Karsten Reichold
Biographic Data
| ID | 8920864 |
|---|---|
| NAME | Karsten Reichold |
| GIVEN NAMES | Karsten |
| FAMILY NAME | Reichold |
| SIGNATURE | REICHOLD K |
| AFFILIATIONS | TU Wien |
| ORCID | 0000-0001-5980-356X |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Forecasting post-pandemic tourism demand: Random forests and calendar variables
Policy measures restricting international and domestic travel during the COVID-19 pandemic led to substantial distortions in tourism demand data, which continue to affect forecasting models trained on such samples. To address this issue, we employ a random forest approach to forecast monthly tourism demand using lagged values of the target variable and ex ante observable calendar variables as predictors. This specification allows the model to exp…
Bootstrap Inference in Cointegrating Regressions: Traditional and Self-Normalized Test Statistics
Traditional tests of hypotheses on the cointegrating vector are well known to suffer from severe size distortions in finite samples, especially when the data are characterized by large levels of endogeneity or error serial correlation.To address this issue, we combine a vector autoregressive (VAR) sieve bootstrap to construct critical values with a self-normalization approach that avoids direct estimation of long-run variance parameters when comp…
No prominent works on this page.
Bootstrap Inference in Cointegrating Regressions: Traditional and Self-Normalized Test Statistics
Traditional tests of hypotheses on the cointegrating vector are well known to suffer from severe size distortions in finite samples, especially when the data are characterized by large levels of endogeneity or error serial correlation.To address this issue, we combine a vector autoregressive (VAR) sieve bootstrap to construct critical values with a self-normalization approach that avoids direct estimation of long-run variance parameters when comp…
Forecasting post-pandemic tourism demand: Random forests and calendar variables
Policy measures restricting international and domestic travel during the COVID-19 pandemic led to substantial distortions in tourism demand data, which continue to affect forecasting models trained on such samples. To address this issue, we employ a random forest approach to forecast monthly tourism demand using lagged values of the target variable and ex ante observable calendar variables as predictors. This specification allows the model to exp…
Autocorrelation (1 works) · Autoregressive model (1 works) · Cointegration (1 works) · Complex Systems and Time Series Analysis (1 works) · Demand forecasting (1 works) · Digital Marketing and Social Media (1 works) · Diverse Aspects of Tourism Research (1 works) · Econometrics (1 works) · Economic and Environmental Valuation (1 works) · Economic Policies and Impacts (1 works)