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Journal articles on the topic 'Limited dependent variable'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Newey, Whitney K. "Linear instrumental variable estimation of limited dependent variable models with endogenous explanatory variables." Journal of Econometrics 32, no. 1 (June 1986): 127–41. http://dx.doi.org/10.1016/0304-4076(86)90015-1.

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2

Herron, Michael C. "Postestimation Uncertainty in Limited Dependent Variable Models." Political Analysis 8, no. 1 (1999): 83–98. http://dx.doi.org/10.1093/oxfordjournals.pan.a029806.

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Many political science research articles that use limited dependent variable models report estimated quantities, in particular, fitted probabilities, predicted probabilities, and functions of such probabilities, without indicating that such estimates are subject to uncertainty. This practice, along with the reporting of “percentage correctly predicted,” can overstate the precision of reported results. In light of this, the present article describes a variety of measures of uncertainty that authors can include alongside estimates generated by limited dependent variable models. It also proposes an alternative to “percentage correctly predicted” and illustrates its calculations with congressional cosponsorship data from Krehbiel (1995).

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3

Maddala, G. S. "Limited Dependent Variable Models Using Panel Data." Journal of Human Resources 22, no. 3 (1987): 307. http://dx.doi.org/10.2307/145742.

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4

Anderson, G. J. "Prediction tests in limited dependent variable models." Journal of Econometrics 34, no. 1-2 (January 1987): 253–61. http://dx.doi.org/10.1016/0304-4076(87)90074-1.

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5

Lien, Donald, and David Rearden. "Missing measurements in limited dependent variable models." Economics Letters 26, no. 1 (January 1988): 33–36. http://dx.doi.org/10.1016/0165-1765(88)90047-x.

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6

Lee, Lung-fei. "Rational expectations in limited dependent variable models." Economics Letters 46, no. 2 (October 1994): 97–104. http://dx.doi.org/10.1016/0165-1765(94)90003-5.

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7

Newey and Newey. "Semiparametric Estimation of Limited Dependent Variable Models with Endogenous Explanatory Variables." Annales de l'inséé, no. 59/60 (1985): 219. http://dx.doi.org/10.2307/20076564.

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8

Newey, Whitney K. "Efficient estimation of limited dependent variable models with endogenous explanatory variables." Journal of Econometrics 36, no. 3 (November 1987): 231–50. http://dx.doi.org/10.1016/0304-4076(87)90001-7.

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9

Lee, Myoung-jae. "A limited dependent variable model under median rationality." Economics Letters 54, no. 3 (July 1997): 221–25. http://dx.doi.org/10.1016/s0165-1765(97)00033-5.

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10

Caudill, Steven B., and John D. Jackson. "Measuring Marginal Effects in Limited Dependent Variable Models." Statistician 38, no. 3 (1989): 203. http://dx.doi.org/10.2307/2348876.

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11

Wang, Liqun, and Cheng Hsiao. "Two‐stage estimation of limited dependent variable models with errors‐in‐variables." Econometrics Journal 10, no. 2 (June 20, 2007): 426–38. http://dx.doi.org/10.1111/j.1368-423x.2007.00214.x.

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12

Sapra, Sunil K. "INSTRUMENTAL VARIABLE ESTIMATION IN NONLINEAR SIMULTANEOUS EQUATION MODELS WITH LIMITED DEPENDENT VARIABLES." Bulletin of Economic Research 41, no. 4 (October 1989): 275–86. http://dx.doi.org/10.1111/j.1467-8586.1989.tb00343.x.

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13

Robinson, P. M., A. K. Bera, and C. M. Jarque. "Tests for Serial Defendence in Limited Dependent Variable Models." International Economic Review 26, no. 3 (October 1985): 629. http://dx.doi.org/10.2307/2526708.

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14

VEALL, MICHAEL R., and KLAUS F. ZIMMERMANN. "PSEUDO-R2MEASURES FOR SOME COMMON LIMITED DEPENDENT VARIABLE MODELS." Journal of Economic Surveys 10, no. 3 (September 1996): 241–59. http://dx.doi.org/10.1111/j.1467-6419.1996.tb00013.x.

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15

FRY, T. R. L., R. D. BROOKS, BR COMLEY, and J. ZHANG. "Economic Motivations for Limited Dependent and Qualitative Variable Models*." Economic Record 69, no. 2 (June 1993): 193–205. http://dx.doi.org/10.1111/j.1475-4932.1993.tb01813.x.

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16

MONOKROUSSOS, GEORGE. "Dynamic Limited Dependent Variable Modeling and U.S. Monetary Policy." Journal of Money, Credit and Banking 43, no. 2-3 (March 2011): 519–34. http://dx.doi.org/10.1111/j.1538-4616.2010.00383.x.

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17

LeSage, James P. "Bayesian Estimation of Limited Dependent Variable Spatial Autoregressive Models." Geographical Analysis 32, no. 1 (September 3, 2010): 19–35. http://dx.doi.org/10.1111/j.1538-4632.2000.tb00413.x.

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18

Lemay, Valerie M., Antal Kozak, and Peter L. Marshall. "Using limited dependent variable estimators for estimating percent decay." Canadian Journal of Forest Research 23, no. 2 (February 1, 1993): 266–74. http://dx.doi.org/10.1139/x93-036.

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The data used for the estimation of percent decay are bounded by zero and 100. Because a value of 100% indicates that the tree is completely decayed, this value is not observable in nature. However, a value of zero percent is often observed over a wide range of the independent variables. The distribution of percent decay is a combination of a truncated continuous distribution for percent decay greater than zero and a discrete component for the zero percents. The use of ordinary least squares with this type of data results in biased and inconsistent estimates of the coefficients of a percent decay equation. An alternative is the tobit estimator (a combined regression and probit estimator based on a maximum likelihood equation), which results in consistent estimates of the coefficients if the error terms of the model are independent and identically distributed as the truncated normal distribution. A Monte Carlo simulation using data for three species with different proportions of zero percents was performed to compare the ordinary least squares and tobit estimators. As expected, the tobit estimator resulted in quite different estimates of the coefficients of the equations than did ordinary least squares. An unexpected result was that the estimated expected percent decay was slightly more biased for the tobit estimator than for the ordinary least squares estimator, even with a large number of zero percents in the data set. Possible explanations for the Monte Carlo simulation results and recommendations for fitting percent decay equations are given in the paper.

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19

Spirling, Arthur. "Bayesian Approaches for Limited Dependent Variable Change Point Problems." Political Analysis 15, no. 4 (2007): 387–405. http://dx.doi.org/10.1093/pan/mpm022.

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Limited dependent variable (LDV) data are common in political science, and political methodologists have given much good advice on dealing with them. We review some methods for LDV “change point problems” and demonstrate the use of Bayesian approaches for count, binary, and duration-type data. Our applications are drawn from American politics, Comparative politics, and International Political Economy. We discuss the tradeoffs both philosophically and computationally. We conclude with possibilities for multiple change point work.

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20

Robinson, P. M. "Estimation of disequilibrium and limited dependent variable models with serially dependent residuals." Economics Letters 23, no. 1 (January 1987): 53–57. http://dx.doi.org/10.1016/0165-1765(87)90201-1.

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21

Angrist, Joshua D. "Estimation of Limited Dependent Variable Models With Dummy Endogenous Regressors." Journal of Business & Economic Statistics 19, no. 1 (January 2001): 2–28. http://dx.doi.org/10.1198/07350010152472571.

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22

Magnusson, Leandro M. "Inference in limited dependent variable models robust to weak identification." Econometrics Journal 13, no. 3 (September 21, 2010): S56—S79. http://dx.doi.org/10.1111/j.1368-423x.2009.00309.x.

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23

Alava, Mónica Hernández, and Allan Wailoo. "Fitting Adjusted Limited Dependent Variable Mixture Models to EQ-5D." Stata Journal: Promoting communications on statistics and Stata 15, no. 3 (October 2015): 737–50. http://dx.doi.org/10.1177/1536867x1501500307.

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24

Blundell, R. W., and R. J. Smith. "Estimation in a Class of Simultaneous Equation Limited Dependent Variable Models." Review of Economic Studies 56, no. 1 (January 1, 1989): 37–57. http://dx.doi.org/10.2307/2297748.

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25

Lee, Myoung-Jae. "Semiparametric Estimators for Limited Dependent Variable (LDV) Models with Endogenous Regressors." Econometric Reviews 31, no. 2 (March 2012): 171–214. http://dx.doi.org/10.1080/07474938.2011.607101.

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26

Bowen, Harry P. "Testing Moderating Hypotheses in Limited Dependent Variable and Other Nonlinear Models." Journal of Management 38, no. 3 (June 11, 2010): 860–89. http://dx.doi.org/10.1177/0149206309356324.

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27

Yen, Steven T., and Andrew M. Jones. "Individual cigarette consumption and addiction: A flexible limited dependent variable approach." Health Economics 5, no. 2 (March 1996): 105–17. http://dx.doi.org/10.1002/(sici)1099-1050(199603)5:2<105::aid-hec188>3.0.co;2-i.

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28

Ruud, Paul A. "Consistent estimation of limited dependent variable models despite misspecification of distribution." Journal of Econometrics 32, no. 1 (June 1986): 157–87. http://dx.doi.org/10.1016/0304-4076(86)90017-5.

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29

Smith, Richard J. "Testing the normality assumption in multivariate simultaneous limited dependent variable models." Journal of Econometrics 34, no. 1-2 (January 1987): 105–23. http://dx.doi.org/10.1016/0304-4076(87)90069-8.

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30

Pinkse, C. A. P. "On the computation of semiparametric estimates in limited dependent variable models." Journal of Econometrics 58, no. 1-2 (July 1993): 185–205. http://dx.doi.org/10.1016/0304-4076(93)90118-o.

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31

Laitila, Thomas. "A pseudo-R2 measure for limited and qualitative dependent variable models." Journal of Econometrics 56, no. 3 (April 1993): 341–55. http://dx.doi.org/10.1016/0304-4076(93)90125-o.

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32

Čížek, Pavel. "GENERAL TRIMMED ESTIMATION: ROBUST APPROACH TO NONLINEAR AND LIMITED DEPENDENT VARIABLE MODELS." Econometric Theory 24, no. 6 (July 9, 2008): 1500–1529. http://dx.doi.org/10.1017/s0266466608080596.

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High-breakdown-point regression estimators protect against large errors and data contamination. We generalize the concept of trimming used by many of these robust estimators, such as the least trimmed squares and maximum trimmed likelihood, and propose a general trimmed estimator, which renders robust estimators applicable far beyond the standard (non)linear regression models. We derive here the consistency and asymptotic distribution of the proposed general trimmed estimator under mild β-mixing conditions and demonstrate its applicability in nonlinear regression and limited dependent variable models.

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33

Wang, Liqun. "A simple adjustment for measurement errors in some limited dependent variable models." Statistics & Probability Letters 58, no. 4 (July 2002): 427–33. http://dx.doi.org/10.1016/s0167-7152(02)00165-7.

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34

Vijverberg, Wim P. M. "NON-NORMALITY AS DISTRIBUTIONAL MISSPECIFICATION IN SINGLE-EQUATION LIMITED DEPENDENT VARIABLE MODELS." Oxford Bulletin of Economics and Statistics 49, no. 4 (May 1, 2009): 417–30. http://dx.doi.org/10.1111/j.1468-0084.1987.mp49004005.x.

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35

Matzkin, Rosa L. "Identification in nonparametric limited dependent variable models with simultaneity and unobserved heterogeneity." Journal of Econometrics 166, no. 1 (January 2012): 106–15. http://dx.doi.org/10.1016/j.jeconom.2011.06.009.

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36

Smith, Richard J. "On the Use of Distributional Mis-Specification Checks in Limited Dependent Variable Models." Economic Journal 99, no. 395 (1989): 178. http://dx.doi.org/10.2307/2234079.

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37

Hsiao, Cheng, M. Hashem Pesaran, and Andreas Pick. "Diagnostic Tests of Cross-section Independence for Limited Dependent Variable Panel Data Models*." Oxford Bulletin of Economics and Statistics 74, no. 2 (July 5, 2011): 253–77. http://dx.doi.org/10.1111/j.1468-0084.2011.00646.x.

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38

Wiersema, Margarethe F., and Harry P. Bowen. "The use of limited dependent variable techniques in strategy research: issues and methods." Strategic Management Journal 30, no. 6 (June 2009): 679–92. http://dx.doi.org/10.1002/smj.758.

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39

Fosu, Augustin Kwasi. "Nonwage benefits as a limited-dependent variable: Implications for the impact of unions." Journal of Labor Research 14, no. 1 (March 1993): 29–43. http://dx.doi.org/10.1007/bf02685535.

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40

Donald, Stephen G., and G. S. Maddala. "A note on the estimation of limited dependent variable models under rational expectations." Economics Letters 38, no. 1 (January 1992): 17–23. http://dx.doi.org/10.1016/0165-1765(92)90155-r.

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41

Dejong, D. N. "Bayesian inference in limited dependent variable models. An application to measuring strike duration." Journal of Applied Econometrics 8, no. 2 (April 1993): 115–28. http://dx.doi.org/10.1002/jae.3950080202.

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42

Silvapulle, Mervyn J. "On Limited Dependent Variable Models: Maximum Likelihood Estimation and Test of One-sided Hypothesis." Econometric Theory 7, no. 3 (September 1991): 385–95. http://dx.doi.org/10.1017/s0266466600004527.

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The limited dependent variable models with errors having log-concave density functions are studied here. For such models with normal errors, the asymptotic normality of the maximum likelihood estimator was established by Amemiya [1]. We show, when the density of the error distribution is log-concave, that the maximum likelihood estimator exists with arbitrarily large probability for large sample sizes, and is asymptotically normal. The general theory presented here includes the important special cases of normal, logistic, and extreme value error distributions. The main results are established under rather weak conditions. It is also shown that, under the null hypothesis, the asymptotic distribution of the likelihood ratio statistic for testing a one-sided alternative hypothesis is a weighted sum of chi-squares.

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43

Börsch-Supan, Axel, and Vassilis A. Hajivassiliou. "Smooth unbiased multivariate probability simulators for maximum likelihood estimation of limited dependent variable models." Journal of Econometrics 58, no. 3 (August 1993): 347–68. http://dx.doi.org/10.1016/0304-4076(93)90049-b.

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44

Traynor, Thomas L., and Patrick S. McCarthy. "Economic regulation and highway safety in the trucking industry: A limited dependent variable analysis." Quarterly Review of Economics and Finance 33, no. 2 (June 1993): 141–53. http://dx.doi.org/10.1016/1062-9769(93)90019-g.

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45

Rabe-Hesketh, Sophia, Anders Skrondal, and Andrew Pickles. "Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects." Journal of Econometrics 128, no. 2 (October 2005): 301–23. http://dx.doi.org/10.1016/j.jeconom.2004.08.017.

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46

Smith, Richard J. "Testing for exogeneity in limited dependent variable models using a simplified likelihood ratio statistic." Journal of Applied Econometrics 2, no. 3 (July 1987): 237–45. http://dx.doi.org/10.1002/jae.3950020307.

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47

NIKOLOVA, Lydia, Kizhake SOMAN, Jeffry C. NICHOLS, D. Sundarsingh DANIEL, Burton F. DICKEY, and Simon HOFFENBERG. "Conformationally variable Rab protein surface regions mapped by limited proteolysis and homology modelling." Biochemical Journal 336, no. 2 (December 1, 1998): 461–69. http://dx.doi.org/10.1042/bj3360461.

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Tryptic proteolysis of the small GTPases Rab4 and Rab5 is a multi-step, nucleotide-dependent process. Using N-terminal peptide sequencing, matrix-assisted laser desorption ionization–time-of-flight MS and molecular modelling, we identified the three initial sites of proteolysis in Rab5 as Arg-4, Arg-81 and Arg-197. Arg-4 and Arg-81 lie within regions previously implicated in Rab5 endocytic function, and Arg-197 lies in a region involved in membrane targeting. Topologically, Arg-81 lies within the conformationally variable Switch II region shown to be important for protein–protein interactions of other GTPases. Homology modelling studies on Rab5 indicate that the Arg-81 side chain is buried in the Rab5 GTP conformation, but is solvent-accessible in the GDP conformation, explaining the dependence of proteolysis on nucleotides. Peptide mapping of Rab4 was performed to take advantage of additional scissile bonds within Switch II to determine more precisely the limits of the nucleotide-dependent protease-accessible region. The Rab4 cleavage sites corresponded to Arg-81 and Pro-87 of Rab5, and taken together with the finding that Rab5 was not cleaved at Arg-91 this analysis defines an eight-residue surface-exposed conformationally variable region lying in the centre of Switch II. A sequence comparison of Rab proteins shows these eight residues to have a loosely conserved motif that we term Switch II(v) for its relative variability. C-terminal to Switch II(v) is a highly conserved Rab-specific YYRGA motif that we term Switch II(c) for its constant sequence. N-terminal to Switch II(v) is a sequence-invariant G-domain involved in nucleotide binding and hydrolysis. We propose that the Rab Switch II(v) region imparts specificity to nucleotide-dependent protein–protein interactions.

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48

Handcock, Mark S., Michael S. Rendall, and Jacob E. Cheadle. "5. Improved Regression Estimation of a Multivariate Relationship with Population Data on the Bivariate Relationship." Sociological Methodology 35, no. 1 (August 2005): 291–334. http://dx.doi.org/10.1111/j.0081-1750.2006.00170.x.

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Regression coefficients specify the partial effect of a regressor on the dependent variable. Sometimes the bivariate or limited multivariate relationship of that regressor variable with the dependent variable is known from population-level data. We show here that such population-level data can be used to reduce variance and bias about estimates of those regression coefficients from sample survey data. The method of constrained MLE is used to achieve these improvements. Its statistical properties are first described. The method constrains the weighted sum of all the covariate-specific associations (partial effects) of the regressors on the dependent variable to equal the overall association of one or more regressors, where the latter is known exactly from the population data. We refer to those regressors whose bivariate or limited multivariate relationships with the dependent variable are constrained by population data as being “directly constrained.” Our study investigates the improvements in the estimation of directly constrained variables as well as the improvements in the estimation of other regressor variables that may be correlated with the directly constrained variables, and thus “indirectly constrained” by the population data. The example application is to the marital fertility of black versus white women. The difference between white and black women's rates of marital fertility, available from population-level data, gives the overall association of race with fertility. We show that the constrained MLE technique both provides a far more powerful statistical test of the partial effect of being black and purges the test of a bias that would otherwise distort the estimated magnitude of this effect. We find only trivial reductions, however, in the standard errors of the parameters for indirectly constrained regressors.

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49

Billé, Anna Gloria, and Giuseppe Arbia. "SPATIAL LIMITED DEPENDENT VARIABLE MODELS: A REVIEW FOCUSED ON SPECIFICATION, ESTIMATION, AND HEALTH ECONOMICS APPLICATIONS." Journal of Economic Surveys 33, no. 5 (July 29, 2019): 1531–54. http://dx.doi.org/10.1111/joes.12333.

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50

Wailoo, A. J., M. Hernández, and R. Ara. "MA4 Modelling EQ-5D Health State Values: Developing A Limited Dependent Variable, Mixture Modeling Approach." Value in Health 14, no. 7 (November 2011): A242. http://dx.doi.org/10.1016/j.jval.2011.08.062.

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