Details about Anastasios Nicholas Panagiotelis

Access statistics for papers by Anastasios Nicholas Panagiotelis.

Last updated 2020-08-16. Update your information in the RePEc Author Service.

Short-id: ppa802

Working Papers

2020

1. Forecast Reconciliation: A geometric View with New Insights on Bias Correction
   Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics View citations (3)
   Also in Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics (2019) View citations (3)
2. Probabilistic Forecast Reconciliation: Properties, Evaluation and Score Optimisation
   Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics View citations (8)

2019

3. Forecasting Swiss Exports Using Bayesian Forecast Reconciliation
   Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics
4. Hierarchical Forecasting
   Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics View citations (6)
5. Updating Variational Bayes: Fast Sequential Posterior Inference
   Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics View citations (2)

2018

6. Probabilisitic forecasts in hierarchical time series
   Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics View citations (12)

2017

7. Bayesian Inference for a 1-Factor Copula Model
   Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics View citations (3)
8. Bayesian Weighted Inference from Surveys "Abstract: Data from large surveys are often supplemented with sampling weights that are designed to reflect unequal probabilities of response and selection inherent in complex survey sampling methods. We propose two methods for Bayesian estimation of parametric models in a setting where the survey data and the weights are available, but where information on how the weights were constructed is unavailable. The first approach is to simply replace the likelihood with the pseudo likelihood in the formulation of Bayes theorem. This is proven to lead to a consistent estimator but also leads to credible intervals that suffer from systematic undercoverage. Our second approach involves using the weights to generate a representative sample which is embedded within a Markov chain Monte Carlo (MCMC) or other simulation algorithm designed to estimate the parameters of the model. In extensive simulation studies, the latter methodology is shown to achieve performance comparable to the standard frequentist solution of pseudo maximum likelihood, with the added advantage of being applicable to models that require inference via MCMC. The methodology is demonstrated further by fitting a mixture of gamma densities to a sample of Australian household income."
   Department of Economics - Working Papers Series, The University of Melbourne
9. Macroeconomic forecasting for Australia using a large number of predictors
   Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics View citations (4)
   See also Journal Article Macroeconomic forecasting for Australia using a large number of predictors, International Journal of Forecasting, Elsevier (2019) View citations (14) (2019)
10. The Australian Macro Database: An online resource for macroeconomic research in Australia
    Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics View citations (1)
    Also in CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University (2017) View citations (1)

2016

11. Bayesian Rank Selection in Multivariate Regression
    Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics

2013

12. From Amazon to Apple: Modeling Online Retail Sales, Purchase Incidence and Visit Behavior
    Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics
    See also Journal Article From Amazon to Apple: Modeling Online Retail Sales, Purchase Incidence, and Visit Behavior, Journal of Business & Economic Statistics, Taylor & Francis Journals (2014) View citations (3) (2014)

Journal Articles

2019

1. A forecast reconciliation approach to cause-of-death mortality modeling
   Insurance: Mathematics and Economics, 2019, 86, (C), 122-133 View citations (15)
2. Macroeconomic forecasting for Australia using a large number of predictors
   International Journal of Forecasting, 2019, 35, (2), 616-633 View citations (14)
   See also Working Paper Macroeconomic forecasting for Australia using a large number of predictors, Monash Econometrics and Business Statistics Working Papers (2017) View citations (4) (2017)
3. Probabilistic forecast reconciliation with applications to wind power and electric load
   European Journal of Operational Research, 2019, 279, (2), 364-379 View citations (43)

2017

4. Model selection for discrete regular vine copulas
   Computational Statistics & Data Analysis, 2017, 106, (C), 138-152 View citations (11)

2014

5. From Amazon to Apple: Modeling Online Retail Sales, Purchase Incidence, and Visit Behavior
   Journal of Business & Economic Statistics, 2014, 32, (1), 14-29 View citations (3)
   See also Working Paper From Amazon to Apple: Modeling Online Retail Sales, Purchase Incidence and Visit Behavior, Monash Econometrics and Business Statistics Working Papers (2013) (2013)

2012

6. Pair Copula Constructions for Multivariate Discrete Data
   Journal of the American Statistical Association, 2012, 107, (499), 1063-1072 View citations (50)

2010

7. Bayesian skew selection for multivariate models
   Computational Statistics & Data Analysis, 2010, 54, (7), 1824-1839 View citations (5)

2008

8. Bayesian density forecasting of intraday electricity prices using multivariate skew t distributions
   International Journal of Forecasting, 2008, 24, (4), 710-727 View citations (89)
9. Bayesian identification, selection and estimation of semiparametric functions in high-dimensional additive models
   Journal of Econometrics, 2008, 143, (2), 291-316 View citations (24)