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Journal articles on the topic 'Semiparametric dynamics'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

Park, Byeong U., Enno Mammen, Wolfgang Härdle, and Szymon Borak. "Time Series Modelling With Semiparametric Factor Dynamics." Journal of the American Statistical Association 104, no. 485 (March 2009): 284–98. http://dx.doi.org/10.1198/jasa.2009.0105.

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2

Yang, Lijian, and Rolf Tschernig. "NON- AND SEMIPARAMETRIC IDENTIFICATION OF SEASONAL NONLINEAR AUTOREGRESSION MODELS." Econometric Theory 18, no. 6 (September 24, 2002): 1408–48. http://dx.doi.org/10.1017/s0266466602186075.

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Non- or semiparametric estimation and lag selection methods are proposed for three seasonal nonlinear autoregressive models of varying seasonal flexibility. All procedures are based on either local constant or local linear estimation. For the semiparametric models, after preliminary estimation of the seasonal parameters, the function estimation and lag selection are the same as nonparametric estimation and lag selection for standard models. A Monte Carlo study demonstrates good performance of all three methods. The semiparametric methods are applied to German real gross national product and UK public investment data. For these series our procedures provide evidence of nonlinear dynamics.

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3

Choroś-Tomczyk, Barbara, Wolfgang Karl Härdle, and Ostap Okhrin. "A semiparametric factor model for CDO surfaces dynamics." Journal of Multivariate Analysis 146 (April 2016): 151–63. http://dx.doi.org/10.1016/j.jmva.2015.09.002.

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4

Borak, Szymon, and Rafał Weron. "A semiparametric factor model for electricity forward curve dynamics." Journal of Energy Markets 1, no. 3 (September 2008): 3–16. http://dx.doi.org/10.21314/jem.2008.012.

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5

Dekker, T., P. Koster, and R. Brouwer. "Changing with the Tide: Semiparametric Estimation of Preference Dynamics." Land Economics 90, no. 4 (October 3, 2014): 717–45. http://dx.doi.org/10.3368/le.90.4.717.

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Härdle, Wolfgang K., and Piotr Majer. "Yield curve modeling and forecasting using semiparametric factor dynamics." European Journal of Finance 22, no. 12 (June 11, 2014): 1109–29. http://dx.doi.org/10.1080/1351847x.2014.926281.

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7

Fengler, M. R., W. K. Hardle, and E. Mammen. "A semiparametric factor model for implied volatility surface dynamics." Journal of Financial Econometrics 5, no. 2 (December 27, 2006): 189–218. http://dx.doi.org/10.1093/jjfinec/nbm005.

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Härdle, Wolfgang Karl, Nikolaus Hautsch, and Andrija Mihoci. "Modelling and forecasting liquidity supply using semiparametric factor dynamics." Journal of Empirical Finance 19, no. 4 (September 2012): 610–25. http://dx.doi.org/10.1016/j.jempfin.2012.04.002.

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9

Härdle, Wolfgang Karl, and Elena Silyakova. "Implied basket correlation dynamics." Statistics & Risk Modeling 33, no. 1-2 (January 1, 2016): 1–20. http://dx.doi.org/10.1515/strm-2014-1176.

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AbstractEquity basket correlation can be estimated both using the physical measure from stock prices, and also using the risk neutral measure from option prices. The difference between the two estimates motivates a so-called “dispersion strategy”. We study the performance of this strategy on the German market and propose several profitability improvement schemes based on implied correlation (IC) forecasts. Modelling IC conceals several challenges. Firstly the number of correlation coefficients would grow with the size of the basket. Secondly, IC is not constant over maturities and strikes. Finally, IC changes over time. We reduce the dimensionality of the problem by assuming equicorrelation. The IC surface (ICS) is then approximated from the implied volatilities of stocks and the implied volatility of the basket. To analyze the dynamics of the ICS we employ a dynamic semiparametric factor model.

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Hu, Yingyao, Robert Moffitt, and Yuya Sasaki. "Semiparametric estimation of the canonical permanent‐transitory model of earnings dynamics." Quantitative Economics 10, no. 4 (2019): 1495–536. http://dx.doi.org/10.3982/qe1117.

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This paper presents identification and estimation results for a flexible state space model. Our modification of the canonical model allows the permanent component to follow a unit root process and the transitory component to follow a semiparametric model of a higher‐order autoregressive‐moving‐average (ARMA) process. Using panel data of observed earnings, we establish identification of the nonparametric joint distributions for each of the permanent and transitory components over time. We apply the identification and estimation method to the earnings dynamics of U.S. men using the Panel Survey of Income Dynamics (PSID). The results show that the marginal distributions of permanent and transitory earnings components are more dispersed, more skewed, and have fatter tails than the normal and that earnings mobility is much lower than for the normal. We also find strong evidence for the existence of higher‐order ARMA processes in the transitory component, which lead to much different estimates of the distributions of and earnings mobility in the permanent component, implying that misspecification of the process for transitory earnings can affect estimated distributions of the permanent component and estimated earnings dynamics of that component. Thus our flexible model implies earnings dynamics for U.S. men different from much of the prior literature.

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11

Nelson, William A., Edward McCauley, and Jörg Wimbert. "CAPTURING DYNAMICS WITH THE CORRECT RATES: INVERSE PROBLEMS USING SEMIPARAMETRIC APPROACHES." Ecology 85, no. 4 (April 2004): 889–903. http://dx.doi.org/10.1890/02-8019.

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12

Wu, Song, Jie Yang, and Rongling Wu. "Semiparametric functional mapping of quantitative trait loci governing long-term HIV dynamics." Bioinformatics 23, no. 13 (July 1, 2007): i569—i576. http://dx.doi.org/10.1093/bioinformatics/btm164.

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13

Yap, Wai Weng, Tamat Sarmidi, Abu Hassan Shaari, and Fathin Faizah Said. "Income inequality and shadow economy: a nonparametric and semiparametric analysis." Journal of Economic Studies 45, no. 1 (January 8, 2018): 2–13. http://dx.doi.org/10.1108/jes-07-2016-0137.

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Purpose The purpose of this paper is to investigate the nonlinear relationship between shadow economy and income inequality and determine whether the size of shadow economy can influence the level of income inequality. Design/methodology/approach Both parametric (panel OLS) and nonparametric/semiparametric regression suggested by Robinson (1988) will be used to capture the dynamic nonlinear relationship between these variables using unbalanced panel data of 154 countries from 2000 to 2007. Additionally, the relationship between income inequality and shadow economy on both developed and developing countries will be analyzed and compared. Findings First, semiparametric analysis and nonparametric analysis are significantly different than parametric analysis and better in nonlinear analysis between income inequality and shadow economy. Second, income inequality and shadow economy resemble an inverted-N relationship. Third, the relationship between income inequality and shadow economy is different in developed countries (OECD countries) and developing countries, where OECD countries have similar inverted-N relationship as before. However, for developing countries, income inequality and shadow economy show an inverted-U relationship, similar to the original Kuznets hypothesis. Practical implications This study suggests that there is a possible trade-off between income inequality and shadow economy and helps policy makers in solving both problems effectively. Originality/value Despite the growing importance of income inequality and shadow economy, literature linking the two variables is scarce. To the best of the authors’ knowledge, there is no literature that nonlinearly links these two variables. Furthermore, the dynamics of the relationship between these two variables in developed countries and developing countries will be explored as well.

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14

Lolla, Tapovan, and Pierre F. J. Lermusiaux. "A Gaussian Mixture Model Smoother for Continuous Nonlinear Stochastic Dynamical Systems: Theory and Scheme." Monthly Weather Review 145, no. 7 (July 2017): 2743–61. http://dx.doi.org/10.1175/mwr-d-16-0064.1.

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Retrospective inference through Bayesian smoothing is indispensable in geophysics, with crucial applications in ocean and numerical weather estimation, climate dynamics, and Earth system modeling. However, dealing with the high-dimensionality and nonlinearity of geophysical processes remains a major challenge in the development of Bayesian smoothers. Addressing this issue, a novel subspace smoothing methodology for high-dimensional stochastic fields governed by general nonlinear dynamics is obtained. Building on recent Bayesian filters and classic Kalman smoothers, the fundamental equations and forward–backward algorithms of new Gaussian Mixture Model (GMM) smoothers are derived, for both the full state space and dynamic subspace. For the latter, the stochastic Dynamically Orthogonal (DO) field equations and their time-evolving stochastic subspace are employed to predict the prior subspace probabilities. Bayesian inference, both forward and backward in time, is then analytically carried out in the dominant stochastic subspace, after fitting semiparametric GMMs to joint subspace realizations. The theoretical properties, varied forms, and computational costs of the new GMM smoother equations are presented and discussed.

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15

Liu, Nan, Liangyu Li, Bing Hao, Liusong Yang, Tonghai Hu, Tao Xue, Shoujun Wang, and Xingmao Shao. "Semiparametric Deep Learning Manipulator Inverse Dynamics Modeling Method for Smart City and Industrial Applications." Complexity 2020 (June 30, 2020): 1–11. http://dx.doi.org/10.1155/2020/9053715.

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In smart cities and factories, robotic applications require high accuracy and security, which depends on precise inverse dynamics modeling. However, the physical modeling methods cannot include the nondeterministic factors of the manipulator, such as flexibility, joint clearance, and friction. In this paper, the Semiparametric Deep Learning (SDL) method is proposed to model robot inverse dynamics. SDL is a type of deep learning framework, designed for optimal inference, combining the Rigid Body Dynamics (RBD) model and Nonparametric Deep Learning (NDL) model. The SDL model takes advantage of the global characteristics of classic RBD and the powerful fitting capabilities of the deep learning approach. Moreover, the parametric and nonparametric parts of the SDL model can be optimized at the same time instead of being optimized separately. The proposed method is validated using experiments, performed on a UR5 robotic platform. The results show that the performance of SDL model is better than that of RBD model and NDL model. SDL can always provide relatively accurate joint torque prediction, even when the RBD or NDL model is not accurate.

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16

Serletis, Apostolos, and Asghar Shahmoradi. "NOTE ON FINITE APPROXIMATIONS OF THE ASYMPTOTICALLY IDEAL MODEL." Macroeconomic Dynamics 12, no. 4 (September 2008): 579–90. http://dx.doi.org/10.1017/s1365100508070260.

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This note builds on recent work by Serletis and Shahmoradi [Macroeconomic Dynamics 9 (2005), 542–559] and estimates the AIM model at different degrees of approximation, using the same optimization procedures as in Gallant and Golub [Journal of Econometrics. 26 (1984), 295–321]. We estimate the models subject to regularity and provide a comparison between the different versions. We argue that the AIM(3) model estimated subject to global curvature currently provides the best specification for research in semiparametric modeling of consumer demand systems.

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17

Bringmann, Laura, Emilio Ferrer, Ellen Hamaker, Denny Borsboom, and Francis Tuerlinckx. "Modeling Nonstationary Emotion Dynamics in Dyads Using a Semiparametric Time-Varying Vector Autoregressive Model." Multivariate Behavioral Research 50, no. 6 (November 2, 2015): 730–31. http://dx.doi.org/10.1080/00273171.2015.1120182.

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18

Bhasi, Kavitha, Alan Forrest, and Murali Ramanathan. "Application of SPLINDID, a Semiparametric, Model-Based Method for Pharmacogenomic Modeling of mRNA Dynamics." Pharmaceutical Research 23, no. 4 (March 24, 2006): 663–69. http://dx.doi.org/10.1007/s11095-006-9747-1.

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19

Schearer, Eric M., Yu-Wei Liao, Eric J. Perreault, Matthew C. Tresch, William D. Memberg, Robert F. Kirsch, and Kevin M. Lynch. "Semiparametric Identification of Human Arm Dynamics for Flexible Control of a Functional Electrical Stimulation Neuroprosthesis." IEEE Transactions on Neural Systems and Rehabilitation Engineering 24, no. 12 (December 2016): 1405–15. http://dx.doi.org/10.1109/tnsre.2016.2535348.

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20

Zhou, Tianjian, and Yuan Ji. "Semiparametric Bayesian inference for the transmission dynamics of COVID-19 with a state-space model." Contemporary Clinical Trials 97 (October 2020): 106146. http://dx.doi.org/10.1016/j.cct.2020.106146.

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21

Sondergaard, Thomas, and Pierre F. J. Lermusiaux. "Data Assimilation with Gaussian Mixture Models Using the Dynamically Orthogonal Field Equations. Part I: Theory and Scheme." Monthly Weather Review 141, no. 6 (June 1, 2013): 1737–60. http://dx.doi.org/10.1175/mwr-d-11-00295.1.

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Abstract This work introduces and derives an efficient, data-driven assimilation scheme, focused on a time-dependent stochastic subspace that respects nonlinear dynamics and captures non-Gaussian statistics as it occurs. The motivation is to obtain a filter that is applicable to realistic geophysical applications, but that also rigorously utilizes the governing dynamical equations with information theory and learning theory for efficient Bayesian data assimilation. Building on the foundations of classical filters, the underlying theory and algorithmic implementation of the new filter are developed and derived. The stochastic Dynamically Orthogonal (DO) field equations and their adaptive stochastic subspace are employed to predict prior probabilities for the full dynamical state, effectively approximating the Fokker–Planck equation. At assimilation times, the DO realizations are fit to semiparametric Gaussian Mixture Models (GMMs) using the Expectation-Maximization algorithm and the Bayesian Information Criterion. Bayes’s law is then efficiently carried out analytically within the evolving stochastic subspace. The resulting GMM-DO filter is illustrated in a very simple example. Variations of the GMM-DO filter are also provided along with comparisons with related schemes.

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22

Zhao, Xiaobing, and Xian Zhou. "Semiparametric models of longitudinal and time-to-event data with applications to HIV viral dynamics and CD4 counts." Journal of Applied Statistics 42, no. 11 (May 12, 2015): 2461–77. http://dx.doi.org/10.1080/02664763.2015.1043859.

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23

Redfearn, Christian L. "THE EMERGENCE OF CENTRALITY IN A TRANSITION ECONOMY: COMPARING LAND MARKET DYNAMICS MEASURED UNDER MONOCENTRIC AND SEMIPARAMETRIC MODELS." Journal of Regional Science 46, no. 5 (December 2006): 825–46. http://dx.doi.org/10.1111/j.1467-9787.2006.00485.x.

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24

Harrison, Matthew T., Asohan Amarasingham, and Wilson Truccolo. "Spatiotemporal Conditional Inference and Hypothesis Tests for Neural Ensemble Spiking Precision." Neural Computation 27, no. 1 (January 2015): 104–50. http://dx.doi.org/10.1162/neco_a_00681.

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The collective dynamics of neural ensembles create complex spike patterns with many spatial and temporal scales. Understanding the statistical structure of these patterns can help resolve fundamental questions about neural computation and neural dynamics. Spatiotemporal conditional inference (STCI) is introduced here as a semiparametric statistical framework for investigating the nature of precise spiking patterns from collections of neurons that is robust to arbitrarily complex and nonstationary coarse spiking dynamics. The main idea is to focus statistical modeling and inference not on the full distribution of the data, but rather on families of conditional distributions of precise spiking given different types of coarse spiking. The framework is then used to develop families of hypothesis tests for probing the spatiotemporal precision of spiking patterns. Relationships among different conditional distributions are used to improve multiple hypothesis-testing adjustments and design novel Monte Carlo spike resampling algorithms. Of special note are algorithms that can locally jitter spike times while still preserving the instantaneous peristimulus time histogram or the instantaneous total spike count from a group of recorded neurons. The framework can also be used to test whether first-order maximum entropy models with possibly random and time-varying parameters can account for observed patterns of spiking. STCI provides a detailed example of the generic principle of conditional inference, which may be applicable to other areas of neurostatistical analysis.

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Cuccia, Tiziana, Calogero Guccio, and Ilde Rizzo. "UNESCO sites and performance trend of Italian regional tourism destinations." Tourism Economics 23, no. 2 (February 5, 2017): 316–42. http://dx.doi.org/10.1177/1354816616656266.

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This article analyzes the role of United Nations Educational Scientific and Cultural Organization (UNESCO) sites on the enhancement of tourism destinations (TDs) performance, taking the Italian regions over the period 1995–2010 as a case study. Specifically, we aim at studying the effect of the inscription in the World Heritage List (WHL) upon the dynamics of the efficiency of the Italian regions as TDs. We use a two-stage data envelopment analysis window analysis, to detect efficiency trends and resort to both semiparametric pooled-truncated and panel data estimators to evaluate the determinants of the efficiency changes in TDs over time. Moreover, we test for the presence of spatial dependence in the efficiency of TDs. The results reveal that the WHL does not play a significant role in enhancing technical efficiency of TDs. These empirical findings are robust to alternative estimators and model specifications. Furthermore, the spatial analysis does not reveal significant spillover effects in the efficiency of TDs.

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Gupta, Resmi, Jane Khoury, Mekibib Altaye, Lawrence Dolan, and Rhonda D. Szczesniak. "Glycemic Excursions in Type 1 Diabetes in Pregnancy: A Semiparametric Statistical Approach to Identify Sensitive Time Points during Gestation." Journal of Diabetes Research 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/2852913.

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Aim.To examine the gestational glycemic profile and identify specific times during pregnancy that variability in glucose levels, measured by change in velocity and acceleration/deceleration of blood glucose fluctuations, is associated with delivery of a large-for-gestational-age (LGA) baby, in women with type 1 diabetes.Methods.Retrospective analysis of capillary blood glucose levels measured multiple times daily throughout gestation in women with type 1 diabetes was performed using semiparametric mixed models.Results.Velocity and acceleration/deceleration in glucose levels varied across gestation regardless of delivery outcome. Compared to women delivering LGA babies, those delivering babies appropriate for gestational age exhibited significantly smaller rates of change and less variation in glucose levels between 180 days of gestation and birth.Conclusions.Use of innovative statistical methods enabled detection of gestational intervals in which blood glucose fluctuation parameters might influence the likelihood of delivering LGA baby in mothers with type 1 diabetes. Understanding dynamics and being able to visualize gestational changes in blood glucose are a potentially useful tool to assist care providers in determining the optimal timing to initiate continuous glucose monitoring.

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Momper, Jeremiah D., Jin Yang, Mary Gockenbach, Florin Vaida, and Sanjay K. Nigam. "Dynamics of Organic Anion Transporter-Mediated Tubular Secretion during Postnatal Human Kidney Development and Maturation." Clinical Journal of the American Society of Nephrology 14, no. 4 (March 18, 2019): 540–48. http://dx.doi.org/10.2215/cjn.10350818.

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Background and objectivesThe neonatal and juvenile human kidney can be exposed to a variety of potentially toxic drugs (e.g., nonsteroidal anti-inflammatory drugs, antibiotics, antivirals, diuretics), many of which are substrates of the kidney organic anion transporters, OAT1 (SLC22A6, originally NKT) and OAT3 (SLC22A8). Despite the immense concern about the consequences of drug toxicity in this vulnerable population, the developmental regulation of OATs in the immature postnatal kidney is poorly understood.Design, setting, participants, & measurementsRecognizing that today it is difficult to obtain rich data on neonatal kidney handling of OAT probes due to technical, logistic, and ethical considerations, multiple older physiologic studies that used the prototypical organic anion substrate para-aminohippurate (PAH) were reanalyzed in order to provide a quantitative description of OAT-mediated tubular secretion across the pediatric age continuum. Parametric and semiparametric models were evaluated for kidney function outcome variables of interest (maximum tubular secretory capacity of PAH [TmPAH], effective renal plasma flow [ERPF], and GFR).ResultsData from 119 neonates, infants, and children ranging in age from 1 day to 11.8 years were used to fit TmPAH, ERPF, and GFR as functions of postnatal age. TmPAH is low in the immediate postnatal period and increases markedly after birth, reaching 50% of the adult value (80 mg/min) at 8.3 years of age. During the first 2 years of life, TmPAH is lower than that of GFR when viewed as the fraction of the adult value.ConclusionsDuring postnatal human kidney development, proximal tubule secretory function—as measured using PAH, a surrogate for OAT-mediated secretion of organic anion drugs, metabolites, and toxins—is low initially but increases rapidly. Despite developmental differences between species, this overall pattern is roughly consistent with animal studies. The human data raise the possibility that the acquisition of tubular secretory function may not closely parallel glomerular filtration.

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Ning, Jing, Mohammad H. Rahbar, Sangbum Choi, Jin Piao, Chuan Hong, Deborah J. del Junco, Elaheh Rahbar, Erin E. Fox, John B. Holcomb, and Mei-Cheng Wang. "Estimating the ratio of multivariate recurrent event rates with application to a blood transfusion study." Statistical Methods in Medical Research 26, no. 4 (July 9, 2015): 1969–81. http://dx.doi.org/10.1177/0962280215593974.

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In comparative effectiveness studies of multicomponent, sequential interventions like blood product transfusion (plasma, platelets, red blood cells) for trauma and critical care patients, the timing and dynamics of treatment relative to the fragility of a patient’s condition is often overlooked and underappreciated. While many hospitals have established massive transfusion protocols to ensure that physiologically optimal combinations of blood products are rapidly available, the period of time required to achieve a specified massive transfusion standard (e.g. a 1:1 or 1:2 ratio of plasma or platelets:red blood cells) has been ignored. To account for the time-varying characteristics of transfusions, we use semiparametric rate models for multivariate recurrent events to estimate blood product ratios. We use latent variables to account for multiple sources of informative censoring (early surgical or endovascular hemorrhage control procedures or death). The major advantage is that the distributions of latent variables and the dependence structure between the multivariate recurrent events and informative censoring need not be specified. Thus, our approach is robust to complex model assumptions. We establish asymptotic properties and evaluate finite sample performance through simulations, and apply the method to data from the PRospective Observational Multicenter Major Trauma Transfusion study.

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Zhang, Hanze, Yangxin Huang, Wei Wang, Henian Chen, and Barbara Langland-Orban. "Bayesian quantile regression-based partially linear mixed-effects joint models for longitudinal data with multiple features." Statistical Methods in Medical Research 28, no. 2 (September 22, 2017): 569–88. http://dx.doi.org/10.1177/0962280217730852.

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In longitudinal AIDS studies, it is of interest to investigate the relationship between HIV viral load and CD4 cell counts, as well as the complicated time effect. Most of common models to analyze such complex longitudinal data are based on mean-regression, which fails to provide efficient estimates due to outliers and/or heavy tails. Quantile regression-based partially linear mixed-effects models, a special case of semiparametric models enjoying benefits of both parametric and nonparametric models, have the flexibility to monitor the viral dynamics nonparametrically and detect the varying CD4 effects parametrically at different quantiles of viral load. Meanwhile, it is critical to consider various data features of repeated measurements, including left-censoring due to a limit of detection, covariate measurement error, and asymmetric distribution. In this research, we first establish a Bayesian joint models that accounts for all these data features simultaneously in the framework of quantile regression-based partially linear mixed-effects models. The proposed models are applied to analyze the Multicenter AIDS Cohort Study (MACS) data. Simulation studies are also conducted to assess the performance of the proposed methods under different scenarios.

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Dufitinema, Josephine, and Seppo Pynnönen. "Long-range dependence in the returns and volatility of the Finnish housing market." Journal of European Real Estate Research 13, no. 1 (December 19, 2019): 29–54. http://dx.doi.org/10.1108/jerer-07-2019-0019.

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Purpose The purpose of this paper is to examine the evidence of long-range dependence behaviour in both house price returns and volatility for fifteen main regions in Finland over the period of 1988:Q1 to 2018:Q4. These regions are divided geographically into 45 cities and sub-areas according to their postcode numbers. The studied type of dwellings is apartments (block of flats) divided into one-room, two-rooms, and more than three rooms apartments types. Design/methodology/approach For each house price return series, both parametric and semiparametric long memory approaches are used to estimate the fractional differencing parameter d in an autoregressive fractional integrated moving average [ARFIMA (p, d, q)] process. Moreover, for cities and sub-areas with significant clustering effects (autoregressive conditional heteroscedasticity [ARCH] effects), the semiparametric long memory method is used to analyse the degree of persistence in the volatility by estimating the fractional differencing parameter d in both squared and absolute price returns. Findings A higher degree of predictability was found in all three apartments types price returns with the estimates of the long memory parameter constrained in the stationary and invertible interval, implying that the returns of the studied types of dwellings are long-term dependent. This high level of persistence in the house price indices differs from other assets, such as stocks and commodities. Furthermore, the evidence of long-range dependence was discovered in the house price volatility with more than half of the studied samples exhibiting long memory behaviour. Research limitations/implications Investigating the long memory behaviour in both returns and volatility of the house prices is crucial for investment, risk and portfolio management. One reason is that the evidence of long-range dependence in the housing market returns suggests a high degree of predictability of the asset. The other reason is that the presence of long memory in the housing market volatility aids in the development of appropriate time series volatility forecasting models in this market. The study outcomes will be used in modelling and forecasting the volatility dynamics of the studied types of dwellings. The quality of the data limits the analysis and the results of the study. Originality/value To the best of the authors’ knowledge, this is the first research that assesses the long memory behaviour in the Finnish housing market. Also, it is the first study that evaluates the volatility of the Finnish housing market using data on both municipal and geographical level.

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KARIM, MOHAMMAD EHSANUL, and JAHIDUR RAHMAN KHAN. "Guidance for practitioners on the choices of software implementation for frailty models: Simulations and an application in determining the birth interval dynamics." Journal of Statistical Research 52, no. 1 (September 2, 2018): 1–17. http://dx.doi.org/10.47302/jsr.2018520101.

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In clustered survival analysis applications, researchers frequently fit frailty models using parametric and nonparametric approaches to obtain the estimates for the parameters associated with the survival model covariates and heterogeneity (frailty). Availability of the off- the-shelve implementations and freely available R software packages makes it convenient for the practitioners to fit these complicated models easily. Even though there has been a couple of studies assessing the stability of the older packages (e.g., survival, coxme) under a variety of scenarios, some of the newer implementations (e.g., frailtySurv, JM and parfm) have not gone through similar rigorous assessment. It is worth evaluating these new software implementations, and comparing them with the older packages. In the current work, via simulations, we will examine the estimates from all of these popularly used software implementations under a variety of scenarios when the corresponding assumptions related to the baseline hazard and frailty distributions are misspecified. Additionally, true heterogeneity parameter, censoring patterns and number of clusters were varied in the simulations to assess respective impacts on the estimates. From these simulations, we observed that when there is a large number of clusters and mild censoring, Cox PH frailty models fitted using a newer semiparametric estimation technique (from the frailtySurv package) produced regression and heterogeneity parameter estimates that were associated with unusually large bias and variability. On the other hand, when the true heterogeneity parameter is substantially large, the Cox PH frailty models fitted using the coxme package were often producing highly variable estimates of the heterogeneity parameter. The simulation findings then guided our choice of appropriate frailty model in the context of determining the birth interval dynamics in Bangladesh.

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Sun, Yan, Hongjia Yan, Wenyang Zhang, and Zudi Lu. "A semiparametric spatial dynamic model." Annals of Statistics 42, no. 2 (April 2014): 700–727. http://dx.doi.org/10.1214/13-aos1201.

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Tapak, Leili, Michael R. Kosorok, Majid Sadeghifar, Omid Hamidi, Saeid Afshar, and Hassan Doosti. "Regularized Weighted Nonparametric Likelihood Approach for High-Dimension Sparse Subdistribution Hazards Model for Competing Risk Data." Computational and Mathematical Methods in Medicine 2021 (September 19, 2021): 1–13. http://dx.doi.org/10.1155/2021/5169052.

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Variable selection and penalized regression models in high-dimension settings have become an increasingly important topic in many disciplines. For instance, omics data are generated in biomedical researches that may be associated with survival of patients and suggest insights into disease dynamics to identify patients with worse prognosis and to improve the therapy. Analysis of high-dimensional time-to-event data in the presence of competing risks requires special modeling techniques. So far, some attempts have been made to variable selection in low- and high-dimension competing risk setting using partial likelihood-based procedures. In this paper, a weighted likelihood-based penalized approach is extended for direct variable selection under the subdistribution hazards model for high-dimensional competing risk data. The proposed method which considers a larger class of semiparametric regression models for the subdistribution allows for taking into account time-varying effects and is of particular importance, because the proportional hazards assumption may not be valid in general, especially in the high-dimension setting. Also, this model relaxes from the constraint of the ability to simultaneously model multiple cumulative incidence functions using the Fine and Gray approach. The performance/effectiveness of several penalties including minimax concave penalty (MCP); adaptive LASSO and smoothly clipped absolute deviation (SCAD) as well as their L2 counterparts were investigated through simulation studies in terms of sensitivity/specificity. The results revealed that sensitivity of all penalties were comparable, but the MCP and MCP-L2 penalties outperformed the other methods in term of selecting less noninformative variables. The practical use of the model was investigated through the analysis of genomic competing risk data obtained from patients with bladder cancer and six genes of CDC20, NCF2, SMARCAD1, RTN4, ETFDH, and SON were identified using all the methods and were significantly correlated with the subdistribution.

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34

Komunjer, Ivana, and Quang Vuong. "SEMIPARAMETRIC EFFICIENCY BOUND IN TIME-SERIES MODELS FOR CONDITIONAL QUANTILES." Econometric Theory 26, no. 2 (August 18, 2009): 383–405. http://dx.doi.org/10.1017/s0266466609100038.

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We derive the semiparametric efficiency bound in dynamic models of conditional quantiles under a sole strong mixing assumption. We also provide an expression of Stein’s (1956) least favorable parametric submodel. Our approach is as follows: First, we construct a fully parametric submodel of the semiparametric model defined by the conditional quantile restriction that contains the data generating process. We then compare the asymptotic covariance matrix of the MLE obtained in this submodel with those of the M-estimators for the conditional quantile parameter that are consistent and asymptotically normal. Finally, we show that the minimum asymptotic covariance matrix of this class of M-estimators equals the asymptotic covariance matrix of the parametric submodel MLE. Thus, (i) this parametric submodel is a least favorable one, and (ii) the expression of the semiparametric efficiency bound for the conditional quantile parameter follows.

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Norets, A., and X. Tang. "Semiparametric Inference in Dynamic Binary Choice Models." Review of Economic Studies 81, no. 3 (December 15, 2013): 1229–62. http://dx.doi.org/10.1093/restud/rdt050.

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Cai, Zongwu, and Qi Li. "NONPARAMETRIC ESTIMATION OF VARYING COEFFICIENT DYNAMIC PANEL DATA MODELS." Econometric Theory 24, no. 5 (June 23, 2008): 1321–42. http://dx.doi.org/10.1017/s0266466608080523.

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We suggest using a class of semiparametric dynamic panel data models to capture individual variations in panel data. The model assumes linearity in some continuous/discrete variables that can be exogenous/endogenous and allows for nonlinearity in other weakly exogenous variables. We propose a nonparametric generalized method of moments (NPGMM) procedure to estimate the functional coefficients, and we establish the consistency and asymptotic normality of the resulting estimators.

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37

Escanciano, Juan Carlos, and Silvia Mayoral. "Semiparametric estimation of dynamic conditional expected shortfall models." International Journal of Monetary Economics and Finance 1, no. 2 (2008): 106. http://dx.doi.org/10.1504/ijmef.2008.019217.

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Rolain, Yves, Wendy Van Moer, Johan Schoukens, and Tom Dhaene. "Estimation and Validation of Semiparametric Dynamic Nonlinear Models." IEEE Transactions on Instrumentation and Measurement 57, no. 2 (2008): 395–400. http://dx.doi.org/10.1109/tim.2007.909959.

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39

Buchinsky, Moshe, Jinyong Hahn, and Kyoo il Kim. "Semiparametric information bound of dynamic discrete choice models." Economics Letters 108, no. 2 (August 2010): 109–12. http://dx.doi.org/10.1016/j.econlet.2010.04.020.

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Chen, Jia, Degui Li, Oliver Linton, and Zudi Lu. "Semiparametric dynamic portfolio choice with multiple conditioning variables." Journal of Econometrics 194, no. 2 (October 2016): 309–18. http://dx.doi.org/10.1016/j.jeconom.2016.05.009.

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41

Hallin, Marc, and Davide La Vecchia. "R-estimation in semiparametric dynamic location-scale models." Journal of Econometrics 196, no. 2 (February 2017): 233–47. http://dx.doi.org/10.1016/j.jeconom.2016.08.002.

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42

Hafner, Christian M., and Olga Reznikova. "Efficient estimation of a semiparametric dynamic copula model." Computational Statistics & Data Analysis 54, no. 11 (November 2010): 2609–27. http://dx.doi.org/10.1016/j.csda.2010.01.013.

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43

Park, Byeong U., Robin C. Sickles, and Léopold Simar. "Semiparametric efficient estimation of dynamic panel data models." Journal of Econometrics 136, no. 1 (January 2007): 281–301. http://dx.doi.org/10.1016/j.jeconom.2006.03.004.

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44

Khan, Shakeeb, Fu Ouyang, and Elie Tamer. "Inference on semiparametric multinomial response models." Quantitative Economics 12, no. 3 (2021): 743–77. http://dx.doi.org/10.3982/qe1315.

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We explore inference on regression coefficients in semiparametric multinomial response models. We consider cross‐sectional, and both static and dynamic panel settings where we focus throughout on inference under sufficient conditions for point identification. The approach to identification uses a matching insight throughout all three models coupled with variation in regressors: with cross‐section data, we match across individuals while with panel data, we match within individuals over time. Across models, we relax the Indpendence of Irrelevant Alternatives (or IIA assumption, see McFadden (1974)) and allow for arbitrary correlation in the unobservables that determine utility of various alternatives. For the cross‐sectional model, estimation is based on a localized rank objective function, analogous to that used in Abrevaya, Hausman, and Khan (2010), and presents a generalization of existing approaches. In panel data settings, rates of convergence are shown to exhibit a curse of dimensionality in the number of alternatives. The results for the dynamic panel data model generalize the work of Honoré and Kyriazidou (2000) to cover the semiparametric multinomial case. A simulation study establishes adequate finite sample properties of our new procedures. We apply our estimators to a scanner panel data set.

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GOLOSNOY, VASYL, and HELMUT HERWARTZ. "DYNAMIC MODELING OF HIGH-DIMENSIONAL CORRELATION MATRICES IN FINANCE." International Journal of Theoretical and Applied Finance 15, no. 05 (August 2012): 1250035. http://dx.doi.org/10.1142/s0219024912500355.

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A class of dynamic factor and dynamic panel models is proposed for daily high dimensional correlation matrices of asset returns. These flexible semiparametric predictors process ultra high frequency information and allow to exploit both realized correlation matrices and exogenous factors for forecasting purposes. The Fisher-z transformation offers the transmission from (factor and panel) time series models operating on unrestricted random variables to bounded correlation forecasts. Our methodology is contrasted with prominent alternative correlation models. Based on economic performance criteria dynamic factor models turn out to carry the highest predictive content.

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de Jong, Robert M., and Tiemen Woutersen. "DYNAMIC TIME SERIES BINARY CHOICE." Econometric Theory 27, no. 4 (March 3, 2011): 673–702. http://dx.doi.org/10.1017/s0266466610000472.

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This paper considers dynamic time series binary choice models. It proves near epoch dependence and strong mixing for the dynamic binary choice model with correlated errors. Using this result, it shows in a time series setting the validity of the dynamic probit likelihood procedure when lags of the dependent binary variable are used as regressors, and it establishes the asymptotic validity of Horowitz’s smoothed maximum score estimation of dynamic binary choice models with lags of the dependent variable as regressors. For the semiparametric model, the latent error is explicitly allowed to be correlated. It turns out that no long-run variance estimator is needed for the validity of the smoothed maximum score procedure in the dynamic time series framework.

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Zhao, Zifeng, and Zhengjun Zhang. "Semiparametric dynamic max-copula model for multivariate time series." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 80, no. 2 (October 19, 2017): 409–32. http://dx.doi.org/10.1111/rssb.12256.

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Chib, Siddhartha, and Ivan Jeliazkov. "Inference in Semiparametric Dynamic Models for Binary Longitudinal Data." Journal of the American Statistical Association 101, no. 474 (June 1, 2006): 685–700. http://dx.doi.org/10.1198/016214505000000871.

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49

Giacomini, Enzo, Wolfgang Härdle, and Volker Krätschmer. "Dynamic semiparametric factor models in risk neutral density estimation." AStA Advances in Statistical Analysis 93, no. 4 (September 18, 2009): 387–402. http://dx.doi.org/10.1007/s10182-009-0115-4.

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Cai, Zongwu, Linna Chen, and Ying Fang. "Semiparametric Estimation of Partially Varying-Coefficient Dynamic Panel Data Models." Econometric Reviews 34, no. 6-10 (December 17, 2014): 695–719. http://dx.doi.org/10.1080/07474938.2014.956569.

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