Estimation of dynamic latent variable models using simulated nonparametric moments

Given a model that can be simulated, conditional moments at a trial parameter value can be calculated with high accuracy by applying kernel smoothing methods to a long simulation. With such conditional moments in hand, standard method of moments techniques can be used to estimate the parameter. Since conditional moments are calculated using kernel smoothing rather than simple averaging, it is not necessary that the model be simulable subject to the conditioning information that is used to define the moment conditions. For this reason, the proposed estimator is applicable to general dynamic latent variable models. Monte Carlo results show that the estimator performs well in comparison to other estimators that have been proposed for estimation of general DLV models.

Estimation of dynamic latent variable models using simulated nonparametric moments

Abstract. Given a model that can be simulated, conditional moments at a trial parameter value can be calculated with high accuracy by applying kernel smoothing methods to a long simulation. With such conditional moments in hand, standard method of moments techniques can be used to estimate the parameter. Because conditional moments are calculated using kernel smoothing rather than simple averaging, it is not necessary that the model be simulable subject to the conditioning information that is used to define the moment conditions. For this reason, the proposed estimator is applicable to general dynamic latent variable models. It is shown that as the number of simulations diverges, the estimator is consistent and a higher-order expansion reveals the stochastic difference between the infeasible GMM estimator based on the same moment conditions and the simulated version. In particular, we show how to adjust standard errors to account for the simulations. Monte Carlo results show how the estimator may be applied to a range of dynamic latent variable (DLV) models, and that it performs well in comparison to several other estimators that have been proposed for DLV models.

A one-dimensional Keller-Segel equation with a drift issued from the boundary

We investigate in this note the dynamics of a one-dimensional Keller-Segel type model on the half-line. On the contrary to the classical configuration, the chemical production term is located on the boundary. We prove, under suitable assumptions, the following dichotomy which is reminiscent of the two-dimensional Keller-Segel system. Solutions are global if the mass is below the critical mass, they blow-up in finite time above the critical mass, and they converge to some equilibrium at the critical mass. Entropy techniques are presented which aim at providing quantitative convergence results for the subcritical case. This note is completed with a brief introduction to a more realistic model (still one-dimensional).

Solving Large Location-Allocation problems by Clustering and Simulated Annealing

Globalization involves several facility location problems that need to be handled at large scale. Location Allocation (LA) is a combinatorial problem in which the distance among points in the data space matter. Precisely, taking advantage of the distance property of the domain we exploit the capability of clustering techniques to partition the data space in order to convert an initial large LA problem into several simpler LA problems. Particularly, our motivation problem involves a huge geographical area that can be partitioned under overall conditions. We present different types of clustering techniques and then we perform a cluster analysis over our dataset in order to partition it. After that, we solve the LA problem applying simulated annealing algorithm to the clustered and non-clustered data in order to work out how profitable is the clustering and which of the presented methods is the most suitable

Self-serving biases: Evidence from a simulated labor relationship

Previous studies have found evidence of a self-serving bias in bargaining and dispute resolution. We use experimental data to test for this effect in a simulated labor relatonship. We finda consistent discrepancy between employer beliefs and employee actions that can only be attributed to self-serving biases. This discrepancy is evident through stated beliefs, revealed satisfaction, and actual actions. We present evidenceand discuss implications.

Attribution and reciprocity in a simulated labor market: An experimental investigation

While papers such as Akerlof and Yellen (1990) and Rabin (1993) argue that psychological considerations such as fairness and reciprocity are important in individual decision-making, there is little explicit empirical evidence of reciprocal altruism in economic environments. This paper tests whether attribution of volition in choosing a wage has a significant effect on subsequent costly effort provision. An experiment was conducted in which subjects are first randomly divided into groups of employers and employees. Wages were selected and employees asked to choose an effort level, where increased effort is costly to the employee, but highly beneficial to the employer. The wage-determination process was common knowledge and wages were chosen either by the employer or by an external process. There is evidence for both distributional concerns and reciprocal altruism. The slope of the effort/wage profile is clearly positive in all cases, but is significantly higher when wages are chosen by the employer, offering support for the hypothesis of reciprocity. There are implications for models of utility and a critique of some current models is presented.

The drift of public spending towards the elderly: A generational analysis of the trend of public policies in Spain

The tendency for public welfare spending to be increasingly aimed at the elderly has been pointed out for the US and other developed countries. While population ageing is a common trend, it is not obvious why the shift in spending exceeds the trend in ageing, or why per capita spending on the elderly increases.We show that this is the case in Spain, identify the losers from this development, discuss the policies that underlie this trend, and propose adjustments based on Musgrave s fixed proportions rule as an inter-generationally fair distribution.

Experiencing simulated outcomes

Whereas much literature has documented difficulties in making probabilistic inferences, it hasalso emphasized the importance of task characteristics in determining judgmental accuracy.Noting that people exhibit remarkable efficiency in encoding frequency information sequentially,we construct tasks that exploit this ability by requiring people to experience the outcomes ofsequentially simulated data. We report two experiments. The first involved seven well-knownprobabilistic inference tasks. Participants differed in statistical sophistication and answered withand without experience obtained through sequentially simulated outcomes in a design thatpermitted both between- and within-subject analyses. The second experiment involvedinterpreting the outcomes of a regression analysis when making inferences for investmentdecisions. In both experiments, even the statistically naïve make accurate probabilistic inferencesafter experiencing sequentially simulated outcomes and many prefer this presentation format. Weconclude by discussing theoretical and practical implications.

Surface topography and mixed-pixel effects on the simulated L-band brightness temperatures.

The impact of topography and mixed pixels on L-band radiometric observations over land needs to be quantified to improve the accuracy of soil moisture retrievals. For this purpose, a series of simulations has been performed with an improved version of the soil moisture and ocean salinity (SMOS) end-to-end performance simulator (SEPS). The brightness temperature generator of SEPS has been modified to include a 100-m-resolution land cover map and a 30-m-resolution digital elevation map of Catalonia (northeast of Spain). This high-resolution generator allows the assessment of the errors in soil moisture retrieval algorithms due to limited spatial resolution and provides a basis for the development of pixel disaggregation techniques. Variation of the local incidence angle, shadowing, and atmospheric effects (up- and downwelling radiation) due to surface topography has been analyzed. Results are compared to brightness temperatures that are computed under the assumption of an ellipsoidal Earth.

Extremal climatic states simulated by a 2-dimensional model Part I: Sensitivity of the model and present state

The criterion, based on the thermodynamics theory, that the climatic system tends to extremizesome function has suggested several studies. In particular, special attention has been devoted to the possibility that the climate reaches an extremal rate of planetary entropy production.Due to both radiative and material effects contribute to total planetary entropy production,climatic simulations obtained at the extremal rates of total, radiative or material entropy production appear to be of interest in order to elucidate which of the three extremal assumptions behaves more similar to current data. In the present paper, these results have been obtainedby applying a 2-dimensional (2-Dim) horizontal energy balance box-model, with a few independent variables (surface temperature, cloud-cover and material heat fluxes). In addition, climatic simulations for current conditions by assuming a fixed cloud-cover have been obtained. Finally,sensitivity analyses for both variable and fixed cloud models have been carried out

Extremal climatic states simulated by a 2-dimensional model Part II: Different climatic scenarios

Different climatic simulations have been obtained by using a 2-Dim horizontal energy balancemodel (EBM), which has been constrained to satisfy several extremal principles on dissipationand convection. Moreover, 2 different versions of the model with fixed and variable cloud-coverhave been used. The assumption of an extremal type of behaviour for the climatic system canacquire additional support depending on the similarities found with measured data for pastconditions as well as with usual projections for possible future scenarios

On-line event detection by recursive dynamic principal component analysis and gas sensor arrays under drift conditions

Leakage detection is an important issue in many chemical sensing applications. Leakage detection hy thresholds suffers from important drawbacks when sensors have serious drifts or they are affected by cross-sensitivities. Here we present an adaptive method based in a Dynamic Principal Component Analysis that models the relationships between the sensors in the may. In normal conditions a certain variance distribution characterizes sensor signals. However, in the presence of a new source of variance the PCA decomposition changes drastically. In order to prevent the influence of sensor drifts the model is adaptive and it is calculated in a recursive manner with minimum computational effort. The behavior of this technique is studied with synthetic signals and with real signals arising by oil vapor leakages in an air compressor. Results clearly demonstrate the efficiency of the proposed method.

Drift compensation of gas sensor array data by Orthogonal Signal Correction

Drift is an important issue that impairs the reliability of gas sensing systems. Sensor aging, memory effects and environmental disturbances produce shifts in sensor responses that make initial statistical models for gas or odor recognition useless after a relatively short period (typically few weeks). Frequent recalibrations are needed to preserve system accuracy. However, when recalibrations involve numerous samples they become expensive and laborious. An interesting and lower cost alternative is drift counteraction by signal processing techniques. Orthogonal Signal Correction (OSC) is proposed for drift compensation in chemical sensor arrays. The performance of OSC is also compared with Component Correction (CC). A simple classification algorithm has been employed for assessing the performance of the algorithms on a dataset composed by measurements of three analytes using an array of seventeen conductive polymer gas sensors over a ten month period.

Drift compensation of gas sensor array data by common principal component analysis

A new drift compensation method based on Common Principal Component Analysis (CPCA) is proposed. The drift variance in data is found as the principal components computed by CPCA. This method finds components that are common for all gasses in feature space. The method is compared in classification task with respect to the other approaches published where the drift direction is estimated through a Principal Component Analysis (PCA) of a reference gas. The proposed new method ¿ employing no specific reference gas, but information from all gases ¿has shown the same performance as the traditional approach with the best-fitted reference gas. Results are shown with data lasting 7-months including three gases at different concentrations for an array of 17 polymeric sensors.

Morphological changes in convection-diffusion-limited deposition

The effect of hydrodynamic flow upon diffusion-limited deposition on a line is investigated using a Monte Carlo model. The growth process is governed by the convection and diffusion field. The convective diffusion field is simulated by the biased-random walker resulting from a superimposed drift that represents the convective flow. The development of distinct morphologies is found with varying direction and strength of drift. By introducing a horizontal drift parallel to the deposition plate, the diffusion-limited deposit changes into a single needle inclined to the plate. The width of the needle decreases with increasing strength of drift. The angle between the needle and the plate is about 45° at high flow rate. In the presence of an inclined drift to the plate, the convection-diffusion-limited deposit leads to the formation of a characteristic columnar morphology. In the limiting case where the convection dominates, the deposition process is equivalent to ballistic deposition onto an inclined surface.