Intergenerational Earnings Mobility in Italy

2007 ◽  
Vol 7 (2) ◽  
Author(s):  
Sauro Mocetti
Keyword(s):  
Labor Market ◽  
Least Squares ◽  
Household Income ◽  
Intergenerational Mobility ◽  
Least Squares Method ◽  
Developed Countries ◽  
Data Set ◽  
Labor Market Success ◽  
Term Labor

Abstract This paper contributes to the growing number of studies on intergenerational mobility by providing a measure of earnings elasticity for Italy. The absence of an appropriate data set is overcome by adopting the two-sample two-stage least squares method. The analysis, based on the Survey of Household Income and Wealth, shows that intergenerational mobility is lower in Italy than it is in other developed countries. We also examine the reasons why the long-term labor market success of children is related to that of their fathers.

scholarly journals International Real Estate Review

2011 ◽  
Vol 14 (2) ◽  
pp. 159-183
Author(s):  
Oliver Bischoff ◽  
Keyword(s):  
Household Income ◽  
Birth Rate ◽  
Flow Model ◽  
Positive Impact ◽  
Data Set ◽  
Rental Market ◽  
East German ◽  
Vacancy Rates ◽  
Stock Flow

We apply the stock flow model for the German residential rental market using a data set that includes the overwhelming majority of nomenclature of territorial units for statistics (NUTS) 3 regions for the 2004-2007 period. Aside from proving conditional rental price convergence, we have detected a turnaround in vacancy stocks between the short and the long term. While East German counties and West German independent cities currently exhibit the highest and lowest vacancy rates, respectively, the opposite holds true at equilibrium. Leaning on theoretical suggestions, landlords in well-developed areas have incentives to hold onto vacancies in view of future rent increases. Our results support this idea, which demonstrates the significantly positive impact of household income and net birth rate on the natural vacancy rate.

Incorrect least-squares regression coefficients in method-comparison analysis.

1979 ◽  
Vol 25 (3) ◽  
pp. 432-438 ◽  
Author(s):  
P J Cornbleet ◽  
N Gochman
Keyword(s):  
Standard Deviation ◽  
Least Squares ◽  
Least Squares Method ◽  
Regression Line ◽  
Method Comparison ◽  
Regression Coefficients ◽  
Least Squares Regression ◽  
Data Set ◽  
Data Points ◽  
Regression Slopes

Abstract The least-squares method is frequently used to calculate the slope and intercept of the best line through a set of data points. However, least-squares regression slopes and intercepts may be incorrect if the underlying assumptions of the least-squares model are not met. Two factors in particular that may result in incorrect least-squares regression coefficients are: (a) imprecision in the measurement of the independent (x-axis) variable and (b) inclusion of outliers in the data analysis. We compared the methods of Deming, Mandel, and Bartlett in estimating the known slope of a regression line when the independent variable is measured with imprecision, and found the method of Deming to be the most useful. Significant error in the least-squares slope estimation occurs when the ratio of the standard deviation of measurement of a single x value to the standard deviation of the x-data set exceeds 0.2. Errors in the least-squares coefficients attributable to outliers can be avoided by eliminating data points whose vertical distance from the regression line exceed four times the standard error the estimate.

Prestack imaging of seismic data using Lp iterative reweighted least-squares wavefield extrapolation filters in the frequency-space domain

Geophysics ◽  
2018 ◽  
Vol 83 (4) ◽  
pp. V243-V252
Author(s):  
Wail A. Mousa
Keyword(s):  
Least Squares ◽  
Seismic Data ◽  
Least Squares Method ◽  
Filter Design ◽  
Finite Impulse Response ◽  
Weighted Least Squares ◽  
Data Set ◽  
Frequency Space ◽  
Wavefield Extrapolation ◽  
Iterative Reweighted Least Squares

A stable explicit depth wavefield extrapolation is obtained using [Formula: see text] iterative reweighted least-squares (IRLS) frequency-space ([Formula: see text]-[Formula: see text]) finite-impulse response digital filters. The problem of designing such filters to obtain stable images of challenging seismic data is formulated as an [Formula: see text] IRLS minimization. Prestack depth imaging of the challenging Marmousi model data set was then performed using the explicit depth wavefield extrapolation with the proposed [Formula: see text] IRLS-based algorithm. Considering the extrapolation filter design accuracy, the [Formula: see text] IRLS minimization method resulted in an image with higher quality when compared with the weighted least-squares method. The method can, therefore, be used to design high-accuracy extrapolation filters.

Calibration of stormwater quality regression models: a random process?

2010 ◽  
Vol 62 (4) ◽  
pp. 875-882 ◽  
Author(s):  
A. Dembélé ◽  
J.-L. Bertrand-Krajewski ◽  
B. Barillon
Keyword(s):  
Experimental Data ◽  
Least Squares ◽  
Regression Models ◽  
Linear Models ◽  
Least Squares Method ◽  
Weighted Least Squares ◽  
Ordinary Least Squares ◽  
Data Sets ◽  
Data Set ◽  
Urban Catchments

Regression models are among the most frequently used models to estimate pollutants event mean concentrations (EMC) in wet weather discharges in urban catchments. Two main questions dealing with the calibration of EMC regression models are investigated: i) the sensitivity of models to the size and the content of data sets used for their calibration, ii) the change of modelling results when models are re-calibrated when data sets grow and change with time when new experimental data are collected. Based on an experimental data set of 64 rain events monitored in a densely urbanised catchment, four TSS EMC regression models (two log-linear and two linear models) with two or three explanatory variables have been derived and analysed. Model calibration with the iterative re-weighted least squares method is less sensitive and leads to more robust results than the ordinary least squares method. Three calibration options have been investigated: two options accounting for the chronological order of the observations, one option using random samples of events from the whole available data set. Results obtained with the best performing non linear model clearly indicate that the model is highly sensitive to the size and the content of the data set used for its calibration.

scholarly journals Stature and long-term labor market outcomes: Evidence using Mendelian randomization

2017 ◽  
Vol 24 ◽  
pp. 18-29 ◽  
Author(s):  
Petri Böckerman ◽  
Jutta Viinikainen ◽  
Jari Vainiomäki ◽  
Mirka Hintsanen ◽  
Niina Pitkänen ◽  
...  
Keyword(s):  
Labor Market ◽  
Mendelian Randomization ◽  
Labor Market Outcomes ◽  
Market Outcomes ◽  
Term Labor

scholarly journals Evaluation of Organophosphate Pesticide Residues in Food Using the Partial Least Squares Method

2020 ◽  
Vol 3 (1) ◽  
pp. 19
Author(s):  
Gheorghe Ilia ◽  
Simona Funar-Timofei
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Pesticide Residues ◽  
Least Squares Method ◽  
Minimum Energy ◽  
Daily Intake ◽  
Toxicological Data ◽  
Malaria Mosquitoes ◽  
Partial Least Squares Method

Organophosphorus (OP) chemicals were broadly used as insecticides and in the treatment of human diseases such as malaria mosquitoes, parasitosis, myasthenia, and glaucoma. The OP toxicity is well known. They can cause environmental and health problems and have the possibility to accumulate in the food chain. The acceptable daily intake (ADI) can be considered as a measure of the effect of pesticide residues in food on human health. In this paper, the partial least squares (PLS) approach is used to evaluate the ADIs (expressed as pADIs) of a series of 46 structurally diverse OPs. OP structures were pre-optimized using the MMFF94s force field, and structural descriptors were calculated for the minimum energy conformers. This dataset was divided into 26 training compounds, and 20 pesticides were included in the prediction set. Several criteria to check the model robustness, overfitting, and the potential outliers in the X and Y space were employed. The PLS results indicated that new experimental toxicological data would be needed for five out of the 46 OPs, to improve their known ADI values, for qualitative and quantitative dietary long-term risk assessments.

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