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Journal articles on the topic 'Panel probit'

Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

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1

Halistin, Halistin, Vita Ratnasari, Santi Puteri Rahayu, and Tandri Patih. "ESTIMASI PARAMETER MODEL PROBIT PADA DATA PANEL MENGGUNAKAN OPTIMASI BFGS." BAREKENG: Jurnal Ilmu Matematika dan Terapan 14, no. 2 (2020): 167–80. http://dx.doi.org/10.30598/barekengvol14iss2pp167-180.

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Salah satu model yang dapat menjelaskan pola hubungan antara variabel dependen yang bersifat kategorik dengan variabel independen adalah regresi probit. Dalam regresi probit, variabel independen dapat bersifat kategorik atau kontinu. Regresi probit mrnggunakan fungsi link dari distribusi normal standar. Jika pemodelan regresi probit melibatkan data silang dan deret waktu, disebut model probit data panel. Estimasi parameter model probit data panel random effect menggunakan maximum likelihood estimation (MLE) dengan pendekatan Gauss Hermite Quadrature. Proses iterasi menggunakan metode BFGS. Met

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2

Harris, Mark N., Mark Rogers, and Anthony Siouclis. "Modelling firm innovation using panel probit estimators." Applied Economics Letters 10, no. 11 (2003): 683–86. http://dx.doi.org/10.1080/1350485032000138999.

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3

Bertschek, Irene, and Michael Lechner. "Convenient estimators for the panel probit model." Journal of Econometrics 87, no. 2 (1998): 329–71. http://dx.doi.org/10.1016/s0304-4076(98)00008-6.

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4

Antunes, António, Diana Bonfim, Nuno Monteiro, and Paulo M. M. Rodrigues. "Forecasting banking crises with dynamic panel probit models." International Journal of Forecasting 34, no. 2 (2018): 249–75. http://dx.doi.org/10.1016/j.ijforecast.2017.12.003.

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5

Cruz-Gonzalez, Mario, Iván Fernández-Val, and Martin Weidner. "Bias Corrections for Probit and Logit Models with Two-way Fixed Effects." Stata Journal: Promoting communications on statistics and Stata 17, no. 3 (2017): 517–45. http://dx.doi.org/10.1177/1536867x1701700301.

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In this article, we present the user-written commands probitfe and logitfe, which fit probit and logit panel-data models with individual and time unobserved effects. Fixed-effects panel-data methods that estimate the unobserved effects can be severely biased because of the incidental parameter problem (Neyman and Scott, 1948, Econometrica 16: 1–32). We tackle this problem using the analytical and jackknife bias corrections derived in Fernández-Val and Weidner (2016, Journal of Econometrics 192: 291–312) for panels where the two dimensions ( N and T) are moderately large. We illustrate the comm

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6

Lee, Seung-Chun. "A Bayesian inference for fixed effect panel probit model." Communications for Statistical Applications and Methods 23, no. 2 (2016): 179–87. http://dx.doi.org/10.5351/csam.2016.23.2.179.

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7

Amisano, Gianni, and Maria Letizia Giorgetti. "ENTRY INTO PHARMACEUTICAL SUBMARKETS: A BAYESIAN PANEL PROBIT ANALYSIS." Journal of Applied Econometrics 28, no. 4 (2012): 667–701. http://dx.doi.org/10.1002/jae.2267.

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8

Greene, William. "Convenient estimators for the panel probit model: Further results." Empirical Economics 29, no. 1 (2004): 21–47. http://dx.doi.org/10.1007/s00181-003-0187-z.

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9

Laisney, François, and Michael Lechner. "Combining Panel Data and Macro Information for the Estimation of a Panel Probit Model." Journal of Contextual Economics – Schmollers Jahrbuch 116, no. 3 (1996): 339–58. http://dx.doi.org/10.3790/schm.116.3.339.

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10

Stegmueller, Daniel. "Modeling Dynamic Preferences: A Bayesian Robust Dynamic Latent Ordered Probit Model." Political Analysis 21, no. 3 (2013): 314–33. http://dx.doi.org/10.1093/pan/mpt001.

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Much politico-economic research on individuals' preferences is cross-sectional and does not model dynamic aspects of preference or attitude formation. I present a Bayesian dynamic panel model, which facilitates the analysis of repeated preferences using individual-level panel data. My model deals with three problems. First, I explicitly include feedback from previous preferences taking into account that available survey measures of preferences are categorical. Second, I model individuals' initial conditions when entering the panel as resulting from observed and unobserved individual attributes

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11

Lechner, Michael. "Some Specification Tests for Probit Models Estimated on Panel Data." Journal of Business & Economic Statistics 13, no. 4 (1995): 475. http://dx.doi.org/10.2307/1392393.

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12

Lechner, Michael. "Some Specification Tests for Probit Models Estimated on Panel Data." Journal of Business & Economic Statistics 13, no. 4 (1995): 475–88. http://dx.doi.org/10.1080/07350015.1995.10524621.

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13

Spiess, Martin. "Evaluation of a pseudo-R2 measure for panel probit models." British Journal of Mathematical and Statistical Psychology 54, no. 2 (2001): 325–33. http://dx.doi.org/10.1348/000711001159591.

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14

Sutradhar, Brajendra C., Wasimul Bari, and Kalyan Das. "On probit versus logit dynamic mixed models for binary panel data." Journal of Statistical Computation and Simulation 80, no. 4 (2010): 421–41. http://dx.doi.org/10.1080/00949650802695656.

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15

Alexander, Blair, and Robert Breunig. "A Monte Carlo study of bias corrections for panel probit models." Journal of Statistical Computation and Simulation 86, no. 1 (2014): 74–90. http://dx.doi.org/10.1080/00949655.2014.994516.

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16

Wawro, Gregory. "A Panel Probit Analysis of Campaign Contributions and Roll-Call Votes." American Journal of Political Science 45, no. 3 (2001): 563. http://dx.doi.org/10.2307/2669239.

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17

Tugba, Dayioglu. "Determinants of Sovereign Ratings in Emerging Countries with Panel Probit Analysis." International Journal of Engineering Management 3, no. 1 (2019): 6. http://dx.doi.org/10.11648/j.ijem.20190301.12.

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18

Laisney, François, and Michael Lechner. "Almost Consistent Estimation of Panel Probit Models with “Small” Fixed Effects." Econometric Reviews 22, no. 1 (2003): 1–28. http://dx.doi.org/10.1081/etc-120017972.

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19

Harmáček, Jaromír, Miroslav Syrovátka, and Zdeněk Opršal. "Factors of Czech Aid Selection and Allocation: Panel Probit and Tobit Analysis." Politická ekonomie 65, no. 2 (2017): 179–97. http://dx.doi.org/10.18267/j.polek.1135.

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20

Mora, Jhon James. "Un análisis dinámico del efecto de las ganancias fortuitas sobre la participación laboral." Lecturas de Economía, no. 66 (October 23, 2009): 213–24. http://dx.doi.org/10.17533/udea.le.n66a2605.

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Este artículo discute el efecto negativo de las herencias, regalos o loterías, que pueden pensarse como ganancias fortuitas, sobre la participación laboral. En la literatura, este efecto se conoce también como la hipótesis de Carnegie. Para analizar dicho efecto se toma la población de mujeres en España durante el periodo 1994-2000. Los resultados con datos de panel para un probit dinámico muestran que existe una relación negativa y significativa entre la participación laboral y las ganancias fortuitas corroborando los resultados de Holtz-Eakin et al. (1993) para Estados Unidos. Palabras clave

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21

D.V., Domashova, Kripak Е.М., Pisarchik Е.Е., and Ulanova Zh.U. "Modeling of Credit Institution License Withdrawal Based on Panel Data." KnE Social Sciences 3, no. 2 (2018): 309. http://dx.doi.org/10.18502/kss.v3i2.1558.

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The research paper involves the construction of panel data binary response models to forecast the probability of a credit institution's license withdrawal based on its financial performance, including the construction of logit and probit models using various sets of source data offers a technique for shaping a general model. Keywords: credit institutions, panel data, binary response models, license withdrawal

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22

Gao, Wei, Wicher Bergsma, and Qiwei Yao. "Estimation for Dynamic and Static Panel Probit Models with Large Individual Effects." Journal of Time Series Analysis 38, no. 2 (2016): 266–84. http://dx.doi.org/10.1111/jtsa.12178.

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23

Wooldridge, Jeffrey M., and Ying Zhu. "Inference in Approximately Sparse Correlated Random Effects Probit Models With Panel Data." Journal of Business & Economic Statistics 38, no. 1 (2019): 1–18. http://dx.doi.org/10.1080/07350015.2019.1681276.

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24

Fong, Duncan K. H., Sunghoon Kim, Zhe Chen, and Wayne S. DeSarbo. "A Bayesian Multinomial Probit MODEL FOR THE ANALYSIS OF PANEL CHOICE DATA." Psychometrika 81, no. 1 (2014): 161–83. http://dx.doi.org/10.1007/s11336-014-9437-6.

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25

Aßmann, Christian, and Jens Boysen-Hogrefe. "A Bayesian approach to model-based clustering for binary panel probit models." Computational Statistics & Data Analysis 55, no. 1 (2011): 261–79. http://dx.doi.org/10.1016/j.csda.2010.04.016.

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26

Contoyannis, Paul, Andrew M. Jones, and Nigel Rice. "Simulation-based inference in dynamic panel probit models: An application to health." Empirical Economics 29, no. 1 (2004): 49–77. http://dx.doi.org/10.1007/s00181-003-0189-x.

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27

Kao, Chihwa, and John F. Schnell. "Errors in variables in a random-effects probit model for panel data." Economics Letters 24, no. 4 (1987): 339–42. http://dx.doi.org/10.1016/0165-1765(87)90068-1.

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28

Arentze, Theo, Aloys Borgers, Maarten Ponjé, and Harry Timmermans. "Determinants of Attrition Rates in Two-Wave, Two-Day Household Activity Diary: Probit Analysis." Transportation Research Record: Journal of the Transportation Research Board 1719, no. 1 (2000): 159–64. http://dx.doi.org/10.3141/1719-21.

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Transportation researchers often have used panel data to examine the dynamics of travel behavior. Although panel data are probably the best means by which to better understand choice dynamics, they present various challenges to the analyst, with attrition or dropout one of the most critical. Presented are the results of an analysis of the determinants of attrition in a two-wave panel, conducted in the Netherlands. Panelists reported their activities for a consecutive 2 days with 1 year between. Attrition rates between the two waves were predicted as a function of sociodemographics; number of a

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29

Colares, Juscelino F. "An Empirical Examination of Product and Litigant–Specific Theories for the Divergence Between NAFTA Chapter 19 and US Judicial Review." Journal of World Trade 42, Issue 4 (2008): 691–722. http://dx.doi.org/10.54648/trad2008030.

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Empirical analysis of NAFTA panel review has shown that panels reverse US agency trade remedy determinations twice as often as US courts. Recent studies have eliminated case selection and other hypotheses as potential explanations for this divergence. In this article, Probit regressions show that case docket differences, such as type of import or litigant identity, also cannot account for this discrepancy. As NAFTA panels must apply the same law and standards of review as the US courts they replace, this divergence presents serious questions regarding US Congressional acquiescence to the opera

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30

Ogg, Clyde L., and Roger E. Gold. "Laboratory Evaluation of TD-2222 and Chlorpyrifos Against the German Cockroach 1986." Insecticide and Acaricide Tests 13, no. 1 (1988): 390. http://dx.doi.org/10.1093/iat/13.1.390.

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Abstract Efficacy of TD-2222 (24.4% A.I.), an experimental microencapsulated flowable insecticide and 2 formulations of Chlorpyrifos were evaluated on painted white pine panels. The Chlorpyrifos formulations were Duraban ME (11.7% A.I.) and Dursban 4E (44.4% A.I.)- Pin e blocks (232.25 cm2) were cut, painted with 2 coats of semigloss latex paint, and allowed to dry. A 4 mm-deep circular groove was cut into the surface of the wood, so that an inverted plastic Petri dish cover (92 mm diam) could effectively contain cockroaches on the surface. The test surfaces were prepared by applying a 1 ml wa

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31

Elrod, Terry, and Michael P. Keane. "A Factor-Analytic Probit Model for Representing the Market Structure in Panel Data." Journal of Marketing Research 32, no. 1 (1995): 1. http://dx.doi.org/10.2307/3152106.

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32

Poyraz, Gulden. "Modelling an early warning system for currency crises: a dynamic panel probit model." Pressacademia 8, no. 3 (2019): 181–87. http://dx.doi.org/10.17261/pressacademia.2019.1125.

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33

Lee, Seung-Chun. "A computational note on maximum likelihood estimation in random effects panel probit model." Communications for Statistical Applications and Methods 26, no. 3 (2019): 315–23. http://dx.doi.org/10.29220/csam.2019.26.3.315.

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34

Elrod, Terry, and Michael P. Keane. "A Factor-Analytic Probit Model for Representing the Market Structure in Panel Data." Journal of Marketing Research 32, no. 1 (1995): 1–16. http://dx.doi.org/10.1177/002224379503200103.

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35

Hasegawa, Hikaru. "Bayesian Dynamic Panel-Ordered Probit Model and Its Application to Subjective Well-Being." Communications in Statistics - Simulation and Computation 38, no. 6 (2009): 1321–47. http://dx.doi.org/10.1080/03610910902903133.

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36

Fernández-Val, Iván. "Fixed effects estimation of structural parameters and marginal effects in panel probit models." Journal of Econometrics 150, no. 1 (2009): 71–85. http://dx.doi.org/10.1016/j.jeconom.2009.02.007.

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37

Zhang, Xiao-Bing, and Sied Hassen. "Household fuel choice in urban China: evidence from panel data." Environment and Development Economics 22, no. 4 (2017): 392–413. http://dx.doi.org/10.1017/s1355770x17000092.

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AbstractUsing eight rounds of household survey data that span two decades, this paper analyzes the determinants of household fuel choice in urban China. Using the correlated random effects generalized ordered probit model, the authors find that household fuel choice in urban China is related to fuel prices, households’ economic status and size and household head's gender and education. The results suggest that policies and interventions that increase households’ income, reduce the price advantage of dirty fuels (e.g., taxing coal) and empower women in the household are of great significance in

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38

Laisney, François, and Michael Lechner. "Utilisation jointe de données de panel et d'informations agrégées dans l'estimation d'un modèle Probit." Économie & prévision 126, no. 5 (1996): 177–89. http://dx.doi.org/10.3406/ecop.1996.5830.

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39

Lee, Hyejin, and Minjung Kyung. "Korean Welfare Panel Data: A Computational Bayesian Method for Ordered Probit Random Effects Models." Communications for Statistical Applications and Methods 21, no. 1 (2014): 45–60. http://dx.doi.org/10.5351/csam.2014.21.1.045.

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40

Romano, Antonio Angelo, Giuseppe Scandurra, and Alfonso Carfora. "Probabilities to adopt feed in tariff by OPEC countries using a panel probit model." Energy Systems 7, no. 3 (2015): 449–68. http://dx.doi.org/10.1007/s12667-015-0173-5.

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41

PFARR, CHRISTIAN, and UDO SCHNEIDER. "Choosing between subsidized or unsubsidized private pension schemes: evidence from German panel data." Journal of Pension Economics and Finance 12, no. 1 (2012): 62–91. http://dx.doi.org/10.1017/s1474747212000170.

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AbstractSince 2002, the German government has been attempting to increase private old-age provisions by introducing incentives such as supplementary subsidies and tax credits. Since then, the so-called ‘Riester pension’ has grown in popularity. Apart from subsidized pension plans, unsubsidized private pension insurances have – already in the past – been a very important instrument among old-age provision schemes. With data of the German SAVE study for the years 2005–2009, we analyze whether the decision for a ‘Riester pension’ is independent of the decision for unsubsidized private pension ins

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42

Sa’idu, Bello Malam. "Poverty, Inequality and Millennium Development Goals’ Expenditure: A Probabilistic Linkage." International Journal of Management and Sustainability 3, no. 11 (2015): 653–63. http://dx.doi.org/10.18488/journal.11/2014.3.11/11.11.653.663.

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The objective of this paper is to investigate the linkage between poverty, inequality and Millennium Development Goals’ (MDGs) expenditure. To achieve the set objective, probit and logit models were empirically employed using a panel data series. The results revealed that a unit increase in expenditure on MDGs would lead to increase in poverty by a single digit and income inequality by double digits. This is not to blame the MDG funding or discourage it. Plausibly the expenditure on MDGs has been constrained due to technical, managerial, institutional, macro-economic imbalances, and policy bot

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43

Sun, Yutao, and Geert Dhaene. "xtspj: A command for split-panel jackknife estimation." Stata Journal: Promoting communications on statistics and Stata 19, no. 2 (2019): 335–74. http://dx.doi.org/10.1177/1536867x19854016.

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In this article, we present a new command, xtspj, that corrects for incidental parameter bias in panel-data models with fixed effects. The correction removes the first-order bias term of the maximum likelihood estimate using the split-panel jackknife method. Two variants are implemented: the jackknifed maximum-likelihood estimate and the jackknifed log-likelihood function (with corresponding maximizer). The model may be nonlinear or dynamic, and the covariates may be predetermined instead of strictly exogenous. xtspj implements the split-panel jackknife for fixed-effects versions of linear, pr

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44

Spinelli, Daniele. "Fitting spatial autoregressive logit and probit models using Stata: The spatbinary command." Stata Journal: Promoting communications on statistics and Stata 22, no. 2 (2022): 293–318. http://dx.doi.org/10.1177/1536867x221106373.

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Starting from version 15, Stata allows users to manage data and fit regressions accounting for spatial relationships through the sp commands. Spatial regressions can be estimated using the spregress, spxtregress, and spivregress commands. These commands allow users to fit spatial autoregressive models in cross-sectional and panel data. However, they are designed to estimate regressions with continuous dependent variables. Although binary spatial regressions are important in applied econometrics, they cannot be estimated in Stata. Therefore, I introduce spatbinary, a Stata command that allows u

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45

Karoly, Lynn A., and Jeannette A. Rogowski. "The Effect of Access to Post-Retirement Health Insurance on the Decision to Retire Early." ILR Review 48, no. 1 (1994): 103–23. http://dx.doi.org/10.1177/001979399404800108.

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The authors analyze the effect of the availability of post-retirement health insurance on early retirement behavior of men using data from the 1984, 1986, and 1988 panels of the Survey of Income and Program Participation (SIPP). They extend previous static models of retirement to account for access to health insurance as a factor in the retirement decision. The estimates from probit models of retirement during the SIPP panel period show that the offer of continued employer-provided health insurance coverage after retirement increased the likelihood of retirement before age 65. Also, the author

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46

Latif, Ehsan. "Health Outcomes Before, During and After the Great Recession in Canada: A Longitudinal Analysis." Review of Economic Analysis 12, no. 1 (2020): 59–72. http://dx.doi.org/10.15353/rea.v12i1.1759.

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Using panel data from Canadian National Population Health Survey (2006-2011), this study examined what happened to individuals’ self-reported health during and in the aftermath of great recession of 2008-2009. The study used different methods such as ordered probit method, ordinary least square method and panel fixed effects method. Once individual specific fixed effects are controlled for, the results show that great recession had no statistically significant detrimental impact on self-reported health status. The results further show that health status in the aftermath of great recession did

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47

Baltagi, Badi H., Peter H. Egger, and Michaela Kesina. "Generalized spatial autocorrelation in a panel-probit model with an application to exporting in China." Empirical Economics 55, no. 1 (2018): 193–211. http://dx.doi.org/10.1007/s00181-017-1409-0.

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48

Bijwaard, Govert E., Rob Alessie, Viola Angelini, and L. H. Lumey. "Physical and psychological health at adolescence and home care use later in life." PLOS ONE 16, no. 12 (2021): e0261078. http://dx.doi.org/10.1371/journal.pone.0261078.

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Objectives To examine the relation between physical and psychological health indicators at adolescence (age 18) and household, personal, and nursing home care use later in life at ages 57–69 years. Methods Using medical examinations on men born in 1944–1947 who were evaluated for military service at age 18 in the Netherlands, we link physical and psychological health assessments to national administrative microdata on the use of home care services at ages 57–69 years. We postulate a panel probit model for home care use over these years. In the analyses, we account for selective survival throug

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49

Dudbridge, Frank. "Criteria for evaluating risk prediction of multiple outcomes." Statistical Methods in Medical Research 29, no. 12 (2020): 3492–510. http://dx.doi.org/10.1177/0962280220929039.

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Risk prediction models have been developed in many contexts to classify individuals according to a single outcome, such as risk of a disease. Emerging “-omic” biomarkers provide panels of features that can simultaneously predict multiple outcomes from a single biological sample, creating issues of multiplicity reminiscent of exploratory hypothesis testing. Here I propose definitions of some basic criteria for evaluating prediction models of multiple outcomes. I define calibration in the multivariate setting and then distinguish between outcome-wise and individual-wise prediction, and within th

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50

Cinaroglu, Songul. "Interaction Between Self-rated Health and Labour Force Participation: A Panel Data Probit Model with Survival Estimates." Journal of Health Management 23, no. 4 (2021): 594–613. http://dx.doi.org/10.1177/09720634211050483.

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This study aimed to reveal the relationship between health and labour in Turkey under the intervention of demographic variables. Four waves of the TurkStat Income and Living Conditions Panel Survey (2008–2011) were used. Demographic, education, self-rated health and labour force participation indicators were used to examine different generalised linear model (GLM)-like panel binomial probit model specifications using self-assessed health (SAH) status and self-reported working status (SRWS) as dependent variables. Kaplan–Meier (KM) estimates for the probability of survival in SAH and SRWS were

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