Mansi Khurana - Academia.edu (original) (raw)
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Papers by Mansi Khurana
This paper quantifies the most likely trade effects of the euro introduction using a panel data s... more This paper quantifies the most likely trade effects of the euro introduction using a panel data set of 29 European economies extended over the period 1994 to 2011. For this purpose, a gravity model of international trade is used. Following the recommendations of Santos Silva and Tenreyro (2006) paper [The log of gravity. Rev Econ Stat. 88 (4), 641–658], the gravity equation is estimated using Poisson pseudo-maximum-likelihood (PPML) technique. The main finding of this study is that the introduction of the euro has a small but statistically significant effect on export flows of European economies. The PPML estimates report this effect (euro effect) to be around 7%. This effect, although small, matches with the findings of the recent studies.
Communications in Statistics - Theory and Methods, 2014
Singh et al. (1986) proposed an almost unbiased ridge estimator using Jackknife method that requi... more Singh et al. (1986) proposed an almost unbiased ridge estimator using Jackknife method that required transformation of the regression parameters. This article shows that the same method can be used to derive the Jackknifed ridge estimator of the original (untransformed) parameter without transformation. This method also leads in deriving easily the second order Jackknifed ridge that may reduce the bias further. We further investigate the performance of these estimators along with a recent method by Batah et al. (2008) called modified Jackknifed ridge theoretically as well as numerically.
Some Recent Advances in Mathematics and Statistics, 2013
Far East Journal of Mathematical Sciences (FJMS), 2018
In 2003, Liu [16] proposed a new estimator dealing with the problem of multicollinearity in linea... more In 2003, Liu [16] proposed a new estimator dealing with the problem of multicollinearity in linear regression model pointing out a drawback of ridge estimator used in this context. This new estimator, called Liu-type estimator was demonstrated to have lesser mean squared error than ridge estimator and ordinary least squares estimator, however, it may carry a large amount of bias. In the present paper, we propose different estimators in order to reduce the bias of Liu-type estimator, one using the Jackknife technique and other using the technique proposed in Kadiyala [11]. We also investigate the Bootstrap method of bias correction on the Liu-type estimator as well. The bias and mean squared error of these estimators have been compared using a simulation study as well as a numerical example.
In the present article, we propose a Jackknifed version of stochastic restricted ridge regression... more In the present article, we propose a Jackknifed version of stochastic restricted ridge regression (SRR) estimator given byÖzkale 17 following the lines of Singh et al. . The performance of Jackknifed estimator is investigated in terms of bias and mean square error with those of SRR estimator theoretically as well as with a numerical example.
New Trends in Mathematical Science
In multiple regression analysis, the use of ridge regression estimator over the conventional ordi... more In multiple regression analysis, the use of ridge regression estimator over the conventional ordinary least squares estimator was suggested by Hoerl and Kennard in 1970 to beat the problem of multicollinearity that may exist among the independent variables. Keeping this in mind, in the present study, the authors intend to develop and compare different confidence intervals for regression coefficients based on ridge regression estimator using bootstrap and jackknife methodology. For comparison, the coverage probabilities and confidence widths are calculated through a simulation study for the data which suffers from the problem of multicollinearity.
This paper quantifies the most likely trade effects of the euro introduction using a panel data s... more This paper quantifies the most likely trade effects of the euro introduction using a panel data set of 29 European economies extended over the period 1994 to 2011. For this purpose, a gravity model of international trade is used. Following the recommendations of Santos Silva and Tenreyro (2006) paper [The log of gravity. Rev Econ Stat. 88 (4), 641–658], the gravity equation is estimated using Poisson pseudo-maximum-likelihood (PPML) technique. The main finding of this study is that the introduction of the euro has a small but statistically significant effect on export flows of European economies. The PPML estimates report this effect (euro effect) to be around 7%. This effect, although small, matches with the findings of the recent studies.
Communications in Statistics - Theory and Methods, 2014
Singh et al. (1986) proposed an almost unbiased ridge estimator using Jackknife method that requi... more Singh et al. (1986) proposed an almost unbiased ridge estimator using Jackknife method that required transformation of the regression parameters. This article shows that the same method can be used to derive the Jackknifed ridge estimator of the original (untransformed) parameter without transformation. This method also leads in deriving easily the second order Jackknifed ridge that may reduce the bias further. We further investigate the performance of these estimators along with a recent method by Batah et al. (2008) called modified Jackknifed ridge theoretically as well as numerically.
Some Recent Advances in Mathematics and Statistics, 2013
Far East Journal of Mathematical Sciences (FJMS), 2018
In 2003, Liu [16] proposed a new estimator dealing with the problem of multicollinearity in linea... more In 2003, Liu [16] proposed a new estimator dealing with the problem of multicollinearity in linear regression model pointing out a drawback of ridge estimator used in this context. This new estimator, called Liu-type estimator was demonstrated to have lesser mean squared error than ridge estimator and ordinary least squares estimator, however, it may carry a large amount of bias. In the present paper, we propose different estimators in order to reduce the bias of Liu-type estimator, one using the Jackknife technique and other using the technique proposed in Kadiyala [11]. We also investigate the Bootstrap method of bias correction on the Liu-type estimator as well. The bias and mean squared error of these estimators have been compared using a simulation study as well as a numerical example.
In the present article, we propose a Jackknifed version of stochastic restricted ridge regression... more In the present article, we propose a Jackknifed version of stochastic restricted ridge regression (SRR) estimator given byÖzkale 17 following the lines of Singh et al. . The performance of Jackknifed estimator is investigated in terms of bias and mean square error with those of SRR estimator theoretically as well as with a numerical example.
New Trends in Mathematical Science
In multiple regression analysis, the use of ridge regression estimator over the conventional ordi... more In multiple regression analysis, the use of ridge regression estimator over the conventional ordinary least squares estimator was suggested by Hoerl and Kennard in 1970 to beat the problem of multicollinearity that may exist among the independent variables. Keeping this in mind, in the present study, the authors intend to develop and compare different confidence intervals for regression coefficients based on ridge regression estimator using bootstrap and jackknife methodology. For comparison, the coverage probabilities and confidence widths are calculated through a simulation study for the data which suffers from the problem of multicollinearity.