Add to ORKGStandard procedures for parameter estimation in autoregressive models use maximum likelihood method based on normal random error distribution assumption. However, this thesis is focused on non-standard approaches for parameter estimation in non-negative time series based on the assumption of exponential probability distribution. Both standard and non-standard approaches were tested on forex time series and the results summarized in the thesis
The use of linear parametric models for forecasting economic time series is widespread among practit...
In the thesis, we examine a new approach to the analysis of certain forms of nonstationarity in macr...
The methods and algorithms of time series analysis play an important role in financial econometrics ...
Standard procedures for parameter estimation in autoregressive models use maximum likelihood method ...
This doctoral thesis is comprised of four papers that all relate to the subject of Time Series Analy...
Time series models have been widely used in simulating financial data sets. Finding a nice way to es...
Time series models have been widely used in simulating financial data sets. Finding a nice way to es...
The thesis studies nonlinear nonparametric models used in time series analy- sis. It gives basic int...
The modified maximum-likelihood method has recently been applied to some non-normal time series mode...
New strategies for the implementation of maximum likelihood estimation of nonlinear time series mode...
For over a decade, nonparametric modelling has been successfully applied to study nonlinear structur...
A non-Gaussian autoregressive model with epsilon-skew-normal innovations is introduced. Moments and ...
In this diploma thesis we study basic models of time series, both parametric and nonparametric, and ...
In recent years, analysis of financial time series focuses largely on data related to market trading...
We investigate the estimation methods of the multivariate non-stationary errors-in-variables models ...
The use of linear parametric models for forecasting economic time series is widespread among practit...
In the thesis, we examine a new approach to the analysis of certain forms of nonstationarity in macr...
The methods and algorithms of time series analysis play an important role in financial econometrics ...
Standard procedures for parameter estimation in autoregressive models use maximum likelihood method ...
This doctoral thesis is comprised of four papers that all relate to the subject of Time Series Analy...
Time series models have been widely used in simulating financial data sets. Finding a nice way to es...
Time series models have been widely used in simulating financial data sets. Finding a nice way to es...
The thesis studies nonlinear nonparametric models used in time series analy- sis. It gives basic int...
The modified maximum-likelihood method has recently been applied to some non-normal time series mode...
New strategies for the implementation of maximum likelihood estimation of nonlinear time series mode...
For over a decade, nonparametric modelling has been successfully applied to study nonlinear structur...
A non-Gaussian autoregressive model with epsilon-skew-normal innovations is introduced. Moments and ...
In this diploma thesis we study basic models of time series, both parametric and nonparametric, and ...
In recent years, analysis of financial time series focuses largely on data related to market trading...
We investigate the estimation methods of the multivariate non-stationary errors-in-variables models ...
The use of linear parametric models for forecasting economic time series is widespread among practit...
In the thesis, we examine a new approach to the analysis of certain forms of nonstationarity in macr...
The methods and algorithms of time series analysis play an important role in financial econometrics ...