scholarly journals Fundamental Responsiveness in European Electricity Prices

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7623
Author(s):  
Michail I. Seitaridis ◽  
Nikolaos S. Thomaidis ◽  
Pandelis N. Biskas
Keyword(s):  
Panel Data ◽  
Electricity Markets ◽  
Data Model ◽  
Cointegration Analysis ◽  
Power Delivery ◽  
Panel Data Model ◽  
Leading Indicators ◽  
Electricity Prices ◽  
Analysis Techniques ◽  
Insight Into

We estimate fundamental pricing relationships in selected European day-ahead electricity markets. Using a fractionally integrated panel data model with unobserved common effects, we quantify the responsiveness of hourly electricity prices to two fundamental leading indicators of day-ahead markets: the predicted load and renewable generation. The application of fractional cointegration analysis techniques gives further insight into the pricing mechanism of power delivery contracts, enabling us to measure the persistence of fundamental shocks.

NONLINEAR PANEL DATA MODELS WITH DISTRIBUTION-FREE CORRELATED RANDOM EFFECTS

2021 ◽  
pp. 1-25
Author(s):  
Yu-Chin Hsu ◽  
Ji-Liang Shiu
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Conditional Distribution ◽  
Unobserved Heterogeneity ◽  
Random Effect ◽  
Data Models ◽  
Panel Data Model ◽  
Panel Data Models ◽  
Likelihood Functions ◽  
Correlated Random Effects

Under a Mundlak-type correlated random effect (CRE) specification, we first show that the average likelihood of a parametric nonlinear panel data model is the convolution of the conditional distribution of the model and the distribution of the unobserved heterogeneity. Hence, the distribution of the unobserved heterogeneity can be recovered by means of a Fourier transformation without imposing a distributional assumption on the CRE specification. We subsequently construct a semiparametric family of average likelihood functions of observables by combining the conditional distribution of the model and the recovered distribution of the unobserved heterogeneity, and show that the parameters in the nonlinear panel data model and in the CRE specification are identifiable. Based on the identification result, we propose a sieve maximum likelihood estimator. Compared with the conventional parametric CRE approaches, the advantage of our method is that it is not subject to misspecification on the distribution of the CRE. Furthermore, we show that the average partial effects are identifiable and extend our results to dynamic nonlinear panel data models.

A panel data model of length of stay in hospitals for hip replacements

2021 ◽  
Vol 40 (7) ◽  
pp. 688-707
Author(s):  
Yan Meng ◽  
Jiti Gao ◽  
Xibin Zhang ◽  
Xueyan Zhao
Keyword(s):  
Panel Data ◽  
Length Of Stay ◽  
Data Model ◽  
Panel Data Model ◽  
Hip Replacements
Sign in / Sign up

Export Citation Format

Share Document