This paper discusses the problem of statistical inference in multivariate linear regression models when the errors involved are non normally distributed. We consider multivariate t-distribution, a ...fat-tailed distribution, for the errors as alternative to normal distribution. Such non normality is commonly observed in working with many data sets, e.g., financial data that are usually having excess kurtosis. This distribution has a number of applications in many other areas of research as well. We use modified maximum likelihood estimation method that provides the estimator, called modified maximum likelihood estimator (MMLE), in closed form. These estimators are shown to be unbiased, efficient, and robust as compared to the widely used least square estimators (LSEs). Also, the tests based upon MMLEs are found to be more powerful than the similar tests based upon LSEs.
This study investigates the effects of the real exchange rate on job flows in Turkish manufacturing industries between 2006 and 2015 using data at the four-digit NACE Revision 2 level. Using dynamic ...panel data models, we find that a real appreciation increases gross and net job creation rates, and that the effect of appreciation is magnified as the exposure to international competitiveness of industries increases. We think that this is because Turkish manufacturing firms import a greater share of their inputs compared to the firms in developed countries. Hence, an appreciation creates more jobs because lower imported input costs enable firms to outcompete foreign producers.
In this paper the problem of estimation of location and scatter of multivariate nonnormal distributions is considered. Estimators are derived under a maximum likelihood setup by expressing the ...non-linear likelihood equations in the linear form. The resulting estimators are analytical expressions in terms of sample values and, hence, are easily computable and can also be manipulated analytically. These estimators are found to be remarkably more efficient and robust as compared to the least square estimators. They also provide more powerful tests in testing various relevant statistical hypotheses.
Firm size and job creation: evidence from Turkey Dogan, Ergun; Islam, M. Qamarul; Yazici, Mehmet
Economic research - Ekonomska istraživanja,
12/2017, Letnik:
30, Številka:
1
Journal Article, Paper
Odprti dostop
This study examines the relationship between firm size and job creation by using an extensive data set covering all non-farm Turkish businesses with 20 or more employees from 2003 to 2010. We find ...that small firms (firms with employees between 20 and 100 employees) have higher mean job flow rates (job creation, job destruction and net job creation rates) than large firms. Firm size and job flow rates are inversely related, and this relationship is especially prominent for firms with 50 employees or more. Although the overall pattern observed is also observed in both sectors, job creation rates in services are higher than the ones in manufacturing. The magnitudes of job destruction rates are comparable across sectors. Higher job creation rate in services but comparable job destruction rate results in higher net job creation rate in services. As for shares, only for smaller firms (20-49 and 50-99 size categories), job creation shares are greater than their shares in employment. But these firms have disproportionate job destruction shares as well. We also find that only the 20-49 category firms contribute to net job creation more than their share in employment. The smaller firms have high disproportionate shares in job creation and destruction in manufacturing and services as well.
In this study we investigate the problem of estimation and testing of hypotheses in multivariate linear regression models when the errors involved are assumed to be non-normally distributed. We ...consider the class of heavy-tailed distributions for this purpose. Although our method is applicable for any distribution in this class, we take the multivariate t-distribution for illustration. This distribution has applications in many fields of applied research such as Economics, Business, and Finance. For estimation purpose, we use the modified maximum likelihood method in order to get the so-called modified maximum likelihood estimates that are obtained in a closed form. We show that these estimates are substantially more efficient than least-square estimates. They are also found to be robust to reasonable deviations from the assumed distribution and also many data anomalies such as the presence of outliers in the sample, etc. We further provide test statistics for testing the relevant hypothesis regarding the regression coefficients.
In a simple multiple linear regression model, the design variables have traditionally been assumed to be non-stochastic. In numerous real-life situations, however, they are stochastic and non-normal. ...Estimators of parameters applicable to such situations are developed. It is shown that these estimators are efficient and robust. A real-life example is given.
We consider multiple linear regression models under nonnormality. We derive modified maximum likelihood estimators (MMLEs) of the parameters and show that they are efficient and robust. We show that ...the least squares esimators are considerably less efficient. We compare the efficiencies of the MMLEs and the M estimators for symmetric distributions and show that, for plausible alternatives to an assumed distribution, the former are more efficient. We provide real-life examples.
Data sets in numerous areas of application can be modelled by symmetric bivariate nonnormal distributions. Estimation of parameters in such situations is considered when the mean and variance of one ...variable is a linear and a positive function of the other variable. This is typically true of bivariate
t distribution. The resulting estimators are found to be remarkably efficient. Hypothesis testing procedures are developed and shown to be robust and powerful. Real life examples are given.
We study multiple linear regression model under non-normally distributed random error by considering the family of generalized secant hyperbolic distributions. We derive the estimators of model ...parameters by using
modified maximum likelihood methodology and explore the properties of the modified maximum likelihood estimators so obtained. We show that the proposed estimators are more efficient and robust than the commonly used least square estimators. We also develop the relevant test of hypothesis procedures and compared the performance of such tests vis-a-vis the classical tests that are based upon the least square approach.
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