This title appears in the Scientific Report :
2018
Fitting a Complex Markov Chain Model for Firm and Market Productivity
Fitting a Complex Markov Chain Model for Firm and Market Productivity
This thesis develops a methodology of estimating parameters for a complex Markov chainmodel for rm productivity. The model consists of two Markov chains, one describing rmlevelproductivity and the other modeling the productivity of the whole market. If applicable,the model can be used to help with o...
Saved in:
Personal Name(s): | Valder, Julia (Corresponding author) |
---|---|
Contributing Institute: |
Jülich Supercomputing Center; JSC |
Imprint: |
2018
|
Physical Description: |
vii, 59 p. |
Dissertation Note: |
Masterarbeit, University of Wisconsin-Milwaukee, 2018 |
Document Type: |
Master Thesis |
Research Program: |
Computational Science and Mathematical Methods |
Publikationsportal JuSER |
This thesis develops a methodology of estimating parameters for a complex Markov chainmodel for rm productivity. The model consists of two Markov chains, one describing rmlevelproductivity and the other modeling the productivity of the whole market. If applicable,the model can be used to help with optimal decision making problems for labor demand. Theneed for such a model is motivated and the economical background of this research is shown.A brief introduction to the concept of Markov chains and their application in this contextis given. The simulated data that is being used for the estimation is presented in detail.The underlying economical problem is described as a stochastic process. Available data fora single rm is limited, therefore a 2-step method is used to estimate the probability matrixfor the rm Markov chain. Under a time homogeneity assumption, maximum likelihoodestimation techniques are used to estimate the parameters of a Markov chain for one rmbased on all rms in the market. These parameters are rened using a linear combinationapproach. The expectation and variance of the proposed estimator are analyzed. Themethod's validity is established using various goodness-of-t tests. Theoretical explorationsfor the estimation of a market Markov chain are made. In the end, a summary of resultsand an outlook for further research directions is given. |