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This paper aims to both fit and predict crop biophysical variables with a SAR image series by performing a factorial experiment and estimating time series models using a combination of forecasts. Two plots of barley grown under rainfed conditions in Spain were monitored during the growing cycle of 2015 (February to June). The dataset included nine field estimations of agronomic parameters, 20 RADARSAT-2 images, and daily weather records. Ten polarimetric observables were retrieved and integrated to derive the six agronomic and monitoring variables, including the height, biomass, fraction of vegetation cover, leaf area index, water content, and soil moisture. The statistical methods applied, namely double smoothing, ARIMAX, and robust regression, allowed the adjustment and modelling of these field variables. The model equations showed a positive contribution of meteorological variables and a strong temporal component in the crop’s development, as occurs in natural conditions. After combining different models, the results showed the best efficiency in terms of forecasting and the influence of several weather variables. The existence of a cointegration relationship between the data series of the same crop in different fields allows for adjusting and predicting the results in other fields with similar crops without re-modelling.

Currently, traffic intensity in large cities and their surroundings constitute the main unsustainability factor associated with urban transport, leading to significant traffic speed reduction due to high levels of congestion. Road pricing seems to be a measure of transport policy capable of improving efficiency and sustainability in urban transport, reducing traffic intensity and increasing traffic speed, as reflected in the main road pricing indicators currently in operation (Singapore, London, Stockholm, Milan…). Based on the data obtained through a mobility survey applied to a theoretical design of road pricing for the city of Madrid, we developed a traffic speed forecast model using time series analysis, to which we applied the mobility survey results. The research results show that theoretical urban road pricing could imply very significant positive effects in traffic speed increase and congestion reduction, fundamentally in the city center and metropolitan crown, as well as demonstrating positive effects in the improvement of traffic speed in those municipalities furthest from the urban center. Moreover, our findings reveal that road pricing would allow an average traffic speed increase in the protected area of the city center during the operating hours of between 10% and 32.5%: 15.9% in the metropolitan crown, 10% in M-30, and 32.5% in the case of Madrid’s city center.

Vegetation dynamics is very sensitive to environmental changes, particularly in arid zones where climate change is more prominent. Therefore, it is very important to investigate the response of this dynamics to those changes and understand its evolution according to different climatic factors. Remote sensing techniques provide an effective system to monitor vegetation dynamics on multiple scales using vegetation indices (VI), calculated from remote sensing reflectance measurements in the visible and infrared regions of the electromagnetic spectrum. In this study, we use the normalized difference vegetation index (NDVI), provided from the MOD13Q1 V006 at 250 m spatial resolution product derived from the MODIS sensor. NDVI is frequent in studies related to vegetation mapping, crop state indicator, biomass estimator, drought monitoring and evapotranspiration. In this paper, we use a combination of forecasts to perform time series models and predict NDVI time series derived from optical remote sensing data. The proposed ensemble is constructed using forecasting models based on time series analysis, such as Double Exponential Smoothing and autoregressive integrated moving average with explanatory variables for a better prediction performance. The method is validated using different maize plots and one olive plot. The results after combining different models show the positive influence of several weather measures, namely, temperature, precipitation, humidity and radiation.

The present paper focuses on the analysis of large data sets from public transport networks, more specifically, on how to predict urban bus passenger demand. A series of steps are proposed to ease the understanding of passenger demand. First, given the large number of stops in the bus network, these are divided into clusters and then different models are fitted for a representative of each of the clusters. The aim is to compare and combine the predictions associated with traditional methods, such as exponential smoothing or ARIMA, with machine learning methods, such as support vector machines or artificial neural networks. Moreover, support vector machine predictions are improved by incorporating explanatory variables with temporal structure and moving averages. Finally, through cointegration techniques, the results obtained for the representative of each group are extrapolated to the rest of the series within the same cluster. A case study in the city of Salamanca (Spain) is presented to illustrate the problem.

Since the late 1970s, remote sensing techniques have been proven to be suitable for characterizing and monitoring plants and crops. In particular, synthetic aperture radar (SAR) missions contribute considerably to this prediction effort. However, the main issue when using SAR image series together with field observations is the scarcity of data due to the difficulty of acquiring field measurements. This research aimed to contribute to solving this problem with an alternative statistical model that can overcome the lack of a long, robust series of field-based ground truth observations. The main novelty of this research is the evaluation of the potential of a panel data approach to radar remote sensing imagery for predicting crop biophysical variables. For this purpose, RADARSAT-2 imagery was acquired over the study area in central Spain. Simultaneously, a field campaign was deployed to estimate crop parameters in the same area and to validate the results of the modelling. The analysis of the influence of the crop type on the incidence angle and the polarimetric parameters showed a strong influence of the co-polar channels (HH, VV), the entropy (H) and the coherence between the co-polar channels (γHHVV), with the differences being higher at 25°. The panel data analysis method demonstrated that good predictions, with R2 greater than 0.78, were achieved for all biophysical variables analysed in this study. Overall, this novel statistical approach with remote sensing data showed great applicability for the prediction of crop variables, even with a short series of observations.

This article introduces a new Cramer-Von Misses (CVM) cointegration test robust to nonlinearities. We characterize nonlinear cointegration in terms of a nonlinear moving-average filter (high pass f...

Reducing the air pollution from increases in traffic congestion in large cities and their surroundings is an important problem that requires changes in travel behavior. Road pricing is an effective tool for reducing air pollution, as reflected currently urban road pricing outcomes (Singapore, London, Stockholm and Milan). A survey was conducted based on establishing a hypothetical urban road pricing system in Madrid (a random sample size n = 1298). We developed a forecast air pollution model with time series analysis to evaluate the consequences of possible air pollution decreases in Madrid. Results reveal that the hypothetical road pricing for Madrid could have highly significant effects on decreasing air pollution outside of the city and in the inner city during the peak operating time periods of maximum congestion (morning peak hours from 7:00 to 10:00 and evening peak hours from 18:00 to 20:00). Furthermore, this system could have significant positive effects on a shift toward using public transport and non-motorized modes inside the hypothetical toll zone. This reveals that the system has a high capacity to motivate a decrease in air pollution and impose more sustainable behavior for public transport users.

We extend the Range Unit Root test in two directions. First, we consider the process with time trend and prove that the modified standardized number of new records converges to a sum of two Rayleigh distributions. Second, more general structures of autocorrelated disturbances are also taken into account. Monte Carlo experiments show the good sample properties of this nonparametric unit root test.

This article investigates the relative small sample performance of some robust unit root tests by means of a simulation study. We propose to investigate the stability of unit root test under various kinds of contamination models, and then the article proposes two new statistics that perform better than the celebrated Dickey-Fuller statistic. We illustrate the performances of the tests and their discrepancies with the Dickey-Fuller unit root test through an empirical example based on the US/Finland real exchange rate series.

This article compares the statistical properties of the records from independent and identically distributed time series with those of time series containing a single unit root. It is shown that there are important differences in both the limiting distributions and the convergence rates of the associated record counting processes. Since the record properties of independent and identically distributed time series are shared by a large class of stationary time series, the reported differences underline the possibility of using record-based statistics for robust testing procedures of the unit root hypothesis. We make an extension of the nonparametric test for the Range Unit Root test (RUR) proposed in Aparicio et al. (2006). We prove some properties for the test statistic in the context of the renewal theory and we suggest two new candidates to test the hypothesis of random walk with positive and negative drift.

A sieve bootstrap procedure for constructing interpolation intervals for a general class of linear processes is proposed. This sieve bootstrap provides consistent estimators of the conditional distribution of the missing values, given the observed data. A Monte Carlo experiment is used to show the finite sample properties of the sieve bootstrap and finally, the performance of the proposed method is illustrated with a real data example.

In this paper we explore the usefulness of induced-order statistics in the characterization of integrated series and of cointegration relationships. We propose a non-parametric test statistic for testing the null hypothesis of two independent random walks against wide cointegrating alternatives including monotonic nonlinearities and certain types of level shifts in the cointegration relationship. We call our testing device the induced-order Kolmogorov?Smirnov cointegration test (KS), since it is constructed from the induced-order statistics of the series, and we derive its limiting distribution. This non-parametric statistic endows the test with a number of desirable properties: invariance to monotonic transformations of the series, and robustness for the presence of important parameter shifts. By Monte Carlo simulations we analyze the small sample properties of this test. Our simulation results show the robustness of the induced order cointegration test against departures from linear and constant parameter models. Publicado

In this article we propose a record counting cointegration (RCC) test that is robust to nonlinearities and certain types of structural breaks. The RCC test is based on the synchronicity property of the jumps (new records) of cointegrated series, counting the number of jumps that simultaneously occur in both series. We obtain the rate of convergence of the RCC statistics under the null and alternative hypothesis. Since the asymptotic distribution of RCC under the null hypothesis of a unit root depends on the short-run dependence of the cointegrated series, we propose a small sample correction and show by Monte Carlo simulation techniques their excellent small sample behaviour. Finally, we apply our new cointegration test statistic to several financial and macroeconomic time series that have certain structural breaks and nonlinearities. Publicado

ResumenUn transporte público urbano suficientemente accesible constituye uno de los principales condicionantes para la implantación de peajes urbanos, lo que permite garantizar, además de un adecuado nivel de equidad social en la movilidad, un elevado grado de aceptación social. El presente artículo sobre la base de una encuesta poblacional para la ciudad de Madrid y su entorno ofrece una valoración sobre la actual accesibilidad que presenta el transporte madrileño como garantía de equidad social en la movilidad bajo una situación de peaje urbano, donde los resultados muestran que el sistema permitiría garantizar un nivel de equidad en la movilidad muy significativo y relevante para los desplazamientos interiores y radiales respectivamente, con alcance muy reducido para los desplazamientos transversales, si bien el empleo de determinadas estrategias puede constituir un factor crucial que favorezca la equidad social deseable para el peaje urbano.AbstractA sufficiently accessible public transport, is one of the main factors for the implementation of congestion charging, which ensures addition, an adequate level of social equity in mobility, a high degree of social acceptance. This article based on a population survey for the City of Madrid and its surroundings, provides an assessment of the current transport accessibility features Madrid to guarantee social equity in low mobility urban pricing situations, where the results show as the system would ensure a level of fairness in the mobility very meaningful and relevant to interior and radial displacements respectively with very limited scope for transverse displacements, although the use of certain strategies can be a crucial factor that promotes fairness socially desirable for the congestion charge.