A Model for the Relationship between Work Attitudes and Beliefs of Knowledge Workers with Their Turnover Intention

Growing of science and technology and extending of knowledge-based organizations, the development and maintenance of high-performance knowledge workers with high-potential will be very critical institutionally and nationally. This study aimed to explore the predictability of knowledge workers’ intention to stay through their work attitudes and beliefs including believe in reliability of managers, job satisfaction and organizational commitment in the context of the academic community of Iran. Standard questionnaires were used to measure the variables. In order to gather data, stratified random sampling of faculty members in colleges and higher education institutions in South Khorasan, was accomplished. The results obtained in method of path analysis with AMOS software show that believe of knowledge workers in reliability of managers and organizational commitment can anticipate intend to stay. Among them, trust in management has the strongest indirect effect on the intention to stay. Also, job satisfaction through organizational commitment can predict the intention to stay of knowledge workers

Testing for Noncausal Vector Autoregressive Representation

We propose a test for noncausal vector autoregressive representation generated by non-Gaussian shocks. We prove that in these models the Wold innovations are martingale difference if and only if the model is correctly specified. We propose a test based on a generalized spectral density to check for martingale difference property of the Wold innovations. Our approach does not require to identify and estimate the noncausal models. No specific estimation method is required, and the test has the appealing nuisance parameter free property. The test statistic uses all lags in the sample and it has a convenient asymptotic standard normal distribution under the null hypothesis. A Monte Carlo study is conducted to examine the �finite-sample performance of our test

Testing for Noncausal Vector Autoregressive Representation

We propose a test for noncausal vector autoregressive representation generated by non-Gaussian shocks. We prove that in these models the Wold innovations are martingale difference if and only if the model is correctly specified. We propose a test based on a generalized spectral density to check for martingale difference property of the Wold innovations. Our approach does not require to identify and estimate the noncausal models. No specific estimation method is required, and the test has the appealing nuisance parameter free property. The test statistic uses all lags in the sample and it has a convenient asymptotic standard normal distribution under the null hypothesis. A Monte Carlo study is conducted to examine the �finite-sample performance of our test

News, Noise, and Tests of Present Value Models

I use a present value framework to explore the e�ects of news (or noisy information) onstock prices and drive theoretical restrictions that link price volatility to noise and information. In particular, I show that market e�ciency implies that noise cannot explain more than half of price uctuations. I propose a novel methodology to decompose stock prices into a value component, related to information about future economic fundamentals, and a noise component. The key observation is that noise by construction cannot change future economic fundamentals, but affects stock prices. The advantage of my approach is that it does not require any particular assumptions on unobserved discount rates and econometricians' information set. Consistent with the predictions of the model, my estimates show that in the prewar period noise explains up to 28% of the S&P 500 index, and 36% in the postwar period. Finally, I �nd that the U.S. stock market was undervalued during the 1970s and overvalued during the 1990s, but there is no evidence that the market was overvalued before the crash of 1929

Are the shocks obtained from SVAR fundamental?

This paper provides new conditions under which the shocks recovered from the estimates of structural vector autoregressions are fundamental. I prove that the Wold innovations are unpredictable if and only if the model is fundamental. I propose a test based on a generalized spectral density to check the unpredictability of the Wold innovations. The test is applied to study the dynamic effects of government spending on economic activity. I find that standard SVAR models commonly employed in the literature are non-fundamental. Moreover, I formally show that introduction of a narrative variable that measures anticipation restores fundamentalness

Olving tri-level linear programming problem by a novel hybrid algorithm

This paper presents a revised hybrid algorithm to solve a tri-level linear programming problem, a generalization of a bi-level one, involving three decision makers at the upper, middle, and lower levels. The decision-making priority is from top to bottom and the decision of each decision maker affects the decision space of others. A hybrid algorithm has been already proposed to solve this problem, but it does not ensure to converge whereas the proposed novel revised algorithm lacks this drawback and ensures convergence.Publisher's Versio

Are the shocks obtained from SVAR fundamental?

This paper provides new conditions under which the shocks recovered from the estimates of structural vector autoregressions are fundamental. I prove that the Wold innovations are unpredictable if and only if the model is fundamental. I propose a test based on a generalized spectral density to check the unpredictability of the Wold innovations. The test is applied to study the dynamic effects of government spending on economic activity. I find that standard SVAR models commonly employed in the literature are non-fundamental. Moreover, I formally show that introduction of a narrative variable that measures anticipation restores fundamentalness

Testing for Non-Fundamentalness

Non-fundamentalness arises when observed variables do not contain enough information to recover structural shocks. This paper propose a new test to empirically detect non-fundamentalness, which is robust to the conditional heteroskedasticity of unknown form, does not need information outside of the specified model and could be accomplished with a standard F-test. A Monte Carlo study based on a DSGE model is conducted to examine the finite sample performance of the test. I apply the proposed test to the U.S. quarterly data to identify the dynamic effects of supply and demand disturbances on real GNP and unemployment

Testing for Non-Fundamentalness

Non-fundamentalness arises when observed variables do not contain enough information to recover structural shocks. This paper propose a new test to empirically detect non-fundamentalness, which is robust to the conditional heteroskedasticity of unknown form, does not need information outside of the specified model and could be accomplished with a standard F-test. A Monte Carlo study based on a DSGE model is conducted to examine the finite sample performance of the test. I apply the proposed test to the U.S. quarterly data to identify the dynamic effects of supply and demand disturbances on real GNP and unemployment