Hermann Singer
Biographic Data
| ID | 5805356 |
|---|---|
| NAME | Hermann Singer |
| GIVEN NAMES | Hermann |
| FAMILY NAME | Singer |
| SIGNATURE | SINGER H |
| AFFILIATIONS | FernUniversität in Hagen |
| VERIFIED | No |
| TOTAL WORKS | 10 |
| TOTAL CITATIONS | 15 |
| WORKS AS AUTHOR | 10 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 1938 |
| LATEST PUBLICATION YEAR | 2016 |
| H-INDEX | 3 |
SEM modeling with singular moment matrices Part III
We discuss generalized least squares (GLS) and maximum likelihood (ML) estimation for structural equations models (SEM), when the sample moment matrices are possibly singular. This occurs in several instances, for example, for panel data when there are more panel waves than independent replications or for time series data where the number of time points is large, but only one unit is observed. In previous articles, it was shown that ML estimation…
SEM Modeling with Singular Moment Matrices Part II
Linear stochastic differential equations are expressed as an exact discrete model (EDM) and estimated with structural equation models (SEMs) and the Kalman filter (KF) algorithm. The oversampling approach is introduced in order to formulate the EDM on a time grid which is finer than the sampling intervals. This leads to a simple computation of the nonlinear parameter functionals of the EDM. For small discretization intervals, the functionals can …
SEM Modeling with Singular Moment Matrices Part I
A structural equation model (SEM) with deterministic intercepts is introduced. The Gaussian likelihood function does not contain determinants of sample moment matrices and is thus well-defined for only one statistical unit. The SEM is applied to the dynamic state space model and compared with the Kalman filter (KF) approach. The likelihood of both methods are shown to be equivalent, but for long time series numerical problems occur in the SEM app…
Nonlinear continuous-discrete filtering using kernel density estimatesand functional integrals
We develop filter algorithms for nonlinear stochastic differential equations with discrete time measurements (continuous-discrete state space model). The apriori density (time update) is computed by Monte Carlo simulations of the Fokker-Planck equation using kernel density estimators and measurement updates are obtained by using the extended Kalman filter (EKF) updates. For small sampling intervals, a discretized continuous sampling approach (DCS…
Continuous panel models with time dependent parameters
It is shown how the likelihood function can be computed in this general linear setting by using a Kaiman filter algorithm. The estimation method is tested in a simulation study using a bivariate growth model and applied to the Brownian bridge. Panel data are modeled as dynamic structural equations in continuous time t (stochastic differential equations). The continuously moving latent state vector y(t) is mapped to an observable discrete time ser…
The aliasing‐phenomenon in visual terms
In a recent paper it was shown that the aliasing phenomenon, which leads to a severe identification problem in the estimation of stochastic differential equations, can be overcome by using a polygonal (or higher) approximation for the time paths of the exogenous variables. This work attempts to visualize the problem and presents several simulated trajectories of a continuous time AR(2)‐process (Ornstein‐Uhlenbeck‐process) together with the observ…
Problems with the estimation of stochastic differential equations using structural equations models
The present paper deals with the identification and maximum likelihood estimation of systems of linear stochastic differential equations using panel data. So we only have a sample of discrete observations over time of the relevant variables for each individual. A popular approach in the social sciences advocates the estimation of the “exact discrete model” after a reparameterization with LISREL or similar programs for structural equations models.…
Success stories of the 1970s
Profit Measurement and the Trade Cycle
Profit Measurement and the Trade Cycle Get access H. W. Singer H. W. Singer Lake Success, U.S. A Search for other works by this author on: Oxford Academic Google Scholar The Economic Journal, Volume 58, Issue 232, 1 December 1948, Pages 594–596, https://doi.org/10.2307/2226195 Published: 01 December 1948
Price Dispersion in Periods of Change
The aliasing‐phenomenon in visual terms
In a recent paper it was shown that the aliasing phenomenon, which leads to a severe identification problem in the estimation of stochastic differential equations, can be overcome by using a polygonal (or higher) approximation for the time paths of the exogenous variables. This work attempts to visualize the problem and presents several simulated trajectories of a continuous time AR(2)‐process (Ornstein‐Uhlenbeck‐process) together with the observ…
Continuous panel models with time dependent parameters
It is shown how the likelihood function can be computed in this general linear setting by using a Kaiman filter algorithm. The estimation method is tested in a simulation study using a bivariate growth model and applied to the Brownian bridge. Panel data are modeled as dynamic structural equations in continuous time t (stochastic differential equations). The continuously moving latent state vector y(t) is mapped to an observable discrete time ser…
Problems with the estimation of stochastic differential equations using structural equations models
The present paper deals with the identification and maximum likelihood estimation of systems of linear stochastic differential equations using panel data. So we only have a sample of discrete observations over time of the relevant variables for each individual. A popular approach in the social sciences advocates the estimation of the “exact discrete model” after a reparameterization with LISREL or similar programs for structural equations models.…
Success stories of the 1970s
SEM Modeling with Singular Moment Matrices Part I
A structural equation model (SEM) with deterministic intercepts is introduced. The Gaussian likelihood function does not contain determinants of sample moment matrices and is thus well-defined for only one statistical unit. The SEM is applied to the dynamic state space model and compared with the Kalman filter (KF) approach. The likelihood of both methods are shown to be equivalent, but for long time series numerical problems occur in the SEM app…
Price Dispersion in Periods of Change
Profit Measurement and the Trade Cycle
Profit Measurement and the Trade Cycle Get access H. W. Singer H. W. Singer Lake Success, U.S. A Search for other works by this author on: Oxford Academic Google Scholar The Economic Journal, Volume 58, Issue 232, 1 December 1948, Pages 594–596, https://doi.org/10.2307/2226195 Published: 01 December 1948
Success stories of the 1970s
Problems with the estimation of stochastic differential equations using structural equations models
The present paper deals with the identification and maximum likelihood estimation of systems of linear stochastic differential equations using panel data. So we only have a sample of discrete observations over time of the relevant variables for each individual. A popular approach in the social sciences advocates the estimation of the “exact discrete model” after a reparameterization with LISREL or similar programs for structural equations models.…
The aliasing‐phenomenon in visual terms
In a recent paper it was shown that the aliasing phenomenon, which leads to a severe identification problem in the estimation of stochastic differential equations, can be overcome by using a polygonal (or higher) approximation for the time paths of the exogenous variables. This work attempts to visualize the problem and presents several simulated trajectories of a continuous time AR(2)‐process (Ornstein‐Uhlenbeck‐process) together with the observ…
Continuous panel models with time dependent parameters
It is shown how the likelihood function can be computed in this general linear setting by using a Kaiman filter algorithm. The estimation method is tested in a simulation study using a bivariate growth model and applied to the Brownian bridge. Panel data are modeled as dynamic structural equations in continuous time t (stochastic differential equations). The continuously moving latent state vector y(t) is mapped to an observable discrete time ser…
Nonlinear continuous-discrete filtering using kernel density estimatesand functional integrals
We develop filter algorithms for nonlinear stochastic differential equations with discrete time measurements (continuous-discrete state space model). The apriori density (time update) is computed by Monte Carlo simulations of the Fokker-Planck equation using kernel density estimators and measurement updates are obtained by using the extended Kalman filter (EKF) updates. For small sampling intervals, a discretized continuous sampling approach (DCS…
SEM Modeling with Singular Moment Matrices Part I
A structural equation model (SEM) with deterministic intercepts is introduced. The Gaussian likelihood function does not contain determinants of sample moment matrices and is thus well-defined for only one statistical unit. The SEM is applied to the dynamic state space model and compared with the Kalman filter (KF) approach. The likelihood of both methods are shown to be equivalent, but for long time series numerical problems occur in the SEM app…
SEM Modeling with Singular Moment Matrices Part II
Linear stochastic differential equations are expressed as an exact discrete model (EDM) and estimated with structural equation models (SEMs) and the Kalman filter (KF) algorithm. The oversampling approach is introduced in order to formulate the EDM on a time grid which is finer than the sampling intervals. This leads to a simple computation of the nonlinear parameter functionals of the EDM. For small discretization intervals, the functionals can …
SEM modeling with singular moment matrices Part III
We discuss generalized least squares (GLS) and maximum likelihood (ML) estimation for structural equations models (SEM), when the sample moment matrices are possibly singular. This occurs in several instances, for example, for panel data when there are more panel waves than independent replications or for time series data where the number of time points is large, but only one unit is observed. In previous articles, it was shown that ML estimation…
Applied Mathematics (7 works) · Applied Mathematics (7 works) · Mathematics (7 works) · Physics (6 works) · Statistics (6 works) · Stochastic differential equation (5 works) · Kalman filter (4 works) · Computer Science (3 works) · Gaussian (3 works) · Mathematical analysis (3 works)