Shahnaz Parsaeian
Biographic Data
| ID | 8920054 |
|---|---|
| NAME | Shahnaz Parsaeian |
| GIVEN NAMES | Shahnaz |
| FAMILY NAME | Parsaeian |
| SIGNATURE | PARSAEIAN S |
| AFFILIATIONS | University of Kansas |
| VERIFIED | No |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Shrinkage Estimation and Identification of Latent Group Structures in Panel Data with Interactive Fixed Effects
This paper provides a framework for joint shrinkage estimation and identification of latent group structures in panel data models with interactive fixed effects and large number of explanatory variables. The latent structure of the model allows individuals to be classified into a number of groups where the number of groups and/or each individual’s group identity are unknown. A doubly penalized principal component estimation procedure using a pair…
Optimal forecast under structural breaks
This paper develops an optimal combined estimator to forecast out‐of‐sample under structural breaks. When it comes to forecasting, using only the postbreak observations after the most recent break point may not be optimal. In this paper, we propose a new estimation method that exploits the prebreak information. In particular, we show how to combine the estimator using the full‐sample (i.e., both the prebreak and postbreak data) and the estimator …
Forecasting Under Structural Breaks Using Improved Weighted Estimation
In forecasting a time series containing a structural break, it is important to determine how much weight can be given to the observations prior to the time when the break occurred. In this context, Pesaran et al . (2013) (PPP) proposed a weighted least squares estimator by giving different weights to observations before and after a break point for forecasting out‐of‐sample. We revisit their approach by introducing an improved weighted generalized…
No prominent works on this page.
Optimal forecast under structural breaks
This paper develops an optimal combined estimator to forecast out‐of‐sample under structural breaks. When it comes to forecasting, using only the postbreak observations after the most recent break point may not be optimal. In this paper, we propose a new estimation method that exploits the prebreak information. In particular, we show how to combine the estimator using the full‐sample (i.e., both the prebreak and postbreak data) and the estimator …
Forecasting Under Structural Breaks Using Improved Weighted Estimation
In forecasting a time series containing a structural break, it is important to determine how much weight can be given to the observations prior to the time when the break occurred. In this context, Pesaran et al . (2013) (PPP) proposed a weighted least squares estimator by giving different weights to observations before and after a break point for forecasting out‐of‐sample. We revisit their approach by introducing an improved weighted generalized…
Shrinkage Estimation and Identification of Latent Group Structures in Panel Data with Interactive Fixed Effects
This paper provides a framework for joint shrinkage estimation and identification of latent group structures in panel data models with interactive fixed effects and large number of explanatory variables. The latent structure of the model allows individuals to be classified into a number of groups where the number of groups and/or each individual’s group identity are unknown. A doubly penalized principal component estimation procedure using a pair…
Econometrics (2 works) · Estimator (2 works) · Mathematics (2 works) · Monetary Policy and Economic Impact (2 works) · Statistics (2 works) · Applied Mathematics (1 work) · Bias of an estimator (1 work) · Combinatorics (1 work) · Computer Science (1 work) · Consistent estimator (1 work)