Results 51 to 60 of about 63,609 (288)

Forecasting Count Data With Varying Dispersion: A Latent‐Variable Approach

Journal of Forecasting, EarlyView.
ABSTRACT Count data, such as product sales and disease case counts, are common in business forecasting and many areas of science. Although the Poisson distribution is the best known model for such data, its use is severely limited by its assumption that the dispersion is a fixed function of the mean, which rarely holds in real‐world scenarios.
Easton Huch, Candace Berrett, Mason Ferlic, Kimberly F. Sellers   +3 more
wiley   +1 more source

Forecasting House Prices: The Role of Market Interconnectedness

Journal of Forecasting, EarlyView.
ABSTRACT While the existing research uncovers interconnections between various housing markets, it largely ignores the question of whether such linkages can improve house price predictions. To address this issue, we proceed in two steps. First, we forecast disaggregated house price growth rates from Australia and China to determine whether ...
Zac Chen, George Milunovich, Shuping Shi, Ben Wang   +3 more
wiley   +1 more source

Volatility Analysis of Returns of Financial Assets Using a Bayesian Time-Varying Realized GARCH-Itô Model

Econometrics
In a stage of more and more complex and high-frequency financial markets, the volatility analysis is a cornerstone of modern financial econometrics with practical applications in portfolio optimization, derivative pricing, and systematic risk assessment.
Pathairat Pastpipatkul, Htwe Ko
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

An Agnostic Look at Bayesian Statistics and Econometrics [PDF]

Bayesians and non-Bayesians, often called frequentists, seem to be perpetually at logger- heads on fundamental questions of statistical inference. This paper takes as agnostic a stand as is possible for a practising frequentist, and tries to elicit a ...
Russell Davidson
core   +4 more sources

Modeling high-frequency financial data using R and Stan: A bayesian autoregressive conditional duration approach

Journal of Open Innovation: Technology, Market and Complexity
In econometrics, Autoregressive Conditional Duration (ACD) models use high-frequency economic or financial duration data, which mostly exhibit irregular time intervals.
Mosab I. Tabash, T. Muhammed Navas, P.V. Thayyib, Shazia Farhin, Athar Ali Khan, Azzam Hannoon   +5 more
doaj   +1 more source

Electricity Price Prediction Using Multikernel Gaussian Process Regression Combined With Kernel‐Based Support Vector Regression

Journal of Forecasting, EarlyView.
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, Stephan Schlüter, Lorenz Schneider   +2 more
wiley   +1 more source

Parametric and nonparametric inference in equilibrium job search models [PDF]

, 2008
Equilibrium job search models allow for labor markets with homogeneous workers and firms to yield nondegenerate wage densities. However, the resulting wage densities do not accord well with empirical regularities.
Koop, Gary
core   +1 more source

Term Spread Volatility as a Leading Indicator of Economic Activity

Journal of Forecasting, EarlyView.
ABSTRACT In this paper, we examine the macroeconomic predictive power of the volatility of the US Treasury yield curve slope (term spread volatility). Our forecasting exercise shows that US term spread volatility has significant predictive power for US industrial production and employment growth.
Anastasios Megaritis, Dimitrios Bakas, Theodora Bermpei, Athanasios Triantafyllou   +3 more
wiley   +1 more source

Robust Inference of Dynamic Covariance Using Wishart Processes and Sequential Monte Carlo

Entropy
Several disciplines, such as econometrics, neuroscience, and computational psychology, study the dynamic interactions between variables over time.
Hester Huijsdens, David Leeftink, Linda Geerligs, Max Hinne   +3 more
doaj   +1 more source