Ovidijus Stauskas
Biographic Data
| ID | 8920836 |
|---|---|
| NAME | Ovidijus Stauskas |
| GIVEN NAMES | Ovidijus |
| FAMILY NAME | Stauskas |
| SIGNATURE | STAUSKAS O |
| AFFILIATIONS | Department of Economics BI Norwegian Business School Oslo Norway |
| ORCID | 0000-0002-5326-8794 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 5 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Robust Tests of Forecast Accuracy for Factor‐Augmented Regressions With an Application to the Novel EA‐MD‐QD Dataset
We present four novel tests of equal predictive accuracy and encompassing á Pitarakis (2023, 2025) for factor‐augmented regressions. Factors are estimated using cross‐section averages (CAs) of grouped series and our theoretical findings are empirically relevant: asymptotic normality, robustness to an overspecification of the number of factors, tractability of different degrees of predictor persistence, and invariance to the location of structural…
Cross-Section Bootstrap for CCE Regressions with General Unknown Factors
The Common Correlated Effects (CCE) approach enjoys considerable popularity for estimating factor-augmented panel data models. A key benefit is that by orthogonalizing the data on the available cross-section averages, the unobserved components are eliminated from the data, regardless of their order(s) of integration. This obviates the need for such knowledge, and makes CCE particularly attractive for macroeconomic applications, where the set of u…
Handling Distinct Correlated Effects with CCE
The common correlated effects (CCE) approach by Pesaran is a popular method for estimating panel data models with interactive effects. Due to its simplicity, i.e., unobserved common factors are approximated with cross‐section averages of the observables, the estimator is highly flexible and lends itself to a wide range of applications. Despite such flexibility, however, the properties of CCE estimators are typically only examined under the restri…
Complete Theory for CCE Under Heterogeneous Slopes and General Unknown Factors
A recent study of Westerlund (CCE in Panels with General Unknown Factors, The Econometrics Journal , 21 , 264‐276, 2018) showed that a very popular common correlated effects (CCE) estimator is significantly more applicable than it was thought before. Specifically, the common factors can have much more general time series properties than stationarity. This helps to alleviate the uncertainty over deterministic model components (e.g. trends) since t…
Tests of Equal Forecasting Accuracy for Nested Models with Estimated CCE Factors
In this article, we propose new tests of equal predictive ability between nested models when factor-augmented regressions are used to forecast. In contrast to the previous literature, the unknown factors are not estimated by principal components but by the common correlated effects (CCE) approach, which employs cross-sectional averages of blocks of variables. This makes for easy interpretation of the estimated factors, and the resulting tests are…
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Tests of Equal Forecasting Accuracy for Nested Models with Estimated CCE Factors
In this article, we propose new tests of equal predictive ability between nested models when factor-augmented regressions are used to forecast. In contrast to the previous literature, the unknown factors are not estimated by principal components but by the common correlated effects (CCE) approach, which employs cross-sectional averages of blocks of variables. This makes for easy interpretation of the estimated factors, and the resulting tests are…
Complete Theory for CCE Under Heterogeneous Slopes and General Unknown Factors
A recent study of Westerlund (CCE in Panels with General Unknown Factors, The Econometrics Journal , 21 , 264‐276, 2018) showed that a very popular common correlated effects (CCE) estimator is significantly more applicable than it was thought before. Specifically, the common factors can have much more general time series properties than stationarity. This helps to alleviate the uncertainty over deterministic model components (e.g. trends) since t…
Handling Distinct Correlated Effects with CCE
The common correlated effects (CCE) approach by Pesaran is a popular method for estimating panel data models with interactive effects. Due to its simplicity, i.e., unobserved common factors are approximated with cross‐section averages of the observables, the estimator is highly flexible and lends itself to a wide range of applications. Despite such flexibility, however, the properties of CCE estimators are typically only examined under the restri…
Robust Tests of Forecast Accuracy for Factor‐Augmented Regressions With an Application to the Novel EA‐MD‐QD Dataset
We present four novel tests of equal predictive accuracy and encompassing á Pitarakis (2023, 2025) for factor‐augmented regressions. Factors are estimated using cross‐section averages (CAs) of grouped series and our theoretical findings are empirically relevant: asymptotic normality, robustness to an overspecification of the number of factors, tractability of different degrees of predictor persistence, and invariance to the location of structural…
Cross-Section Bootstrap for CCE Regressions with General Unknown Factors
The Common Correlated Effects (CCE) approach enjoys considerable popularity for estimating factor-augmented panel data models. A key benefit is that by orthogonalizing the data on the available cross-section averages, the unobserved components are eliminated from the data, regardless of their order(s) of integration. This obviates the need for such knowledge, and makes CCE particularly attractive for macroeconomic applications, where the set of u…
Spatial and Panel Data Analysis (4 works) · Econometrics (3 works) · Mathematics (3 works) · Monetary Policy and Economic Impact (3 works) · Statistics (3 works) · Computer Science (2 works) · Energy, Environment, Economic Growth (2 works) · Estimator (2 works) · Factor analysis (2 works) · Fiscal Policy and Economic Growth (2 works)