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Books on the topic 'Model Misspecification'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Coutts, J. Andrew. Misspecification of the market model: The implications for event studies. Sheffield University, School of Management, 1994.

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2

Bank, European Central, ed. Model misspecification, the equilibrium natural interest rate and the equity premium. European Central Bank, 2007.

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3

Mills, T. C. Misspecification testing and robust estimation of the market model and their implications for event studies. University of Hull. Department of Economics, 1994.

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4

Mills, T. C. Estimating betas for the FT-SE industry baskets: Misspecification testing and robust estimation of the market model. University of Hull, Department of Economics, 1993.

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5

Mills, Terence C. Misspecification testing and robust estimation of the market model: Estimating betas for the FT-SE industry baskets. Sheffield University, School of Management, 1994.

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6

Pollock, D. S. G. The misspecification of dynamic regression models. University of London. Queen Mary and Westfield College. Department of Economics, 1993.

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7

Pollock, S. The misspecification of dynamic regression models. London University, Queen Mary and WestfieldCollege, Department of Economics, 1993.

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8

Smith, Jeremy. Comparing the bias and misspecification in Arfima models. Warwick University, Department of Economics, 1995.

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9

Chen, Chen-Miao Carol. Examining Uncertainty and Misspecification of Attributes in Cognitive Diagnostic Models. [publisher not identified], 2013.

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10

Angrist, Joshua David. Quantile regression under misspecification, with an application to the U.S. wage structure. National Bureau of Economic Research, 2004.

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11

Baldwin, Beatrice Ott. The effects of structural model misspecification and sample size on the robustness of LISREL maximum likelihood parameter estimates. 1987.

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12

McCleary, Richard, David McDowall, and Bradley J. Bartos. Statistical Conclusion Validity. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0006.

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Chapter 6 addresses the sub-category of internal validity defined by Shadish et al., as statistical conclusion validity, or “validity of inferences about the correlation (covariance) between treatment and outcome.” The common threats to statistical conclusion validity can arise, or become plausible through either model misspecification or through hypothesis testing. The risk of a serious model misspecification is inversely proportional to the length of the time series, for example, and so is the risk of mistating the Type I and Type II error rates. Threats to statistical conclusion validity ar

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13

Cai, Zongwu. Functional Coefficient Models for Economic and Financial Data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.6.

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This article discusses the use of functional coefficient models for economic and financial data analysis. It first provides an overview of recent developments in the nonparametric estimation and testing of functional coefficient models, with particular emphasis on the kernel local polynomial smoothing method, before considering misspecification testing as an important econometric question when fitting a functional (varying) coefficient model or a trending time-varying coefficient model. It then describes two major real-life applications of functional coefficient models in economics and finance

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14

Golan, Amos. New Applications Across Disciplines. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199349524.003.0014.

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In this chapter I provide four detailed, cross-disciplinary case studies, developed for this book. The case studies are from the medical sciences, political science, and finance. In each case, I discuss the empirical background, the input information, the necessary analytics, the inferred solution, and a brief summary of its implications. Each problem highlights one basic issue and demonstrates the advantage and applicability of the info-metrics framework. In each one of the problems analyzed, the constraints are specified as stochastic. Thus, the inference tolerates possible misspecification

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15

Golan, Amos. Info-Metrics and Statistical Inference. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199349524.003.0013.

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In this chapter I concentrate on continuous inferential problems: problems where the dependent variable is continuous, such as classical regression problems. As in the previous chapter, using duality theory, I show that the info-metrics framework is general enough to include the class of information-theoretic methods as a special case. The formulation is developed for the classical regression problem, but the results apply to many other problems. A detailed discussion of the benefits and costs of using the info-metrics framework is provided and contrasted with other approaches. I use theoretic

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16

Godfrey, L. G. Misspecification Tests in Econometrics: The Lagrange Multiplier Principle and Other Approaches. Cambridge University Press, 2013.

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17

Godfrey, L. G. Misspecification Tests in Econometrics: The Lagrange Multiplier Principle and Other Approaches (Econometric Society Monographs). Cambridge University Press, 1991.

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18

Godfrey, L. G. Misspecification Tests in Econometrics: The Lagrange Multiplier Principle and Other Approaches (Econometric Society Monographs). Cambridge University Press, 1989.

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19

Effects of misspecification, and strength of signal on the estimation of structural equation models. 1986.

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