A Note on the Forecasting Properties of Two Stage Least Squares Restricted Reduced Forms-The Finite Sample Case

1972 ◽  
Vol 13 (3) ◽  
pp. 757 ◽  
Author(s):  
Michael D. McCarthy
Keyword(s):  
Least Squares ◽  
Finite Sample ◽  
Two Stage ◽  
Two Stage Least Squares ◽  
Reduced Forms ◽  
Sample Case

scholarly journals Nonparametric Instrumental Variable Estimation in Practice

2016 ◽  
Vol 5 (1) ◽  
Author(s):  
Philip Shaw ◽  
Michael Andrew Cohen ◽  
Tao Chen
Keyword(s):  
Least Squares ◽  
Econometric Models ◽  
Least Squares Estimator ◽  
Finite Sample ◽  
Demand Model ◽  
Two Stage ◽  
Rule Of Thumb ◽  
Endogenous Regressors ◽  
Marginal Effects ◽  
Two Stage Least Squares

AbstractThis paper investigates recent developments in the literature on nonparametric instrumental variables estimation and considers the practical importance of the features of these estimators in the context of typically applied econometric models. Our primary focus is on the estimation of econometric models with endogenous regressors, and their marginal effects, without a known functional form. We develop an estimator for the marginal effects and investigate its finite sample performance. We show that when instruments are weak, in the classic sense, the nonparametric estimates of the marginal effect outperforms the classic two-stage least squares estimator, even when the model is correctly specified. When the instruments are strong, we show that the nonparametric estimator for the partial effects is still effective compared to the two-stage least squares estimator even as the number of IVs increases. We also investigate bandwidth choice and find that a rule-of-thumb bandwidth performs relatively well. Whereas cross-validation leads to a better fit when the number of instruments is small, as the number of instruments increases the rule-of-thumb standard actually results in better model fit. In an empirical application we estimate the work-horse aggregate logit demand model, discuss the required nonparametric identification properties, and document the differences between nonparametric and parametric specifications on the estimation of demand elasticities.

scholarly journals Model Persamaan Simultan pada Analisis Hubungan Kemiskian dan PDRB

2014 ◽  
Vol 5 (2) ◽  
pp. 810
Author(s):  
Rokhana Dwi Bekti ◽  
David David ◽  
Gita N ◽  
Priscillia Priscillia ◽  
Serlyana Serlyana
Keyword(s):  
Economic Growth ◽  
Least Squares ◽  
Least Squares Estimation ◽  
Two Stage ◽  
Two Stage Least Squares ◽  
Regional Product ◽  
The Relationship ◽  
Simultaneous Model

Simultaneous model is a model for some equation which have simultaneous relationships. It was often found in econometrics, such as the relationship between Gross Domestic Regional Product (GDRP) and poverty. GDP is a common indicator that can be used to determine the economic growth occurred in region. Meanwhile, poverty is one of the indicators to measure the society welfare. Information about these relathionships were important to perform the relathionsips between GDP and poverty. So this research conducted an analysis to obtain simultaneous models between GDRP and poverty. Estimation of the parameters used is Two-Stage Least Squares Estimation (2SLS). The data used are 33 provinces in Indonesia at 2010. By α = 5%, it was conclude that variable which significant effect on GDRP is poverty, export, and import. Meanwhile, the variables that significantly affect poverty are population. The simultaneous model (α = 5%) also conclude that there is no simultaneous relationship between GDRP and poverty. However, with α = 25%, there is a simultaneous relationship between GDRP and poverty.

scholarly journals Profile Inferences on Restricted Additive Partially Linear EV Models

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Xiuli Wang
Keyword(s):  
Least Squares ◽  
Asymptotic Distribution ◽  
The Other ◽  
Finite Sample ◽  
Test Statistic ◽  
Two Stage ◽  
Chi Square ◽  
Partially Linear ◽  
Profile Least Squares ◽  
Restricted Estimator

We consider the testing problem for the parameter and restricted estimator for the nonparametric component in the additive partially linear errors-in-variables (EV) models under additional restricted condition. We propose a profile Lagrange multiplier test statistic based on modified profile least-squares method and two-stage restricted estimator for the nonparametric component. We derive two important results. One is that, without requiring the undersmoothing of the nonparametric components, the proposed test statistic is proved asymptotically to be a standard chi-square distribution under the null hypothesis and a noncentral chi-square distribution under the alternative hypothesis. These results are the same as the results derived by Wei and Wang (2012) for their adjusted test statistic. But our method does not need an adjustment and is easier to implement especially for the unknown covariance of measurement error. The other is that asymptotic distribution of proposed two-stage restricted estimator of the nonparametric component is asymptotically normal and has an oracle property in the sense that, though the other component is unknown, the estimator performs well as if it was known. Some simulation studies are carried out to illustrate relevant performances with a finite sample. The asymptotic distribution of the restricted corrected-profile least-squares estimator, which has not been considered by Wei and Wang (2012), is also investigated.

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