Kerem Tuzcuoglu
Biographic Data
| ID | 8920069 |
|---|---|
| NAME | Kerem Tuzcuoglu |
| GIVEN NAMES | Kerem |
| FAMILY NAME | Tuzcuoglu |
| SIGNATURE | TUZCUOGLU K |
| AFFILIATIONS | Bank of Canada |
| ORCID | 0000-0002-2434-5789 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Sectoral Uncertainty
We propose a new empirical framework to estimate sectoral uncertainty from data-rich environments. We jointly decompose the conditional variance of economic time series into a common, a sector-specific, and an idiosyncratic component. By specifying a hierarchical-factor structure to stochastic volatility modeling, our framework combines both dimension reduction and flexibility. To estimate the model, we develop an efficient Markov Chain Monte Car…
Composite Likelihood Estimation of an Autoregressive Panel Ordered Probit Model with Random Effects
Modeling and estimating autocorrelated discrete data can be challenging. In this article, we use an autoregressive panel ordered probit model where the serial correlation in the discrete variable is driven by the autocorrelation in the latent variable. In such a nonlinear model, the presence of a lagged latent variable results in an intractable likelihood containing high-dimensional integrals. To tackle this problem, we use composite likelihoods …
No prominent works on this page.
Composite Likelihood Estimation of an Autoregressive Panel Ordered Probit Model with Random Effects
Modeling and estimating autocorrelated discrete data can be challenging. In this article, we use an autoregressive panel ordered probit model where the serial correlation in the discrete variable is driven by the autocorrelation in the latent variable. In such a nonlinear model, the presence of a lagged latent variable results in an intractable likelihood containing high-dimensional integrals. To tackle this problem, we use composite likelihoods …
Sectoral Uncertainty
We propose a new empirical framework to estimate sectoral uncertainty from data-rich environments. We jointly decompose the conditional variance of economic time series into a common, a sector-specific, and an idiosyncratic component. By specifying a hierarchical-factor structure to stochastic volatility modeling, our framework combines both dimension reduction and flexibility. To estimate the model, we develop an efficient Markov Chain Monte Car…
Econometrics (2 works) · Monetary Policy and Economic Impact (2 works) · Asymptotic distribution (1 work) · Autocorrelation (1 work) · Autoregressive model (1 work) · Computer Science (1 work) · Economics (1 work) · Energy, Environment, Economic Growth (1 work) · Estimator (1 work) · Financial Risk and Volatility Modeling (1 work)