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Social and Behavioral Sciences Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
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- Forecasting and Time Series (6)
- Copula Modeling (4)
- Copulas (2)
- Vector Autoregression (2)
- Asymmetry (1)
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- Bayesian Model Averaging (1)
- Bayesian Model Averaging and Semiparametric Regression (1)
- Bayesian Model Selection (1)
- Bayesian Monotonic Function Estimation (1)
- Bayesian methods (1)
- Bayesian semiparametric regression (1)
- Bayesian variable selection (1)
- Central bank intervention (1)
- Continuous Ranked Probability Score (1)
- Copula Model (1)
- Copula Multivariate Time Series Model; Gaussian Copula; Real-Time Density Forecasting; Survey of Professional Forecasters; Vine Copula (1)
- Copula Time Series Model. (1)
- Data Augmentation (1)
- Density Forecasts (1)
- Drawable Vines (1)
- Electricity Demand (1)
- Electricity Spot Price Forecasting (1)
- Foreign Exchange Returns (1)
- Foreign exchange volume (1)
- Heteroskedasticity (1)
- Inflation Forecasting (1)
- Intra-day electricity load modelling (1)
- Intraday Electricity Prices (1)
- Intrinsic Gaussian Markov random fields (1)
- Longitudinal Model (1)
Articles 1 - 12 of 12
Full-Text Articles in Social and Behavioral Sciences
Inversion Copulas From Nonlinear State Space Models With An Application To Inflation Forecasting, Michael S. Smith, Worapree Ole Maneesoonthorn
Inversion Copulas From Nonlinear State Space Models With An Application To Inflation Forecasting, Michael S. Smith, Worapree Ole Maneesoonthorn
Michael Stanley Smith
Time Series Copulas For Heteroskedastic Data, Ruben Loaiza-Maya, Michael S. Smith, Worapree Maneesoonthorn
Time Series Copulas For Heteroskedastic Data, Ruben Loaiza-Maya, Michael S. Smith, Worapree Maneesoonthorn
Michael Stanley Smith
Econometric Modeling Of Regional Electricity Spot Prices In The Australian Market, Michael S. Smith, Thomas S. Shively
Econometric Modeling Of Regional Electricity Spot Prices In The Australian Market, Michael S. Smith, Thomas S. Shively
Michael Stanley Smith
Variational Bayes Estimation Of Discrete-Margined Copula Models With Application To Ime Series, Ruben Loaiza-Maya, Michael S. Smith
Variational Bayes Estimation Of Discrete-Margined Copula Models With Application To Ime Series, Ruben Loaiza-Maya, Michael S. Smith
Michael Stanley Smith
Asymmetric Forecast Densities For U.S. Macroeconomic Variables From A Gaussian Copula Model Of Cross-Sectional And Serial Dependence, Michael S. Smith, Shaun Vahey
Asymmetric Forecast Densities For U.S. Macroeconomic Variables From A Gaussian Copula Model Of Cross-Sectional And Serial Dependence, Michael S. Smith, Shaun Vahey
Michael Stanley Smith
Copula Modelling Of Dependence In Multivariate Time Series, Michael S. Smith
Copula Modelling Of Dependence In Multivariate Time Series, Michael S. Smith
Michael Stanley Smith
From Amazon To Apple: Modeling Online Retail Sales, Purchase Incidence And Visit Behavior, Anastasios Panagiotelis, Michael S. Smith, Peter Danaher
From Amazon To Apple: Modeling Online Retail Sales, Purchase Incidence And Visit Behavior, Anastasios Panagiotelis, Michael S. Smith, Peter Danaher
Michael Stanley Smith
In this study we propose a multivariate stochastic model for website visit duration, page views, purchase incidence and the sale amount for online retailers. The model is constructed by composition from carefully selected distributions, and involves copula components. It allows for the strong nonlinear relationships between the sales and visit variables to be explored in detail, and can be used to construct sales predictions. The model is readily estimated using maximum likelihood, making it an attractive choice in practice given the large sample sizes that are commonplace in online retail studies. We examine a number of top-ranked U.S. online retailers, …
Bicycle Commuting In Melbourne During The 2000s Energy Crisis: A Semiparametric Analysis Of Intraday Volumes, Michael S. Smith, Goeran Kauermann
Bicycle Commuting In Melbourne During The 2000s Energy Crisis: A Semiparametric Analysis Of Intraday Volumes, Michael S. Smith, Goeran Kauermann
Michael Stanley Smith
Cycling is attracting renewed attention as a mode of transport in western urban environments, yet the determinants of usage are poorly understood. In this paper we investigate some of these using intraday bicycle volumes collected via induction loops located at ten bike paths in the city of Melbourne, Australia, between December 2005 and June 2008. The data are hourly counts at each location, with temporal and spatial disaggregation allowing for the impact of meteorology to be measured accurately for the first time. Moreover, during this period petrol prices varied dramatically and the data also provide a unique opportunity to assess …
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 …