Results 221 to 230 of about 1,779,916 (322)
LASSO vector autoregression structures for very short-term wind power forecasting
, 2017 L. Cavalcante +3 more
semanticscholar +1 more source
Macroeconomic Expectations in a War
Scottish Journal of Political Economy, EarlyView.ABSTRACT Using short‐ and long‐term macroeconomic forecasts, we estimate the projected cost of the Russian full‐scale invasion of Ukraine for countries in Eastern Europe, the Caucasus, and Central Asia. Shortly after the invasion, the projected cumulative cost over 6 years stood at $2.44 trillion for the region.
Yuriy Gorodnichenko, Vittal Vasudevan
wiley +1 more source
Journal of Forecasting, Volume 45, Issue 4, Page 2059-2077, July 2026.ABSTRACT This paper presents a new hybrid model for predicting German electricity prices. The algorithm is based on a combination of Gaussian process regression (GPR) and support vector regression (SVR). Although GPR is a competent model for learning stochastic patterns within data and for interpolation, its performance for out‐of‐sample data is not ...
Abhinav Das +2 more
wiley +1 more source
Econometrics at the Extreme: From Quantile Regression to QFAVAR1
Journal of Economic Surveys, Volume 40, Issue 3, Page 1672-1686, July 2026.ABSTRACT This paper surveys quantile modelling from its theoretical origins to current advances. We organize the literature and present core econometric formulations and estimation methods for: (i) cross‐sectional quantile regression; (ii) quantile time series models and their time series properties; (iii) quantile vector autoregressions for ...
Stéphane Goutte +4 more
wiley +1 more source
Extremely Fast Maximum Likelihood Estimation of High‐Order Autoregressive Models
Journal of Time Series Analysis, Volume 47, Issue 4, Page 876-884, July 2026.ABSTRACT We consider the problem of exact maximum likelihood estimation of potentially high‐order (p>50) autoregressive models. We propose an extremely fast coordinate‐wise algorithm for fitting autoregressive models. This fast algorithm exploits several properties of the negative log‐likelihood when parameterised in terms of partial autocorrelations ...
Daniel F. Schmidt, Enes Makalic
wiley +1 more source
Solar power forecasting with sparse vector autoregression structures
2017 IEEE Manchester PowerTech, 2017 L. Cavalcante, R. Bessa
semanticscholar +1 more source
On the Comovement of Contango and Backwardation Across Futures Commodity Markets
Journal of Futures Markets, Volume 46, Issue 6, Page 955-981, June 2026.ABSTRACT We examine the time‐varying nature of the comovement of the slope of the futures curve in major agricultural, metals and energy commodity futures markets in a Global Vector Autoregressive model. We find significant comovement between the slopes, indicating the co‐existence of backwardation and contango in many seemingly unrelated commodity ...
Angelo Luisi +2 more
wiley +1 more source
What Explains International Interest Rate Co‐Movement?
Journal of Applied Econometrics, Volume 41, Issue 4, Page 343-359, June/July 2026.ABSTRACT The international co‐movement of interest rates reflects correlated business‐cycle fluctuations, largely driven by demand shocks. Monetary policy in advanced economies follows domestic mandates—inflation and the output gap—and does not respond to foreign policy shocks.
Annika Camehl, Gregor von Schweinitz
wiley +1 more source
The U.S. economy in 1990 and 1991: continued expansion likely
This paper reports an optimistic forecast of U.S. output and inflation trends in 1990_91. Generated by a Bayesian vector autoregression (BVAR) model of the U.S.
David E. Runkle
core
Forecasting Related Time Series
Journal of Applied Econometrics, Volume 41, Issue 4, Page 481-498, June/July 2026.ABSTRACT A collection of time series are “related” if they follow similar stochastic processes and/or they are statistically dependent. This paper proposes a related time series (RTS) forecasting model that exploits these relationships. The model's foundation is a set of univariate Gaussian autoregressions, one for each series, which are then augmented
Ulrich K. Müller, Mark W. Watson
wiley +1 more source