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The evolutions of crop productions are influenced by a series of both deterministic and stochastic factors, so the data series of these evolutions are variable with significant random components. Starting from this consideration, the paper comparatively analyzes the evolutions of the average annual wheat production per hectare at the level of Romania and the South-West Oltenia development region, as well as at the level of Dolj county, component of this region. The main objective is to identify and analyze trend components, based on regression models, as well as random components, based on autoregressive and moving average (ARMA) models in order to identify similarities and differences between the evolutions of average wheat production per hectare in the last two decades. The conclusion is that, although the evolution trends are similar, the random components highlight differences between the evolutions registered in Romania and in the South-West Oltenia region, which highlights the significant impact of specific random factors. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to investigate the application of a generalized dynamic factor model (GDFM) based on dynamic principal components analysis to forecasting short-term economic growth in Romania. We have used a generalized principal components approach to estimate a dynamic model based on a dataset comprising 86 economic and non-economic variables that are linked to economic output. The model exploits the dynamic correlations between these variables and uses three common components that account for roughly 72% of the information contained in the original space. We show that it is possible to generate reliable forecasts of quarterly real gross domestic product (GDP) using just the common components while also assessing the contribution of the individual variables to the dynamics of real GDP. In order to assess the relative performance of the GDFM to standard models based on principal components analysis, we have also estimated two Stock-Watson (SW) models that were used to perform the same out-of-sample forecasts as the GDFM. The results indicate significantly better performance of the GDFM compared with the competing SW models, which empirically confirms our expectations that the GDFM produces more accurate forecasts when dealing with large datasets. [ABSTRACT FROM AUTHOR]

In a previous paper, it was made a statistical analysis for hydropower ratio from total energy production in Romania over the period 2006 -2016 for monthly values, and the main characteristics for this set of observations were determined. Based on monthly recorded data and on mathematical model, this paper presents a method for the prediction of this random variable using the second order autoregressive (AR2) model. The reconstructed data are compared with recorded values for the last three years of the analyzed time series and, for the next two years, a forecast was made, whose results are compared with real values published in the annual reports of Hidroelectrica, for 2017, and the Transelectrica Website, for 2018. [ABSTRACT FROM AUTHOR]

The researches were carried out in 2017-2019, in the experimental field from the Experimental Didactic Station of the Banat's University of Agricultural Sciences and Veterinary Medicine of "King Mihai I of Romania" from Timişoara. The experiments were organized on a cambic chernozem soil, wet groundwater (slightly gleyic), slightly decarbonated, on loessoid deposits - dusty, loamy - clay, with pH -6.10 and a humus content of 2.58%.In terms of climate, according to Koppen, the climate falls within the formula c.f.b.x., namely a temperate climate, with rainfall throughout the year, but with a deficit in the summer months. Three biotypes were studied in the research, namely the Bioro variety and two populations. The research included a two-factor experiment with three repetitions. Fertilization was performed with variable doses of nitrogen fertilizers, applied on a constant background of P60K60. On average, on the fertilizer doses, the highest yield of 4523 kg/ha was obtained for the Purple population, followed by the White population with 3871 k ha, both populations exceeding the yield for the Bioro variety, for which the yield was only 3404 kg/Ha. The protein content was close, varying between 24.61% in the Purple population and 25.37% in the White population. In this paper, statistical correlations were made based on the following parameters from the laws of distribution of the random variable X like: mean, dispersion, standard deviation, which we will note by λ. Be it two estimated values λ1 *and λ2 * found from two selection variables. In other words, the dispersion is the square mean of the deviation from the mean. From the definition relations it is observed that the narrower the interval in which the values of xi are concentrated, the smaller the dispersion and vice versa. Dispersion is also called variance. The error we are working with is 0.05 in all cases of comparing the means. [ABSTRACT FROM AUTHOR]

An earthquake of Mw6.4 hit the coastal zone of Albania on 26 November 2019, at 02:54:11 UTC. It was intensively felt at about 34 km away, in Tirana City, where damages and lives lost occurred. To emphasize a pre-seismic geomagnetic signature before the onset of this earthquake, the data collected on the interval 15 October–30 November 2019, at the Panagjurishte (PAG)-Bulgaria and Surlari (SUA)-Romania observatories were analyzed. Further on, for geomagnetic signal identification we used the polarization parameter (BPOL) which is time invariant in non-seismic conditions and it becomes unstable due to the strain effect related to the Mw6.4earthquake. Consequently, BPOL time series and its standard deviations are performed for the both sites using ultra low frequency (ULF)-fast Fourier transform (FFT) band-pass filtering. A statistical analysis, based on a standardized random variable equation, was applied to emphasize on the BPOL* (PAG) and ABS BPOL* (PAG) time series the anomalous signal's singularity and, to differentiate the transient local anomalies due to the Mw6.4 earthquake, from the internal and external parts of the geomagnetic field, taken PAG observatory as reference. Finally, the ABS BPOL* (PAG-SUA) time series were obtained on the interval 1–30 November 2019, where a geomagnetic signature greater than 2.0, was detected on 23 November and the lead time was 3 days before the onset of the Mw6.4earthquake. [ABSTRACT FROM AUTHOR]

In this paper, we used the geomagnetic data, collected in real time on the intervals (August–September and November-December, 2016) by the electromagnetic monitoring system placed at the Geomagnetic Observatory Provita de Sus (GOPS), to emphasize possible relationships between the anomalous behavior of the normalized function Bzn and the earthquakes of M5.7 and M5.6, both being generated in the seismic active Vrancea zone on September 24 and December 28, 2016. It has already been demonstrated that for a 2D geoelectric structure, in pre-seismic conditions, the normalized function Bzn has significant variations, due to the resistivity changes, possibly associated with the earthquake-induced rupture-processes, followed by dehydration of the rocks and high-pressure fluid flow through the existing lithospheric faulting systems expanded between the Vrancea zone and GOPS. Consequently, the daily mean distributions of the Bzn = Bz/Bperp (where Bz is vertical component of the geomagnetic field; Bperp is geomagnetic component perpendicular to the geoelectric strike) and its standard deviation (SD) are carried out for the both above time-intervals, in the ULF frequency range 0.001Hz to 0.0083Hz, by using the FFT band-pass filtering. Further on, to investigate the singularities of the pre-seismic geomagnetic signals related to the M5.7 and M5.6 earthquakes, we applied a statistical analysis based on a standardized random variable equation, and the results are presented as Bz* time series, performed on the new intervals 1-30 September and 1-31December, 2016. Finally, two pre-seismic anomalous signals are observed: (i) first one, with values greater than 5 SD was identified on September 22, 2016, what means a lead time of 2 days before the M5.7earthquake; (ii) second one, with values greater than 4SD, identified on December 21, 2016, with a lead time of 7days prior to M5.6earthquake. In conclusion, as the work-station has specific programs for geomagnetic data processing, analysis and real time (daily) Bzn and Bzn* display on the institute website, this may be used as an early warning system able to provide useful information for the resilience improvement against the Vrancea intermediate depth earthquakes. [ABSTRACT FROM AUTHOR]