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Physical Sciences and Mathematics Commons™
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- Bayesian Model Averaging and Semiparametric Regression (5)
- Forecasting and Time Series (5)
- Electricity Demand (2)
- Multivariate Models in Marketing (2)
- Vector Autoregression (2)
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- Asymmetry (1)
- Bayesian Model Selection (1)
- Bayesian semiparametric regression (1)
- Bayesian variable selection (1)
- Central bank intervention (1)
- Continuous Ranked Probability Score (1)
- Copula Modeling (1)
- District Demand Heating (1)
- Electricity Spot Price Forecasting (1)
- Foreign exchange volume (1)
- Intra-day electricity load modelling (1)
- Intrinsic Gaussian Markov random fields (1)
- Longitudinal Model (1)
- Markov Chain Monte Carlo (1)
- Markov chain Monte Carlo (1)
- Missing observations (1)
- Multivariate stochastic volatility (1)
- P-splines (1)
- Parsimonious Covariance (1)
- Peak Load Forecasting (1)
- Penalized Spline Smoothing (1)
- Regularization (1)
- Seemingly Unrelated Regression (1)
- Seemingly unrelated regression (1)
- Threshold model (1)
Articles 1 - 10 of 10
Full-Text Articles in Physical Sciences and Mathematics
A Comparison Of Periodic Autoregressive And Dynamic Factor Models In Intraday Energy Demand Forecasting, Thomas Mestekemper, Goeran Kauermann, Michael Smith
A Comparison Of Periodic Autoregressive And Dynamic Factor Models In Intraday Energy Demand Forecasting, Thomas Mestekemper, Goeran Kauermann, Michael Smith
Michael Stanley Smith
We suggest a new approach for forecasting energy demand at an intraday resolution. Demand in each intraday period is modeled using semiparametric regression smoothing to account for calendar and weather components. Residual serial dependence is captured by one of two multivariate stationary time series models, with dimension equal to the number of intraday periods. These are a periodic autoregression and a dynamic factor model. We show the benefits of our approach in the forecasting of district heating demand in a steam network in Germany and aggregate electricity demand in the state of Victoria, Australia. In both studies, accounting for weather …
Rejoinder: Estimation Issues For Copulas Applied To Marketing Data, Peter Danaher, Michael Smith
Rejoinder: Estimation Issues For Copulas Applied To Marketing Data, Peter Danaher, Michael Smith
Michael Stanley Smith
Estimating copula models using Bayesian methods presents some subtle challenges, ranging from specification of the prior to computational tractability. There is also some debate about what is the most appropriate copula to employ from those available. We address these issues here and conclude by discussing further applications of copula models in marketing.
Forecasting Television Ratings, Peter Danaher, Tracey Dagger, Michael Smith
Forecasting Television Ratings, Peter Danaher, Tracey Dagger, Michael Smith
Michael Stanley Smith
Despite the state of flux in media today, television remains the dominant player globally for advertising spend. Since television advertising time is purchased on the basis of projected future ratings, and ad costs have skyrocketed, there is increasing pressure to forecast television ratings accurately. Previous forecasting methods are not generally very reliable and many have not been validated, but more distressingly, none have been tested in today’s multichannel environment. In this study we compare 8 different forecasting models, ranging from a naïve empirical method to a state-of-the-art Bayesian model-averaging method. Our data come from a recent time period, 2004-2008 in …
Bayesian Identification, Selection And Estimation Of Functions In High-Dimensional Additive Models, Anastasios Panagiotelis, Michael Smith
Bayesian Identification, Selection And Estimation Of Functions In High-Dimensional Additive Models, Anastasios Panagiotelis, Michael Smith
Michael Stanley Smith
In this paper we propose an approach to both estimate and select unknown smooth functions in an additive model with potentially many functions. Each function is written as a linear combination of basis terms, with coefficients regularized by a proper linearly constrained Gaussian prior. Given any potentially rank deficient prior precision matrix, we show how to derive linear constraints so that the corresponding effect is identified in the additive model. This allows for the use of a wide range of bases and precision matrices in priors for regularization. By introducing indicator variables, each constrained Gaussian prior is augmented with a …
Bayesian Density Forecasting Of Intraday Electricity Prices Using Multivariate Skew T Distributions, Anastasios Panagiotelis, Michael Smith
Bayesian Density Forecasting Of Intraday Electricity Prices Using Multivariate Skew T Distributions, Anastasios Panagiotelis, Michael Smith
Michael Stanley Smith
Electricity spot prices exhibit strong time series properties, including substantial periodicity, both inter-day and intraday serial correlation, heavy tails and skewness. In this paper we capture these characteristics using a first order vector autoregressive model with exogenous effects and a skew t distributed disturbance. The vector is longitudinal, in that it comprises observations on the spot price at intervals during a day. A band two inverse scale matrix is employed for the disturbance, as well as a sparse autoregressive coefficient matrix. This corresponds to a parsimonious dependency structure that directly relates an observation to the two immediately prior, and the …
Foreign Exchange Intervention By The Bank Of Japan: Bayesian Analysis Using A Bivariate Stochastic Volatility Model, Michael Smith, Andrew Pitts
Foreign Exchange Intervention By The Bank Of Japan: Bayesian Analysis Using A Bivariate Stochastic Volatility Model, Michael Smith, Andrew Pitts
Michael Stanley Smith
A bivariate stochastic volatility model is employed to measure the effect of intervention by the Bank of Japan (BOJ) on daily returns and volume in the USD/YEN foreign exchange market. Missing observations are accounted for, and a data-based Wishart prior for the precision matrix of the errors to the transition equation that is in line with the likelihood is suggested. Empirical results suggest there is strong conditional heteroskedasticity in the mean-corrected volume measure, as well as contemporaneous correlation in the errors to both the observation and transition equations. A threshold model is used for the BOJ reaction function, which is …
Bayesian Modelling And Forecasting Of Intra-Day Electricity Load, Remy Cottet, Michael Smith
Bayesian Modelling And Forecasting Of Intra-Day Electricity Load, Remy Cottet, Michael Smith
Michael Stanley Smith
With the advent of wholesale electricity markets there has been renewed focus on intra-day electricity load forecasting. This paper employs a multi-equation regression model with a diagonal first order stationary vector autoregresson (VAR) for modeling and forecasting intra-day electricity load. The correlation structure of the disturbances to the VAR and the appropriate subset of regressors are explored using Bayesian model selection methodology. The full spectrum of finite sample inference is obtained using a Bayesian Markov chain Monte Carlo sampling scheme. This includes the predictive distribution of load and the distribution of the time and level of daily peak load, something …
A Bayesian Approach To Bivariate Nonparametric Regression, Michael Smith, Robert Kohn
A Bayesian Approach To Bivariate Nonparametric Regression, Michael Smith, Robert Kohn
Michael Stanley Smith
No abstract provided.
Nonparametric Regression Using Bayesian Variable Selection, Michael Smith, Robert Kohn
Nonparametric Regression Using Bayesian Variable Selection, Michael Smith, Robert Kohn
Michael Stanley Smith
No abstract provided.
Finite Sample Performance Of Robust Bayesian Regression, Michael Smith, Sheather Simon, Kohn Robert
Finite Sample Performance Of Robust Bayesian Regression, Michael Smith, Sheather Simon, Kohn Robert
Michael Stanley Smith
No abstract provided.