Accurately predicting heat transfer performance of ground-coupled heat pump system using improved autoregressive model. [PDF]
PeerJ Comput Sci, 2021 Zhuang Z, Zhai X, Ben X, Wang B, Yuan D.
europepmc +1 more source
Prediction of transcriptional profiles of Synechocystis PCC6803 by dynamic autoregressive modeling of DNA microarray data [PDF]
, 2003 William A. Schmitt +1 more
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Climate Change and Investors' Behaviour: Assessing a New Type of Systematic Risk
International Journal of Finance &Economics, EarlyView.ABSTRACT This study explores how temperature anomalies, a novel form of systematic risk, affect financial markets, expanding the traditional understanding of market‐wide risks. While climate change is becoming an important consideration, the extent to which temperature anomalies disrupt economic activities and influence stock returns is urgently needed
Natthinee Thampanya, Junjie Wu
wiley +1 more source
Examining the dynamic effect of COVID-19 pandemic on dwindling oil prices using structural vector autoregressive model. [PDF]
Energy (Oxf), 2021 Adedeji AN, Ahmed FF, Adam SU.
europepmc +1 more source
Ability of the Bispectral Index, Autoregressive Modelling with Exogenous Input-derived Auditory Evoked Potentials, and Predicted Propofol Concentrations to Measure Patient Responsiveness during Anesthesia with Propofol and Remifentanil [PDF]
, 2003 Michel Struys +7 more
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Determinants of Dividend Payout Policy: More Evidence From Emerging Markets of G20 Bloc
International Journal of Finance &Economics, EarlyView.ABSTRACT The purpose of this article is to examine the key factors influencing dividend payout policy in emerging markets, using a quantitative approach with a sample of 938 firms and 19,698 firm‐year observations. The study considers dividends, and share repurchases as elements of payout, analysing the effect of earnings, taxes, debt, size and free ...
Wagner Dantas de Souza Junior +2 more
wiley +1 more source
The Output Cost of Disinflation in Traditional and Vector Autoregressive Models [PDF]
, 2003 Stephen R. King
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Deep Learning for Bond Yield Forecasting: The LSTM‐LagLasso
International Journal of Finance &Economics, EarlyView.ABSTRACT We present long short‐term memory (LSTM)‐LagLasso, a novel explainable deep learning approach applied to bond yield forecasting. Our method involves feature selection from a large universe of potential features and forecasts bond yields using dynamic LSTM networks.
Manuel Nunes +4 more
wiley +1 more source
Noninformative priors and frequentist risks of bayesian estimators of vector-autoregressive models [PDF]
, 2003 Shawn Ni, Dongchu Sun
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