Essays on Econometric Theory
(1998) In Lund Economic Studies 78. Abstract
 This dissertation contains a variety of contributions to econometric theory. Broadly speaking, econometrics may be categorized depending on what type of data is being analyzed, the two main categories being time series data and cross sectional data. A third category is panel data, combining cross sectional data observed over time. This is the area at which this dissertation is aimed. Panel data models may themselves be divided into sub models depending on what the explanatory variable looks like. In the most common type of model, the explanatory variable is a continuous variable. In another class of models, the explanatory variable is time to an event. The analysis of such models is called Duration Analysis. Duration analysis utilizes... (More)
 This dissertation contains a variety of contributions to econometric theory. Broadly speaking, econometrics may be categorized depending on what type of data is being analyzed, the two main categories being time series data and cross sectional data. A third category is panel data, combining cross sectional data observed over time. This is the area at which this dissertation is aimed. Panel data models may themselves be divided into sub models depending on what the explanatory variable looks like. In the most common type of model, the explanatory variable is a continuous variable. In another class of models, the explanatory variable is time to an event. The analysis of such models is called Duration Analysis. Duration analysis utilizes panel data since the explanatory variables are measured over time as well as over individuals/firms/countries. The first paper, Behind the Diffusion Curve: An Analysis of ATM Adoption, coauthored with Sunil Sharma, is aimed at making contributions to duration analysis. An important aspect of applying duration analysis in economics is the fact that the data is grouped, something which is typically not the case in the other fields. The first paper suggests a new method of evaluating the fit of the model when the data is grouped. The second paper, Evaluating the Proportionality Assumptions for the Duration of Unemployment is also aimed at making contributions to duration analysis. The models used in economics for studying time to an event is almost exclusively based on the Cox’s proportional hazard model. There are many examples where such a specification is unrealistic. This paper suggests a new method of testing the proportionality assumption that has several advantages. The third paper, Stochastic Frontier Production Functions with Errors in Variables is meant to make a contribution to the literature on cross sectional data when you have measurement errors in the explanatory variables (labor and capital). The model I consider is the one studied by Aigner et al (1977) specifying the production function as a CobbDouglas. This paper develops a procedure for consistently estimating the parameters in the production function and the technical efficiencies when there is measurement errors but where the reliability of the data is known. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/39049
 author
 Jochumzen, Peter ^{LU}
 supervisor
 opponent

 Brännäs, Curt
 organization
 publishing date
 1998
 type
 Thesis
 publication status
 published
 subject
 keywords
 economic theory, econometrics, Economics, measure of fit, unemployment insurance, non proportionality, stochastic frontier production function, diffusion of technology, Duration analysis, economic systems, economic policy, Nationalekonomi, ekonometri, ekonomisk teori, ekonomiska system, ekonomisk politik
 in
 Lund Economic Studies
 volume
 78
 pages
 128 pages
 publisher
 Department of Economics, Lund University
 defense location
 EC1:239
 defense date
 19981027 10:00:00
 ISSN
 04600029
 language
 English
 LU publication?
 yes
 id
 817f60e74ad34660abcac5295e162d0c (old id 39049)
 date added to LUP
 20160401 16:52:36
 date last changed
 20190521 16:21:45
@phdthesis{817f60e74ad34660abcac5295e162d0c, abstract = {This dissertation contains a variety of contributions to econometric theory. Broadly speaking, econometrics may be categorized depending on what type of data is being analyzed, the two main categories being time series data and cross sectional data. A third category is panel data, combining cross sectional data observed over time. This is the area at which this dissertation is aimed. Panel data models may themselves be divided into sub models depending on what the explanatory variable looks like. In the most common type of model, the explanatory variable is a continuous variable. In another class of models, the explanatory variable is time to an event. The analysis of such models is called Duration Analysis. Duration analysis utilizes panel data since the explanatory variables are measured over time as well as over individuals/firms/countries. The first paper, Behind the Diffusion Curve: An Analysis of ATM Adoption, coauthored with Sunil Sharma, is aimed at making contributions to duration analysis. An important aspect of applying duration analysis in economics is the fact that the data is grouped, something which is typically not the case in the other fields. The first paper suggests a new method of evaluating the fit of the model when the data is grouped. The second paper, Evaluating the Proportionality Assumptions for the Duration of Unemployment is also aimed at making contributions to duration analysis. The models used in economics for studying time to an event is almost exclusively based on the Cox’s proportional hazard model. There are many examples where such a specification is unrealistic. This paper suggests a new method of testing the proportionality assumption that has several advantages. The third paper, Stochastic Frontier Production Functions with Errors in Variables is meant to make a contribution to the literature on cross sectional data when you have measurement errors in the explanatory variables (labor and capital). The model I consider is the one studied by Aigner et al (1977) specifying the production function as a CobbDouglas. This paper develops a procedure for consistently estimating the parameters in the production function and the technical efficiencies when there is measurement errors but where the reliability of the data is known.}, author = {Jochumzen, Peter}, issn = {04600029}, language = {eng}, publisher = {Department of Economics, Lund University}, school = {Lund University}, series = {Lund Economic Studies}, title = {Essays on Econometric Theory}, volume = {78}, year = {1998}, }