Results 101 to 110 of about 1,700,696 (345)

Dependence structure between business cycles and CO2 emissions in the U.S.: Evidence from the time-varying Markov-Switching Copula models

, 2019
The relationship between CO2 emissions and economic growth is well-examined. However, there is a gap in the literature to examine the nexus by regime-switching models.
Giray Gozgor, A. Tiwari, Naceur Khraief, M. Shahbaz   +3 more
semanticscholar   +1 more source

The dependence structure between crude oil futures prices and Chinese agricultural commodity futures prices: Measurement based on Markov-switching GRG copula

Energy, 2019
The relational measurement based on Markov-switching GRG copula constructed by this paper is harnessed to analyze the dependence structure between WTI (BRENT) crude oil futures price and 12 kinds of Chinese agricultural commodity futures prices.
X. Liu, Fei Pan, Lin Yuan, Yu-wang Chen
semanticscholar   +1 more source

Optimizing Electric Vehicle Charging Scheduling With Deep Q Networks and Long Short‐Term Memory‐Based Electricity and Battery State of Charge Prediction

Energy Science &Engineering, EarlyView.
Schematic diagram showing the proposed approach for EV charging/discharging. ABSTRACT The number of electric vehicles (EVs) on the road is rising as a result of recent advancements in EV technology, and EVs are important to the smart grid economy. Demand response schemes involving electric vehicles have the potential to dramatically reduce the cost of ...
F. Zonuntluanga, Subhasish Deb, Kshetrimayum Robert Singh, Subir Datta, Ahmet Onen, Taha Selim Ustun, Jaesung Jung   +6 more
wiley   +1 more source

Markov-Switching Model Selection Using Kullback-Leibler Divergence [PDF]

In Markov-switching regression models, we use Kullback-Leibler (KL) divergence between the true and candidate models to select the number of states and variables simultaneously.
Naik, Prasad A., Smith, Aaron D., Tsai, Chih-Ling   +2 more
core   +1 more source

Pemodelan Markov Switching Autoregressive [PDF]

, 2014
Transition from depreciation to appreciation of exchange rate is one of regime switching that ignored by classic time series model, such as ARIMA, ARCH, or GARCH.
Ariyani, F. D. (Fiqria), Warsito, B. (Budi), Yasin, H. (Hasbi)   +2 more
core  

A Comparison of Realized Measures of Integrated Volatility: Price Duration‐ vs. Return‐Based Approaches

Journal of Forecasting, EarlyView.
ABSTRACT We study the accuracy of a variety of parametric price duration‐based realized variance estimators constructed via various financial duration models and compare their forecasting performance with the performance of various nonparametric return‐based realized variance estimators.
Björn Schulte‐Tillmann, Mawuli Segnon, Timo Wiedemann   +2 more
wiley   +1 more source

Analysing the response of CO2 emissions to business cycle in a developing economy: evidence for South Africa post-apartheid era

Frontiers in Environmental Science
Introduction: This research addresses the response of CO2 emissions to economic fluctuations in South Africa post-Apartheid, covering the period 1990–2018.
Delphin Kamanda Espoir, Regret Sunge, Biyase Mduduzi, Biyase Mduduzi, Frank Bannor, Simion Matsvai   +5 more
doaj   +1 more source

Regime‐Dependent Nowcasting of the Austrian Economy

Journal of Forecasting, EarlyView.
ABSTRACT We nowcast and forecast economic activity in Austria, namely, real gross domestic product (GDP), consumption, and investment, which are available at a quarterly frequency, using a preselected number of monthly indicators based on a combination of statistical procedures.
Jaroslava Hlouskova, Ines Fortin
wiley   +1 more source

Bayesian Analysis of Markov Switching Vector Error Correction Model [PDF]

This paper introduces a Bayesian approach to a Markov switching vector error correction model that allows for regime shifts in the intercept terms, the lag terms, the adjustment terms and the variance-covariance matrix.
Sugita, Katsuhiro
core  

Using DSGE and Machine Learning to Forecast Public Debt for France

Journal of Forecasting, EarlyView.
ABSTRACT Forecasting public debt is essential for effective policymaking and economic stability, yet traditional approaches face challenges due to data scarcity. While machine learning (ML) has demonstrated success in financial forecasting, its application to macroeconomic forecasting remains underexplored, hindered by short historical time series and ...
Emmanouil Sofianos, Thierry Betti, Theophilos Papadimitriou, Amélie Barbier‐Gauchard, Periklis Gogas   +4 more
wiley   +1 more source