a functional relationship, and (3) using a significance test. Analyzing the path is illustrated through a confirmatory factor model. This method is ad hoc but intuitive. A more rigor

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4

Uppal, Raman, and Tan Wang. "Model Misspecification and Underdiversification." Journal of Finance 58, no. 6 (2003): 2465–86. http://dx.doi.org/10.1046/j.1540-6261.2003.00612.x.

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5

Winship, Christopher, and Bruce Western. "Multicollinearity and Model Misspecification." Sociological Science 3 (2016): 627–49. http://dx.doi.org/10.15195/v3.a27.

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6

Robitzsch, Alexander. "Modeling Model Misspecification in Structural Equation Models." Stats 6, no. 2 (2023): 689–705. http://dx.doi.org/10.3390/stats6020044.

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Structural equation models constrain mean vectors and covariance matrices and are frequently applied in the social sciences. Frequently, the structural equation model is misspecified to some extent. In many cases, researchers nevertheless intend to work with a misspecified target model of interest. In this article, a simultaneous statistical inference for sampling errors and model misspecification errors is discussed. A modified formula for the variance matrix of the parameter estimate is obtained by imposing a stochastic model for model errors and applying M-estimation theory. The presence of

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7

Bonhomme, Stéphane, and Martin Weidner. "Minimizing sensitivity to model misspecification." Quantitative Economics 13, no. 3 (2022): 907–54. http://dx.doi.org/10.3982/qe1930.

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We propose a framework for estimation and inference when the model may be misspecified. We rely on a local asymptotic approach where the degree of misspecification is indexed by the sample size. We construct estimators whose mean squared error is minimax in a neighborhood of the reference model, based on one‐step adjustments. In addition, we provide confidence intervals that contain the true parameter under local misspecification. As a tool to interpret the degree of misspecification, we map it to the local power of a specification test of the reference model. Our approach allows for systemati

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8

McMillen, Daniel P. "Spatial Autocorrelation Or Model Misspecification?" International Regional Science Review 26, no. 2 (2003): 208–17. http://dx.doi.org/10.1177/0160017602250977.

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9

Chien, Li-Chu, and Tsung-Shan Tsou. "Regression Diagnostic under Model Misspecification." Journal of Applied Statistics 34, no. 5 (2007): 563–75. http://dx.doi.org/10.1080/02664760701235014.

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10

Hansen, Lars Peter, Thomas J. Sargent, Gauhar Turmuhambetova, and Noah Williams. "Robust control and model misspecification." Journal of Economic Theory 128, no. 1 (2006): 45–90. http://dx.doi.org/10.1016/j.jet.2004.12.006.

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11

Bohren, J. Aislinn. "Informational herding with model misspecification." Journal of Economic Theory 163 (May 2016): 222–47. http://dx.doi.org/10.1016/j.jet.2016.01.011.

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12

Waggoner, Daniel F., and Tao Zha. "Confronting model misspecification in macroeconomics." Journal of Econometrics 171, no. 2 (2012): 167–84. http://dx.doi.org/10.1016/j.jeconom.2012.06.013.

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13

Liu, Ren. "Misspecification of Attribute Structure in Diagnostic Measurement." Educational and Psychological Measurement 78, no. 4 (2017): 605–34. http://dx.doi.org/10.1177/0013164417702458.

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Attribute structure is an explicit way of presenting the relationship between attributes in diagnostic measurement. The specification of attribute structures directly affects the classification accuracy resulted from psychometric modeling. This study provides a conceptual framework for understanding misspecifications of attribute structures. Under the framework, each attribute structure can be represented through an external shape and an internal organization. A simulation study and an application example were used to investigate how misspecification of external shapes and internal organizatio

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14

Ellison, Martin, and Thomas J. Sargent. "Welfare Cost of Business Cycles with Idiosyncratic Consumption Risk and a Preference for Robustness." American Economic Journal: Macroeconomics 7, no. 2 (2015): 40–57. http://dx.doi.org/10.1257/mac.20130098.

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The welfare cost of random consumption fluctuations is known from De Santis (2007) to be increasing in the level of uninsured idiosyncratic consumption risk. It is known from Barillas, Hansen, and Sargent (2009) to increase if agents care about robustness to model misspecification. We calculate the cost of business cycles in an economy where agents face idiosyncratic consumption risk and fear model misspecification, finding that idiosyncratic risk has a greater impact on the cost of business cycles if agents already fear model misspecification. Correspondingly, endowing agents with fears about

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15

Beatty, Anne, Sandra Chamberlain, and Joseph Magliolo. "An Empirical Analysis of Model Misspecification in Studies of Valuation of Financial Statement Disclosures." Journal of Accounting, Auditing & Finance 10, no. 4 (1995): 719–49. http://dx.doi.org/10.1177/0148558x9501000403.

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A number of studies have examined the correlation between financial statement disclosures and share prices to assess the informativeness of these disclosures. There are several potential econometric problems with analyses of this type, and the interpretations of the results depend critically on the type of econometric problem. For example, the results of these studies should not be used to answer accounting policy questions unless the effect of an omitted variable bias is likely to be minimal. Given potential interpretation problems, we argue that analysis of model misspecification should be p

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16

Viraswami, K., and N. Reid. "Higher-order asymptotics under model misspecification." Canadian Journal of Statistics 24, no. 2 (1996): 263–78. http://dx.doi.org/10.2307/3315632.

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17

Anderson, Garnet L., and Thomas R. Fleming. "Model Misspecification in Proportional Hazards Regression." Biometrika 82, no. 3 (1995): 527. http://dx.doi.org/10.2307/2337531.

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18

Robinson, Timothy J., and Jeffery B. Birch. "Model misspecification in parametric dual modeling." Journal of Statistical Computation and Simulation 66, no. 2 (2000): 113–26. http://dx.doi.org/10.1080/00949650008812017.

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19

Risch, Neil, and Luis Giuffra. "Model Misspecification and Multipoint Linkage Analysis." Human Heredity 42, no. 1 (1992): 77–92. http://dx.doi.org/10.1159/000154047.

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20

Presnell, Brett, and Dennis D. Boos. "The IOS Test for Model Misspecification." Journal of the American Statistical Association 99, no. 465 (2004): 216–27. http://dx.doi.org/10.1198/016214504000000214.

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21

Inoue, Atsushi, Chun-Hung Kuo, and Barbara Rossi. "Identifying the sources of model misspecification." Journal of Monetary Economics 110 (April 2020): 1–18. http://dx.doi.org/10.1016/j.jmoneco.2019.01.003.

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22

Spokoiny, Vladimir, and Mayya Zhilova. "Bootstrap confidence sets under model misspecification." Annals of Statistics 43, no. 6 (2015): 2653–75. http://dx.doi.org/10.1214/15-aos1355.

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23

Tzavelas, George, Maria Douli, and Polychronis Economou. "Model misspecification effects for biased samples." Metrika 80, no. 2 (2016): 171–85. http://dx.doi.org/10.1007/s00184-016-0597-5.

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24

ANDERSON, G. L., and T. R. FLEMING. "Model misspecification in proportional hazards regression." Biometrika 82, no. 3 (1995): 527–41. http://dx.doi.org/10.1093/biomet/82.3.527.

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25

Luo, Yulei, Jun Nie, and Eric R. Young. "Ambiguity, Low Risk-Free Rates and Consumption Inequality." Economic Journal 130, no. 632 (2020): 2649–79. http://dx.doi.org/10.1093/ej/ueaa045.

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Abstract Macroeconomists failed to predict the Great Recession, suggesting that the existing macroeconomic models may have been misspecified. Bearing in mind this potential misspecification or ‘model uncertainty’, how do agents’ optimal decisions change? Furthermore, how large are the welfare costs of model misspecification? To shed light on these questions, we develop a tractable continuous-time general equilibrium model to show that a fear of model misspecification reduces both the equilibrium interest rate and the relative inequality of consumption to income, making the model’s predictions

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26

Hernández, Freddy, and Viviana Giampaoli. "The Impact of Misspecified Random Effect Distribution in a Weibull Regression Mixed Model." Stats 1, no. 1 (2018): 48–76. http://dx.doi.org/10.3390/stats1010005.

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Mixed models are useful tools for analyzing clustered and longitudinal data. These models assume that random effects are normally distributed. However, this may be unrealistic or restrictive when representing information of the data. Several papers have been published to quantify the impacts of misspecification of the shape of the random effects in mixed models. Notably, these studies primarily concentrated their efforts on models with response variables that have normal, logistic and Poisson distributions, and the results were not conclusive. As such, we investigated the misspecification of t

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27

Falk, Carl F., and Scott Monroe. "On Lagrange Multiplier Tests in Multidimensional Item Response Theory: Information Matrices and Model Misspecification." Educational and Psychological Measurement 78, no. 4 (2017): 653–78. http://dx.doi.org/10.1177/0013164417714506.

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Lagrange multiplier (LM) or score tests have seen renewed interest for the purpose of diagnosing misspecification in item response theory (IRT) models. LM tests can also be used to test whether parameters differ from a fixed value. We argue that the utility of LM tests depends on both the method used to compute the test and the degree of misspecification in the initially fitted model. We demonstrate both of these points in the context of a multidimensional IRT framework. Through an extensive Monte Carlo simulation study, we examine the performance of LM tests under varying degrees of model mis

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28

Zhu, Hai, Xia Luo, Yanjin Li, Ying Zhu, and Qian Huang. "Comparing the efficiency and robustness of state-of-the-art experimental designs for stated choice modeling: A simulation analysis." Advances in Mechanical Engineering 9, no. 2 (2017): 168781401769189. http://dx.doi.org/10.1177/1687814017691894.

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Among the ways to construct experimental designs having been proposed, orthogonal design, uniform design, and D-efficient design are state-of-the-art methods. This article provides detailed comparisons on the efficiency and robustness among these methods with three case studies in multinomial logit and mixed multinomial logit models. ND-error values and the departures of D-errors corresponding to misspecification of prior information are used as measurements of design efficiency and design robustness, respectively. Design methods are described, and designs with various numbers of runs are cons

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29

Gasparini, Alessandro, Mark S. Clements, Keith R. Abrams, and Michael J. Crowther. "Impact of model misspecification in shared frailty survival models." Statistics in Medicine 38, no. 23 (2019): 4477–502. http://dx.doi.org/10.1002/sim.8309.

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30

Kirby, James B., and Kenneth A. Bollen. "10. Using Instrumental Variable Tests to Evaluate Model Specification in Latent Variable Structural Equation Models." Sociological Methodology 39, no. 1 (2009): 327–55. http://dx.doi.org/10.1111/j.1467-9531.2009.01217.x.

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Structural equation modeling (SEM) with latent variables is a powerful tool for social and behavioral scientists, combining many of the strengths of psychometrics and econometrics into a single framework. The most common estimator for SEM is the full-information maximum likelihood (ML) estimator, but there is continuing interest in limited information estimators because of their distributional robustness and their greater resistance to structural specification errors. However, the literature discussing model fit for limited information estimators for latent variable models is sparse compared w

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31

Rainey, Carlisle, and Robert A. Jackson. "Unreliable Inferences About Unobserved Processes: A Critique of Partial Observability Models." Political Science Research and Methods 6, no. 2 (2017): 381–91. http://dx.doi.org/10.1017/psrm.2017.3.

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Methodologists and econometricians advocate the partial observability model as a tool that enables researchers to estimate the distinct effects of a single explanatory variable on two partially observable outcome variables. However, we show that when the explanatory variable of interest influences both partially observable outcomes, the partial observability model estimates are extremely sensitive to misspecification. We use Monte Carlo simulations to show that, under partial observability, minor, unavoidable misspecification of the functional form can lead to substantial large-sample bias, ev

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32

Raborn, Anthony W., Walter L. Leite, and Katerina M. Marcoulides. "A Comparison of Metaheuristic Optimization Algorithms for Scale Short-Form Development." Educational and Psychological Measurement 80, no. 5 (2020): 910–31. http://dx.doi.org/10.1177/0013164420906600.

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This study compares automated methods to develop short forms of psychometric scales. Obtaining a short form that has both adequate internal structure and strong validity with respect to relationships with other variables is difficult with traditional methods of short-form development. Metaheuristic algorithms can select items for short forms while optimizing on several validity criteria, such as adequate model fit, composite reliability, and relationship to external variables. Using a Monte Carlo simulation study, this study compared existing implementations of the ant colony optimization, Tab

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33

Vansteelandt, Stijn, Maarten Bekaert, and Gerda Claeskens. "On model selection and model misspecification in causal inference." Statistical Methods in Medical Research 21, no. 1 (2010): 7–30. http://dx.doi.org/10.1177/0962280210387717.

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34

Yoon, Heewon, and Kwang Mo Jeong. "Covariance Estimates of GLMs under Model Misspecification." Korean Data Analysis Society 20, no. 5 (2018): 2177–87. http://dx.doi.org/10.37727/jkdas.2018.20.5.2177.

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35

Kruijer, Willem. "Misspecification in Mixed-Model-Based Association Analysis." Genetics 202, no. 1 (2015): 363–66. http://dx.doi.org/10.1534/genetics.115.177212.

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36

Niu, Yabo, Debdeep Pati, and Bani K. Mallick. "Bayesian graph selection consistency under model misspecification." Bernoulli 27, no. 1 (2021): 637–72. http://dx.doi.org/10.3150/20-bej1253.

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37

Lee, Seonjoo, Brian S. Caffo, Balaji Lakshmanan, and Dzung L. Pham. "Evaluating model misspecification in independent component analysis." Journal of Statistical Computation and Simulation 85, no. 6 (2013): 1151–64. http://dx.doi.org/10.1080/00949655.2013.867961.

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38

Nambiar, Mila, David Simchi-Levi, and He Wang. "Dynamic Learning and Pricing with Model Misspecification." Management Science 65, no. 11 (2019): 4980–5000. http://dx.doi.org/10.1287/mnsc.2018.3194.

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We study a multiperiod dynamic pricing problem with contextual information, where the seller uses a misspecified demand model. The seller sequentially observes past demand, updates model parameters, and then chooses the price for the next period based on time-varying features. We show that model misspecification leads to a correlation between price and prediction error of demand per period, which, in turn, leads to inconsistent price elasticity estimates and hence suboptimal pricing decisions. We propose a “random price shock” (RPS) algorithm that dynamically generates randomized price shocks

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39

Gustafson, Paul. "On measuring sensitivity to parametric model misspecification." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 63, no. 1 (2001): 81–94. http://dx.doi.org/10.1111/1467-9868.00277.

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40

Piegorsch, Walter W., Daniela K. Nitcheva, and R. Webster West. "Excess risk estimation under multistage model misspecification." Journal of Statistical Computation and Simulation 76, no. 5 (2006): 423–30. http://dx.doi.org/10.1080/10629360500107808.

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41

Reisen, Valderio A., Manoel R. Sena Jr., and Silvia R. C. Lopes. "Error and Model Misspecification in ARFIMA Process." Brazilian Review of Econometrics 21, no. 1 (2001): 101. http://dx.doi.org/10.12660/bre.v21n12001.3193.

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42

Lemonte, Artur J. "On the gradient statistic under model misspecification." Statistics & Probability Letters 83, no. 1 (2013): 390–98. http://dx.doi.org/10.1016/j.spl.2012.10.008.

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43

Lin, Chang-Yun. "Robust split-plot designs for model misspecification." Journal of Quality Technology 50, no. 1 (2018): 76–87. http://dx.doi.org/10.1080/00224065.2018.1404325.

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44

Venditti, Chris, and Mark Pagel. "MODEL MISSPECIFICATION NOT THE NODE-DENSITY ARTIFACT." Evolution 62, no. 8 (2008): 2125–26. http://dx.doi.org/10.1111/j.1558-5646.2008.00407.x.

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45

Süveges, Mária, and Anthony C. Davison. "Model misspecification in peaks over threshold analysis." Annals of Applied Statistics 4, no. 1 (2010): 203–21. http://dx.doi.org/10.1214/09-aoas292.

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46

Pereira, Tarciana L., and Francisco Cribari-Neto. "Detecting Model Misspecification in Inflated Beta Regressions." Communications in Statistics - Simulation and Computation 43, no. 3 (2013): 631–56. http://dx.doi.org/10.1080/03610918.2012.712183.

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47

Rubtsov, Alexey. "Model misspecification and pricing of illiquid claims." Finance Research Letters 18 (August 2016): 242–49. http://dx.doi.org/10.1016/j.frl.2016.04.023.

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48

Norget, Julia, and Axel Mayer. "Block-Wise Model Fit for Structural Equation Models With Experience Sampling Data." Zeitschrift für Psychologie 230, no. 1 (2022): 47–59. http://dx.doi.org/10.1027/2151-2604/a000482.

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Abstract. Common model fit indices behave poorly in structural equation models for experience sampling data which typically contain many manifest variables. In this article, we propose a block-wise fit assessment for large models as an alternative. The entire model is estimated jointly, and block-wise versions of common fit indices are then determined from smaller blocks of the variance-covariance matrix using simulated degrees of freedom. In a first simulation study, we show that block-wise fit indices, contrary to global fit indices, correctly identify correctly specified latent state-trait

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49

Sias, Richard, Harry J. Turtle, and Blerina Zykaj. "Hedge Fund Return Dependence: Model Misspecification or Liquidity Spirals?" Journal of Financial and Quantitative Analysis 52, no. 5 (2017): 2157–81. http://dx.doi.org/10.1017/s0022109017000679.

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We test whether model misspecification or liquidity spirals primarily explain the observed excess dependence in filtered (for economic fundamentals) hedge fund index returns and the links between volatility, liquidity shocks, and hedge fund return clustering. Evidence supports the model misspecification hypothesis: i) hedge fund filtered return clustering is symmetric, ii) filtered Short Bias fund returns exhibit negative dependence with filtered returns for other hedge fund types, iii) negative liquidity shocks are associated with clustering in both tails and market volatility subsumes the ro

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50

Beierl, Esther T., Markus Bühner, and Moritz Heene. "Is That Measure Really One-Dimensional?" Methodology 14, no. 4 (2018): 188–96. http://dx.doi.org/10.1027/1614-2241/a000158.

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Abstract. Factorial validity is often assessed using confirmatory factor analysis. Model fit is commonly evaluated using the cutoff values for the fit indices proposed by Hu and Bentler (1999) . There is a body of research showing that those cutoff values cannot be generalized. Model fit does not only depend on the severity of misspecification, but also on nuisance parameters, which are independent of the misspecification. Using a simulation study, we demonstrate their influence on measures of model fit. We specified a severe misspecification, omitting a second factor, which signifies factoria

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