Tom Boot
Biographic Data
| ID | 8920615 |
|---|---|
| NAME | Tom Boot |
| GIVEN NAMES | Tom |
| FAMILY NAME | Boot |
| SIGNATURE | BOOT T |
| AFFILIATIONS | University of Groningen |
| ORCID | 0000-0003-1329-6173 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2018 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Grouped Heterogeneity in Linear Panel Data Models with Heterogeneous Error Variances
We develop a procedure to identify latent group structures in linear panel data models that exploits a grouping in the error variances of cross-sectional units. To accommodate such grouping, we introduce an objective function that avoids a singularity that arises in a pseudolikelihood approach. We provide theoretical and numerical evidence showing when allowing for variance groups improves classification. The developed procedure provides new evid…
Unbiased estimation of the OLS covariance matrix when the errors are clustered
When data are clustered, common practice has become to do OLS and use an estimator of the covariance matrix of the OLS estimator that comes close to unbiasedness. In this paper, we derive an estimator that is unbiased when the random-effects model holds. We do the same for two more general structures. We study the usefulness of these estimators against others by simulation, the size of the t -test being the criterion. Our findings suggest that th…
Optimal Forecasts from Markov Switching Models
We derive forecasts for Markov switching models that are optimal in the mean square forecast error (MSFE) sense by means of weighting observations. We provide analytic expressions of the weights conditional on the Markov states and conditional on state probabilities. This allows us to study the effect of uncertainty around states on forecasts. It emerges that, even in large samples, forecasting performance increases substantially when the constru…
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Optimal Forecasts from Markov Switching Models
We derive forecasts for Markov switching models that are optimal in the mean square forecast error (MSFE) sense by means of weighting observations. We provide analytic expressions of the weights conditional on the Markov states and conditional on state probabilities. This allows us to study the effect of uncertainty around states on forecasts. It emerges that, even in large samples, forecasting performance increases substantially when the constru…
Unbiased estimation of the OLS covariance matrix when the errors are clustered
When data are clustered, common practice has become to do OLS and use an estimator of the covariance matrix of the OLS estimator that comes close to unbiasedness. In this paper, we derive an estimator that is unbiased when the random-effects model holds. We do the same for two more general structures. We study the usefulness of these estimators against others by simulation, the size of the t -test being the criterion. Our findings suggest that th…
Grouped Heterogeneity in Linear Panel Data Models with Heterogeneous Error Variances
We develop a procedure to identify latent group structures in linear panel data models that exploits a grouping in the error variances of cross-sectional units. To accommodate such grouping, we introduce an objective function that avoids a singularity that arises in a pseudolikelihood approach. We provide theoretical and numerical evidence showing when allowing for variance groups improves classification. The developed procedure provides new evid…
Computer Science (3 works) · Econometrics (3 works) · Mathematics (3 works) · Statistics (3 works) · Spatial and Panel Data Analysis (2 works) · Accounting (1 work) · Artificial Intelligence (1 work) · Best linear unbiased prediction (1 work) · Bias of an estimator (1 work) · Business cycle (1 work)