Functional-structural plant (FSP) models have been widely used to understand the complex interactions between plant architecture and underlying developmental mechanisms. However, to obtain evidence ...that a model captures these mechanisms correctly, a clear distinction must be made between model outputs used for calibration and thus verification, and outputs used for validation. In pattern-oriented modelling (POM), multiple verification patterns are used as filters for rejecting unrealistic model structures and parameter combinations, while a second, independent set of patterns is used for validation.
To test the potential of POM for FSP modelling, a model of avocado (Persea americana 'Hass') was developed. The model of shoot growth is based on a conceptual model, the annual growth module (AGM), and simulates photosynthesis and adaptive carbon allocation at the organ level. The model was first calibrated using a set of observed patterns from a published article. Then, for validation, model predictions were compared with a different set of empirical patterns from various field studies that were not used for calibration.
After calibration, our model simultaneously reproduced multiple observed architectural patterns. The model then successfully predicted, without further calibration, the validation patterns. The model supports the hypothesis that carbon allocation can be modelled as being dependent on current organ biomass and sink strength of each organ type, and also predicted the observed developmental timing of the leaf sink-source transition stage.
These findings suggest that POM can help to improve the 'structural realism' of FSP models, i.e. the likelihood that a model reproduces observed patterns for the right reasons. Structural realism increases predictive power so that the response of an AGM to changing environmental conditions can be predicted. Accordingly, our FSP model provides a better but still parsimonious understanding of the mechanisms underlying known patterns of AGM growth.
We develop an omnibus specification test for multivariate continuous-time models using the conditional characteristic function, which often has a convenient closed-form or can be accurately ...approximated for many multivariate continuous-time models in finance and economics. The proposed test fully exploits the information in the joint conditional distribution of underlying economic processes and hence is expected to have good power in a multivariate context. A class of easy-to-interpret diagnostic procedures is supplemented to gauge possible sources of model misspecification. Our tests are also applicable to discrete-time distribution models. Simulation studies show that the tests provide reliable inference in finite samples.
This paper investigates the estimation and inference issues of heterogeneous coefficients in panel data models with common shocks. We propose a novel two-step method to estimate the heterogeneous ...coefficients. We establish the asymptotic theory of our estimators, including consistency, asymptotic representation, and limiting distribution. Our two-step method can effectively address the limitations of the existing methods, such as the common correlated effects method proposed by Pesaran (2006) and the iterated principal components method proposed by Song (2013). The two-step estimator is as efficient as the two existing competitors in the basic model, and more efficient in the model with zero restrictions. Intensive Monte Carlo simulations show that the proposed estimator performs robustly in a variety of data setups.
The Atlantic meridional overturning circulation (AMOC) is central to the climate of the Atlantic by redistributing mass, heat, and freshwater. Hydrographic sections help monitor its strength at ...different latitudes, and inverse box models provide estimates of AMOC, heat, and freshwater transports. We have used all available hydrographic zonal sections at 24.5°N and 30°S over the last 30 years to conclude that single section inverse models agree with monthly outputs from an ocean general circulation model at the time of the cruise. In contrast, inverse models using multiple sections at different latitudes and times of the year for each of the last three decades are more consistent with decadal averages from the same model. Therefore, solutions of inverse models with single sections are affected by aliasing and represent the state of the ocean at the time of cruise. However, aliasing is greatly reduced when using multiple sections to assess low‐frequency variability.
Plain Language Summary
Heat and nutrients in the Atlantic Ocean are redistributed through a process called Atlantic meridional overturning circulation, which is being monitored to detect changes in its strength. Hydrographic data offer the possibility to assess this variability, although it can be influenced by the ocean dynamics happening at the time of the cruise. When comparing the results from inverse box models applied to single sections with numerical model output, the best fit appears for the time of the cruise. This reflects that the monthly variation affects the solutions of the inverse model, which can be understood as representative of the duration of the cruise. Inverse models using several sections from cruises carried out in different times of the year and different years average out the local temporal phenomena that can affect the results and better represent the decadal average of the numerical models.
Key Points
The Atlantic meridional overturning circulation has been monitored with hydrographic data and now basin‐wide arrays enable the detection of high‐frequency variability
Inverse solutions from single sections are affected by aliasing, as they capture the circulation structure of the time of the cruise
Inverse models with multiple sections at different latitudes and times agree with decadal averages from an ocean general circulation model
The hydraulic resistance of groynes is an important factor in the determination of design flood water levels on rivers and the assessment of how much these levels are lowered by modifying the ...groynes. In standard one‐ or two‐dimensional numerical hydrodynamic models for flood risk management, groynes are commonly represented as subgrid features with a local energy loss according to a weir formula. We tested this representation by using a two‐dimensional horizontal mesh at various groyne submergence degrees by comparing the results with those of flume experiments. We also compared the results with simulations using different 2D and 3D approaches on finer grids that incorporate groynes in the bed topography. In one of the two tested 3D models, complete Reynolds‐averaged Navier‐Stokes equations were solved. The second tested 3D model was constructed simpler by assuming hydrostatic pressure distribution in the vertical direction. We employed Delft3D software in construction and execution of all models. One of the 3D models did predict the hydraulic resistance at low submergence better than the standard model, but it slightly underestimated the resistance at higher submergences. Despite differences in flow characteristics, weirs and groynes were found to produce similar flow resistances for the same height and boundary conditions. Simulations of groyne modifications showed that hydraulic resistance decreased nonlinearly with groyne lowering and streamlining.
Plain Language Summary
Groynes are used for river training. Their positive effects include riverbank stabilization, improvement of navigability and prevention of ice jams. However, during floods they become submerged and increase the flood water depths by blocking the flow and increasing turbulence. This may lead to severe outcomes. Floods are among the most fatal disasters that affect the globe. Even an increase of flood water depths by some centimeters may cause disastrous outcomes. Engineers generally resort to approximate solutions for adding the effects of groynes into hydraulic flood models for long river reaches. In this paper, we assess the capabilities of these approximate models as well as those of more simplified and more advanced models. Insights were sought to help flood modelers for better prediction of flood water levels. Our study shows that the most widely used groyne resistance model leaves room for further development, despite demonstrated capabilities.
Key Points
Characterizing the effect of groynes on river flow in two‐dimensional hydrodynamic models by using weir formulas was evaluated
Alternative methods to implement groynes into two‐ and three‐dimensional river models were proposed, and their reliability was tested
The performances of two of the available modeling options in simulating the groyne modifications were assessed
Marine heatwaves have been shown to be increasing in frequency, duration and intensity over the past several decades. Are these changes related to rising mean temperatures, changes to temperature ...variability, or a combination of the two? Here we investigate this question using satellite observations of sea surface temperature (SST) covering 36 years (1982–2017). A statistical climate model is used to simulate SST time series, including realistic variability based on an autoregressive model fit to observations, with specified trends in mean and variance. These simulated SST time series are then used to test whether observed trends in marine heatwave properties can be explained by changes in either mean or variability in SST, or both. We find changes in mean SST to be the dominant driver of the increasing frequency of marine heatwave days over approximately 2/3 of the ocean; while it is the dominant driver of changes in marine heatwave intensity (temperature anomaly) over approximately 1/3 of the ocean. We also find that changes in mean SST explain changes in both MHW properties over a significantly larger proportion of the world’s ocean than changes in SST variance. The implication is that given the high confidence of continued mean warming throughout the twenty-first century due to anthropogenic climate change we can expect the historical trends in marine heatwave properties to continue over the coming decades.
In this paper, we study partial identification and inference in a linear quantile regression, where the dependent variable is subject to possibly unknown dependent censoring characterized by an ...Archimedean copula. An outer set of the identified set for the regression coefficient is characterized via inequality constraints. For one-parameter ordered families of Archimedean copulas, we construct a simple confidence set by inverting an asymptotically pivotal statistic. A bootstrap confidence set is also constructed. Sensitivity of the identified set to possible misspecification of the true copula and the finite sample performance of the boostrap confidence set are investigated numerically.
We propose a likelihood-based Bayesian method that exploits up-to-date sequential Monte Carlo methods to efficiently estimate long-run risk models in which the conditional variance of consumption ...growth follows either an autoregressive (AR) process or an autoregressive gamma (ARG) process. We use the U.S. quarterly consumption and asset returns data from the postwar period to implement estimation. Our findings are: (1) informative priors on the preference parameters can help to improve model performance; (2) expected consumption growth has a very persistent component, whereas consumption volatility is less persistent; (3) while the ARG-based model performs better than the AR-based one statistically, the latter could fit asset returns better; and (4) the solution method matters more for estimation in the AR-based model than in the ARG-based model.
A five-factor asset pricing model Fama, Eugene F.; French, Kenneth R.
Journal of financial economics,
04/2015, Letnik:
116, Številka:
1
Journal Article
Recenzirano
A five-factor model directed at capturing the size, value, profitability, and investment patterns in average stock returns performs better than the three-factor model of Fama and French (FF, 1993). ...The five-factor model׳s main problem is its failure to capture the low average returns on small stocks whose returns behave like those of firms that invest a lot despite low profitability. The model׳s performance is not sensitive to the way its factors are defined. With the addition of profitability and investment factors, the value factor of the FF three-factor model becomes redundant for describing average returns in the sample we examine.
This paper proposes a composite function model to describe the ground reaction curve (GRC) on a trapdoor. The characteristics of the GRC were analysed in detail in combination with the development of ...soil arching and the shear surface. The parameters in the proposed model for two-dimensional (2D) and three-dimensional (3D) cases were determined by suggesting new multi-arch models and the results of existing trapdoor model tests. The multi-arch models consist of three parts: the upper end-bearing arch, the stability zone and the lower friction arch. A formula for predicting the height of the friction arch zone was deduced. The effectiveness of the proposed models was demonstrated by comparing the calculated results using the proposed models with the measured data of laboratory model tests. Results showed that the proposed model can describe the trend of the full GRC accurately and the variations in the minimum and ultimate soil arching ratios and load recovery index with increasing burial depth. Finally, the effects of model parameters on the GRC were analysed.
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