Didier Nibbering
Biographic Data
| ID | 8880650 |
|---|---|
| NAME | Didier Nibbering |
| GIVEN NAMES | Didier |
| FAMILY NAME | Nibbering |
| SIGNATURE | NIBBERING D |
| AFFILIATIONS | Monash University |
| ORCID | 0000-0003-4398-4321 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 6 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Efficient Importance Variational Approximations for State Space Models
Variational methods are a potentially scalable estimation approach for state space models. However, existing methods are inaccurate or computationally infeasible for many state space models. This article proposes a variational approximation that is accurate and fast for any model with a closed-form measurement density function and a state transition distribution within the exponential family of distributions. Our approach constructs a variational…
Instrument-Based Estimation of Full Treatment Effects with Partial Compliers
The effect of the full treatment is a primary parameter of interest in policy evaluation, while often the effect of a subset of treatment is estimated. We partially identify the local average treatment effect of receiving full treatment (LAFTE) using an instrumental variable that may induce individuals into subsets of treatment (partial compliers). We show that partial compliers violate the standard exclusion restriction, necessary conditions on …
Random Subspace Local Projections
We show how random subspace methods can be adapted to estimating local projections with many controls. Random subspace methods have their roots in the machine learning literature and are implemented by averaging over regressions estimated over different combinations of subsets of these controls. We document three key results: (i) Our approach can successfully recover the impulse response functions across Monte Carlo experiments representative of …
A high‐dimensional multinomial logit model
The number of parameters in a standard multinomial logit model increases linearly with the number of choice alternatives and number of explanatory variables. Because many modern applications involve large choice sets with categorical explanatory variables, which enter the model as large sets of binary dummies, the number of parameters in a multinomial logit model is often large. This paper proposes a new method for data‐driven two‐way parameter c…
Fast Variational Bayes Methods for Multinomial Probit Models
The multinomial probit model is often used to analyze choice behavior. However, estimation with existing Markov chain Monte Carlo (MCMC) methods is computationally costly, which limits its applicability to large choice datasets. This article proposes a variational Bayes method that is accurate and fast, even when a large number of choice alternatives and observations are considered. Variational methods usually require an analytical expression for…
Scalable Bayesian Estimation in the Multinomial Probit Model
The multinomial probit (MNP) model is a popular tool for analyzing choice behavior as it allows for correlation between choice alternatives. Because current model specifications employ a full covariance matrix of the latent utilities for the choice alternatives, they are not scalable to a large number of choice alternatives. This article proposes a factor structure on the covariance matrix, which makes the model scalable to large choice sets. The…
No prominent works on this page.
Instrument-Based Estimation of Full Treatment Effects with Partial Compliers
The effect of the full treatment is a primary parameter of interest in policy evaluation, while often the effect of a subset of treatment is estimated. We partially identify the local average treatment effect of receiving full treatment (LAFTE) using an instrumental variable that may induce individuals into subsets of treatment (partial compliers). We show that partial compliers violate the standard exclusion restriction, necessary conditions on …
Random Subspace Local Projections
We show how random subspace methods can be adapted to estimating local projections with many controls. Random subspace methods have their roots in the machine learning literature and are implemented by averaging over regressions estimated over different combinations of subsets of these controls. We document three key results: (i) Our approach can successfully recover the impulse response functions across Monte Carlo experiments representative of …
Efficient Importance Variational Approximations for State Space Models
Variational methods are a potentially scalable estimation approach for state space models. However, existing methods are inaccurate or computationally infeasible for many state space models. This article proposes a variational approximation that is accurate and fast for any model with a closed-form measurement density function and a state transition distribution within the exponential family of distributions. Our approach constructs a variational…
A high‐dimensional multinomial logit model
The number of parameters in a standard multinomial logit model increases linearly with the number of choice alternatives and number of explanatory variables. Because many modern applications involve large choice sets with categorical explanatory variables, which enter the model as large sets of binary dummies, the number of parameters in a multinomial logit model is often large. This paper proposes a new method for data‐driven two‐way parameter c…
Fast Variational Bayes Methods for Multinomial Probit Models
The multinomial probit model is often used to analyze choice behavior. However, estimation with existing Markov chain Monte Carlo (MCMC) methods is computationally costly, which limits its applicability to large choice datasets. This article proposes a variational Bayes method that is accurate and fast, even when a large number of choice alternatives and observations are considered. Variational methods usually require an analytical expression for…
Scalable Bayesian Estimation in the Multinomial Probit Model
The multinomial probit (MNP) model is a popular tool for analyzing choice behavior as it allows for correlation between choice alternatives. Because current model specifications employ a full covariance matrix of the latent utilities for the choice alternatives, they are not scalable to a large number of choice alternatives. This article proposes a factor structure on the covariance matrix, which makes the model scalable to large choice sets. The…
Computer Science (6 works) · Mathematics (6 works) · Econometrics (5 works) · Statistics (4 works) · Algorithm (3 works) · Artificial Intelligence (3 works) · Consumer Market Behavior and Pricing (3 works) · Multinomial distribution (3 works) · Multinomial probit (3 works) · Bayesian probability (2 works)