Least Squares and Method of Moments

1996 ◽  
pp. 5-24 ◽  
Author(s):  
Myoung-jae Lee
Keyword(s):  
Least Squares ◽  
Method Of Moments

Shadow Economy, Corruption, and Economic Growth: An Empirical Analysis

2019 ◽  
Vol 47 (3) ◽  
pp. 276-294 ◽  
Author(s):  
Nedra Baklouti ◽  
Younes Boujelbene
Keyword(s):  
Economic Growth ◽  
Least Squares ◽  
Public Administration ◽  
Method Of Moments ◽  
Fixed Effects ◽  
Shadow Economy ◽  
Generalized Method Of Moments ◽  
Ordinary Least Squares ◽  
Negative Effects ◽  
Considerable Debate

There is considerable debate over the effects of both corruption and shadow economy on growth, but few studies have considered how the interaction between them might affect economic growth. We study how corruption levels in public administration affect economic growth and how this effect depends on the shadow economy. Using Ordinary Least Squares (OLS), fixed effects, and system generalized method of moments (GMM) on a dataset of 34 OECD countries over the period 1995-2014. The estimation results indicate that increased corruption and a larger shadow economy lead to decrease in economic growth. Results additionally indicate that the shadow economy magnifies the effect of corruption on economic growth. These results imply significant complementarities between corruption and the shadow economy, suggesting that the reduction of corruption will lead to a fall in the size of the shadow economy and will also reduce the negative effects of corruption on economic growth through the underground economy.

VI.—On the Graduation of Data by the Orthogonal Polynomials of Least Squares

1934 ◽  
Vol 53 ◽  
pp. 54-78 ◽  
Author(s):  
A. C. Aitken
Keyword(s):  
Orthogonal Polynomials ◽  
Least Squares ◽  
Linear Combination ◽  
Method Of Moments ◽  
Finite Differences ◽  
Continued Fractions ◽  
Selective Bibliography

The problem of fitting a polynomial to data by Least Squares has engaged the attention of many writers. The methods of approach have been many and various. Continued fractions, determinants, the calculus of finite differences and sums, the method of moments, the linear combination of data, the use of the orthogonal polynomials of Legendre and Tchebychef, these and doubtless other instruments of analysis have been pressed into service. At the end of the present paper is given a selective bibliography, which we hope on a future occasion to complete and to supplement by adding brief indications of the standpoint and achievement of each investigator.

scholarly journals Modelling the Influences of Economic, Demographic, and Institutional Factors on Fiscal Pressure Using OLS, PCSE, and FD-GMM Approaches

2020 ◽  
Vol 12 (4) ◽  
pp. 1681
Author(s):  
Alina-Cristina Nuță ◽  
Florian-Marcel Nuță
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Method Of Moments ◽  
Generalized Method Of Moments ◽  
Ordinary Least Squares ◽  
Institutional Factors ◽  
Standard Errors ◽  
Panel Analysis ◽  
Fiscal Pressure ◽  
Robust Standard Errors

The purpose of our article is to assess the effect of diverse factors, such as economic, demographic, and institutional factors, on global and social fiscal pressure. The study is based on a panel analysis of 38 states during 2000–2017. We used ordinary least squares (OLS) as a base model for our estimations, and a linear regression with panel-corrected standard errors and a first difference generalized method of moments (GMM) with robust standard errors and orthogonal deviations. The results of our study indicate that the demographic and institutional factors involved in the analysis contribute to the identification of some variables that affect the global or social fiscal pressure.

scholarly journals A Comparison of Ordinary Least Squares and Least Absolute Error Estimation

1988 ◽  
Vol 4 (3) ◽  
pp. 517-527 ◽  
Author(s):  
Andrew A. Weiss
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Error Estimation ◽  
Method Of Moments ◽  
Linear Regression Model ◽  
Generalized Method Of Moments ◽  
Absolute Error ◽  
Ordinary Least Squares ◽  
Hausman Test ◽  
Heteroscedastic Errors

In a linear-regression model with heteroscedastic errors, we consider two tests: a Hausman test comparing the ordinary least squares (OLS) and least absolute error (LAE) estimators and a test based on the signs of the errors from OLS. It turns out that these are related by the well-known equivalence between Hausman and the generalized method of moments tests. Particular cases, including homoscedasticity and asymmetry in the errors, are discussed.

scholarly journals Governance quality impact on health economics in selected countries: The panel data approach

2015 ◽  
Vol 4 (2) ◽  
pp. 148-154
Author(s):  
Parvaneh Salatin ◽  
Naahid Noorpoor
Keyword(s):  
Panel Data ◽  
Health Economics ◽  
Life Expectancy ◽  
Least Squares ◽  
Method Of Moments ◽  
Generalized Method Of Moments ◽  
Generalized Least Squares ◽  
Middle Income ◽  
Middle Income Countries ◽  
Governance Quality

The purpose of this paper is investigating the theoretical relationship between the effectiveness of governance quality on health economics in selected middle-income countries, using panel data. The Results of the estimation by using the Method of Generalized Least Squares (GLS) & Generalized Method of Moments (GMM) in selected countries for the period 2002-2011 show that governance quality has positive & significant effect on the life expectancy as an index showing the health economics in the group of the selected countries.

scholarly journals Applications of Generalized Method of Moments Estimation

2001 ◽  
Vol 15 (4) ◽  
pp. 87-100 ◽  
Author(s):  
Jeffrey M Wooldridge
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Least Squares ◽  
Cross Section ◽  
Method Of Moments ◽  
Generalized Method Of Moments ◽  
Ordinary Least Squares ◽  
Estimation Problems ◽  
Method Of Moments Estimation ◽  
Moments Estimators

I describe how the method of moments approach to estimation, including the more recent generalized method of moments (GMM) theory, can be applied to problems using cross section, time series, and panel data. Method of moments estimators can be attractive because in many circumstances they are robust to failures of auxiliary distributional assumptions that are not needed to identify key parameters. I conclude that while sophisticated GMM estimators are indispensable for complicated estimation problems, it seems unlikely that GMM will provide convincing improvements over ordinary least squares and two-stage least squares--by far the most common method of moments estimators used in econometrics--in settings faced most often by empirical researchers.

scholarly journals Metafounders are Fst fixation indices and reduce bias in single step genomic evaluations

2016 ◽  
Author(s):  
Carolina Andrea Garcia-Baccino ◽  
Andres Legarra ◽  
Ole F Christensen ◽  
Ignacy Misztal ◽  
Ivan Pocrnic ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Method Of Moments ◽  
Generalized Least Squares ◽  
Single Step ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Genetic Trend ◽  
Allelic Frequencies ◽  
True Value

ABSTRACTBACKGROUNDMetafounders are pseudo-individuals that condense the genetic heterozygosity and relationships within and across base pedigree populations, i.e. ancestral populations. This work addresses estimation and usefulness of metafounder relationships in Single Step GBLUP.RESULTSWe show that the ancestral relationship parameters are proportional to standardized covariances of base allelic frequencies across populations, like Fst fixation indexes. These covariances of base allelic frequencies can be estimated from marker genotypes of related recent individuals, and pedigree. Simple methods for estimation include naïve computation of allele frequencies from marker genotypes or a method of moments equating average pedigree-based and marker-based relationships. Complex methods include generalized least squares or maximum likelihood based on pedigree relationships. To our knowledge, methods to infer Fst coefficients and Fst differentiation have not been developed for related populations.A compatible genomic relationship matrix constructed as a crossproduct of {−1,0,1} codes, and equivalent (up to scale factors) to an identity by state relationship matrix at the markers, is derived. Using a simulation with a single population under selection, in which only males and youngest animals were genotyped, we observed that generalized least squares or maximum likelihood gave accurate and unbiased estimates of the ancestral relationship parameter (true value: 0.40) whereas the other two (naïve and method of moments) were biased (estimates of 0.43 and 0.35). We also observed that genomic evaluation by Single Step GBLUP using metafounders was less biased in terms of accurate genetic trend (0.01 instead of 0.12 bias), slightly overdispersed (0.94 instead of 0.99) and as accurate (0.74) than the regular Single Step GBLUP. Single Step GBLUP using metafounders also provided consistent estimates of heritability.CONCLUSIONSEstimation of metafounder relationship can be achieved using BLUP-like methods with pedigree and markers. Inclusion of metafounder relationships improves bias of genomic predictions with no loss in accuracy.

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