TIME-VARYING ESTIMATION AND DYNAMIC MODEL SELECTION WITH AN APPLICATION OF NETWORK DATA

2020 ◽  
Author(s):  
Lan Xue ◽  
Xinxin Shu ◽  
Annie Qu
Keyword(s):  
Model Selection ◽  
Dynamic Model ◽  
Network Data ◽  
Time Varying

scholarly journals Some Novel Bayesian Model Combination Schemes: An Application to Commodities Prices

2018 ◽  
Vol 10 (8) ◽  
pp. 2801 ◽  
Author(s):  
Krzysztof Drachal
Keyword(s):  
Model Selection ◽  
Dynamic Model ◽  
Bayesian Model ◽  
Model Averaging ◽  
Time Varying ◽  
Model Combination ◽  
Explanatory Variables ◽  
Time Varying Parameters ◽  
Dynamic Model Averaging ◽  
Commodities Prices

Forecasting commodities prices on vividly changing markets is a hard problem to tackle. However, being able to determine important price predictors in a time-varying setting is crucial for sustainability initiatives. For example, the 2000s commodities boom gave rise to questioning whether commodities markets become over-financialized. In case of agricultural commodities, it was questioned if the speculative pressures increase food prices. Recently, some newly proposed Bayesian model combination scheme has been proposed, i.e., Dynamic Model Averaging (DMA). This method has already been applied with success in certain markets. It joins together uncertainty about the model and explanatory variables and a time-varying parameters approach. It can also capture structural breaks and respond to market disturbances. Secondly, it can deal with numerous explanatory variables in a data-rich environment. Similarly, like Bayesian Model Averaging (BMA), Dynamic Model Averaging (DMA), Dynamic Model Selection (DMS) and Median Probability Model (MED) start from Time-Varying Parameters’ (TVP) regressions. All of these methods were applied to 69 spot commodities prices. The period between Dec 1983 and Oct 2017 was analysed. In approximately 80% of cases, according to the Diebold–Mariano test, DMA produced statistically significant more accurate forecast than benchmark forecasts (like the naive method or ARIMA). Moreover, amongst all the considered model types, DMA was in 22% of cases the most accurate one (significantly). MED was most often minimising the forecast errors (28%). However, in the text, it is clarified that this was due to some specific initial parameters setting. The second ”best” model type was MED, meaning that, in the case of model selection, relying on the highest posterior probability is not always preferable.

A dynamic model selection strategy for support vector machine classifiers

2012 ◽  
Vol 12 (8) ◽  
pp. 2550-2565 ◽  
Author(s):  
Marcelo N. Kapp ◽  
Robert Sabourin ◽  
Patrick Maupin
Keyword(s):  
Support Vector Machine ◽  
Model Selection ◽  
Dynamic Model ◽  
Selection Strategy ◽  
Support Vector

scholarly journals Separable and semiparametric network-based counting processes applied to the international combat aircraft trades

2021 ◽  
pp. 1-21
Author(s):  
Cornelius Fritz ◽  
Paul W. Thurner ◽  
Göran Kauermann
Keyword(s):  
Poisson Process ◽  
Random Effects ◽  
Network Data ◽  
Counting Processes ◽  
Time Varying ◽  
Political Factors ◽  
Combat Aircraft ◽  
Intensity Functions ◽  
Multivariate Poisson Process

Abstract We propose a novel tie-oriented model for longitudinal event network data. The generating mechanism is assumed to be a multivariate Poisson process that governs the onset and repetition of yearly observed events with two separate intensity functions. We apply the model to a network obtained from the yearly dyadic number of international deliveries of combat aircraft trades between 1950 and 2017. Based on the trade gravity approach, we identify economic and political factors impeding or promoting the number of transfers. Extensive dynamics as well as country heterogeneities require the specification of semiparametric time-varying effects as well as random effects. Our findings reveal strong heterogeneous as well as time-varying effects of endogenous and exogenous covariates on the onset and repetition of aircraft trade events.

Dynamic Analysis of Mechanical Systems With Time-Varying Topologies: Part 1 — Dynamic Model and Stability Analysis

1990 ◽  
Author(s):  
Yu Wang
Keyword(s):  
Stability Analysis ◽  
Dynamic Model ◽  
Local Stability ◽  
Global Analysis ◽  
Equations Of Motion ◽  
Mechanical Systems ◽  
Time Varying ◽  
Discrete Equations ◽  
Local Stability Analysis ◽  
Multiple Collisions

Abstract A model is developed for analyzing mechanical systems with a pair of bodies with topological changes in their kinematic constraints. It is built upon the concept of Poincaré map rather than following the traditional methods of differential equations. The model provides a set of well-defined and naturally-discrete equations of motion and is capable of giving physical insights of dynamic characteristics of deadbeat convergence of multiple collisions and periodic or chaotic responses. The development of dynamic model and a local stability analysis are presented in Part 1, and the global analysis and numerical simulation are discussed in Part 2.

scholarly journals Forecasting Carbon Emissions with Dynamic Model Averaging Approach: Time-Varying Evidence from China

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Siqi Xu ◽  
Yifeng Zhang ◽  
Xiaodan Chen
Keyword(s):  
Carbon Dioxide ◽  
Dynamic Model ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Predictive Power ◽  
Carbon Dioxide Emission ◽  
Model Averaging ◽  
Predictive Ability ◽  
Time Varying ◽  
Dynamic Model Averaging

Although energy-related factors, such as energy intensity and energy consumption, are well recognized as major drivers of carbon dioxide emission in China, little is known about the time-varying impacts of other macrolevel nonenergy factors on carbon emission, especially those from macroeconomic, financial, household, and technology progress indicators in China. This paper contributes to the literature by investigating the time-varying predictive ability of 15 macrolevel indicators for China’s carbon dioxide emission from 1982 to 2017 with a dynamic model averaging (DMA) method. The empirical results show that, firstly, the explanatory power of each nonenergy predictor changes significantly with time and no predictor has a stable positive/negative impact on China’s carbon emissions throughout the whole sample period. Secondly, all these predictors present a distinct predictive ability for carbon emission in China. The proportion of industry production in GDP (IP) shows the greatest predictive power, while the proportion of FDI in GDP has the smallest forecasting ability. Interestingly, those Chinese household features, such as Engel’s coefficient and household savings rate, play very important roles in the prediction of China’s carbon emission. In addition, we find that IP are losing its predictive power in recent years, while the proportion of value-added of the service sector in GDP presents not only a leading forecasting weight, but a continuous increasing prediction power in recent years. Finally, the dynamic model averaging (DMA) method can produce the most accurate forecasts of carbon emission in China compared to other commonly used forecasting methods.

scholarly journals Structural analysis for dynamic model of time‐varying switch in bond graph – a case study of single phase switch converters

2017 ◽  
Vol 10 (7) ◽  
pp. 756-766
Author(s):  
Kuo‐Hsiung Tseng ◽  
Tuo‐Wen Chang ◽  
Ming‐Fu Hung ◽  
Kuan‐Wen Chen
Keyword(s):  
Structural Analysis ◽  
Dynamic Model ◽  
Single Phase ◽  
Bond Graph ◽  
Time Varying

scholarly journals Bayesian inference in band excitation scanning probe microscopy for optimal dynamic model selection in imaging

2020 ◽  
Vol 128 (5) ◽  
pp. 054105 ◽  
Author(s):  
Rama K. Vasudevan ◽  
Kyle P. Kelley ◽  
Eugene Eliseev ◽  
Stephen Jesse ◽  
Hiroshi Funakubo ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Model Selection ◽  
Dynamic Model ◽  
Scanning Probe Microscopy ◽  
Scanning Probe ◽  
Probe Microscopy ◽  
Optimal Dynamic

scholarly journals Detecting Lineage-Specific Shifts in Diversification: A Proper Likelihood Approach

2020 ◽  
Author(s):  
Giovanni Laudanno ◽  
Bart Haegeman ◽  
Daniel L Rabosky ◽  
Rampal S Etienne
Keyword(s):  
Model Selection ◽  
Phylogenetic Tree ◽  
Time Dependent ◽  
Parameter Estimates ◽  
Time Varying ◽  
Diversification Rates ◽  
Branching Patterns ◽  
Recent Debate ◽  
Likelihood Approach ◽  
New Framework

Abstract The branching patterns of molecular phylogenies are generally assumed to contain information on rates of the underlying speciation and extinction processes. Simple birth–death models with constant, time-varying, or diversity-dependent rates have been invoked to explain these patterns. They have one assumption in common: all lineages have the same set of diversification rates at a given point in time. It seems likely, however, that there is variability in diversification rates across subclades in a phylogenetic tree. This has inspired the construction of models that allow multiple rate regimes across the phylogeny, with instantaneous shifts between these regimes. Several methods exist for calculating the likelihood of a phylogeny under a specified mapping of diversification regimes and for performing inference on the most likely diversification history that gave rise to a particular phylogenetic tree. Here, we show that the likelihood computation of these methods is not correct. We provide a new framework to compute the likelihood correctly and show, with simulations of a single shift, that the correct likelihood indeed leads to parameter estimates that are on average in much better agreement with the generating parameters than the incorrect likelihood. Moreover, we show that our corrected likelihood can be extended to multiple rate shifts in time-dependent and diversity-dependent models. We argue that identifying shifts in diversification rates is a nontrivial model selection exercise where one has to choose whether shifts in now-extinct lineages are taken into account or not. Hence, our framework also resolves the recent debate on such unobserved shifts. [Diversification; macroevolution; phylogeny; speciation]

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