Search results for "Parametric estimation"
showing 10 items of 16 documents
Labor Productivity Growth: Disentangling Technology and Capital Accumulation
2014
We adopt a counterfactual approach to decompose labor productivity growth into growth of Technological Productivity (TEP), growth of the capital-labor ratio and growth of Total Factor Productivity (TFP). We bring the decomposition to the data using international countrysectoral information spanning from the 1960s to the 2000s and a nonparametric generalized kernel method, which enables us to estimate the production function allowing for heterogeneity across all relevant dimensions: countries, sectors and time. As well as documenting substantial heterogeneity across countries and sectors, we nd average TEP to account for about 44% of labor productivity growth and TEP gaps with respect to the…
Feasibility of Linear Parametric Estimation of Dynamic Information Measures to assess Physiological Stress from Short-Term Cardiovascular Variability
2021
Extensive efforts have been recently devoted to implement fast and reliable algorithms capable of assessing the physiological response of the organism to physiological stress. In this study, we propose the comparison between model-free and linear parametric methods as regards their ability to detect alterations in the dynamics and in the complexity of cardiovascular and respiratory variability evoked by postural and mental stress. Dynamic entropy (DE) and information storage (IS) measures were calculated on three physiological time-series, i.e. heart period, respiratory volume and systolic arterial pressure, on 61 healthy subjects monitored in resting conditions as well as during head-up ti…
Estimating Engel curves under unit and item nonresponse
2010
SUMMARY This paper estimates food Engel curves using data from the first wave of the Survey on Health, Aging and Retirement in Europe (SHARE). Our statistical model simultaneously takes into account selectivity due to unit and item nonresponse, endogeneity problems, and issues related to flexible specification of the relationship of interest. We estimate both parametric and semiparametric specifications of the model. The parametric specification assumes that the unobservables in the model follow a multivariate Gaussian distribution, while the semiparametric specification avoids distributional assumptions about the unobservables. Copyright © 2011 John Wiley & Sons, Ltd.
Alternated estimation in semi-parametric space-time branching-type point processes with application to seismic catalogs
2014
An estimation approach for the semi-param-etric intensity function of a class of space-time point processes is introduced. In particular we want to account for the estimation of parametric and nonparametric components simultaneously, applying a forward predictive likelihood to semi-parametric models. For each event, the probability of being a background event or an offspring is therefore estimated.
Estimation de la relation de salaires de Mincer : choix de specification et enjeux économétriques
2012
In the present doctoral thesis, we estimated Mincer’s (1974) semi logarithmic wage function for the French and Pakistani labour force data. This model is considered as a standard tool in order to estimate the relationship between earnings/wages and different contributory factors. Despite of its vide and extensive use, simple estimation of the Mincerian model is biased because of different econometric problems. The main sources of bias noted in the literature are endogeneity of schooling, measurement error, and sample selectivity. We have tackled the endogeneity and measurement error biases via instrumental variables two stage least squares approach for which we have proposed two new instrum…
Estimation of ordered response models with sample selection
2011
We introduce two new Stata commands for the estimation of an ordered response model with sample selection. The opsel command uses a standard maximum-likelihood approach to fit a parametric specification of the model where errors are assumed to follow a bivariate Gaussian distribution. The snpopsel command uses the semi-nonparametric approach of Gallant and Nychka (1987, Econometrica 55: 363–390) to fit a semiparametric specification of the model where the bivariate density function of the errors is approximated by a Hermite polynomial expansion. The snpopsel command extends the set of Stata routines for semi-nonparametric estimation of discrete response models. Compared to the other semi-n…
Estimating with kernel smoothers the mean of functional data in a finite population setting. A note on variance estimation in presence of partially o…
2014
In the near future, millions of load curves measuring the electricity consumption of French households in small time grids (probably half hours) will be available. All these collected load curves represent a huge amount of information which could be exploited using survey sampling techniques. In particular, the total consumption of a specific cus- tomer group (for example all the customers of an electricity supplier) could be estimated using unequal probability random sampling methods. Unfortunately, data collection may undergo technical problems resulting in missing values. In this paper we study a new estimation method for the mean curve in the presence of missing values which consists in…
Nonparametric estimation of quantile versions of the Lorenz curve
2018
Estimators of quantile versions of the Lorenz curve are proposed. The pointwise consistency and asymptotic normality of the estimators is proved. The efficiency of the estimators is also studied in simulations
Space-time Point Processes semi-parametric estimation with predictive measure information
2014
In this paper, we provide a method to estimate the space-time intensity of a branching-type point process by mixing nonparametric and parametric approaches. The method accounts simultaneously for the estimation of the different model components, applying a forward predictive likelihood estimation approach to semi-parametric models.