Benefits of smoothing backgrounds and radar reflectivity observations for multiscale data assimilation with an ensemble Kalman filter at convective scales: A proof of concept study

In the ensemble Kalman filter (EnKF), the covariance localization radius is usually small when assimilating radar observations because of high density of the radar observations. This makes the region away from precipitation difficult to correct if no other observations are available, as there is no reason to correct the background. To correct errors away from the innovating radar observations, a multiscale localization (MLoc) method adapted to dense observations like those from radar is proposed. In this method, different scales are corrected successively by using the same reflectivity observations, but with different degree of smoothing and localization radius at each step. In the context of observing system simulation experiments, single and multiple assimilation experiments are conducted with the MLoc method. Results show that the MLoc assimilation updates areas that are away from the innovative observations and improves on average the analysis and forecast quality in single cycle and cycling assimilation experiments. The forecast gains are maintained until the end of the forecast period, illustrating the benefits of correcting different scales.

The Determinants of Non-Performing Loans in the Polish Banking Sector – the Household Loans Portfolio

The study aims to identify changes in non-performing household loans (NPLs) and their main determinants in the Polish banking sector for the period 2009-2021. Specifically, we look at the main determinants of creditworthiness of households which determine the possibility of repayment of principal installments and interest within the prescribed period. The results of the VECM model confirm the considerable significance of GDP per capita, gross salaries and lending rates to NPL loans of households. The results of the response function show a positive impact of GDP per capita and lending rates on NPLs and a negative impact of real salaries on NPLs. The decomposition of variance in the forecast period confirms an increased level of explanation of NPL by GDP per capita, gross salaries, and the lending rates.

Estimating Vat Refunds the Case of South Africa

This study intends to estimate VAT refund levels in South Africa in an ideal situation where there are well-equipped, incorruptible officials and a proper VAT system is in place. Understanding the dynamics behind the behaviour of VAT and its main drivers is crucial and could have a huge benefit to the country’s economy with regards to closing the tax gap related to this tax type. Using the data from various sources (VAT refunds and some macroeconomic variables), a Vector Autoregression (VAR) model was used to estimate the level of VAT refunds in South Africa. The model estimates VAT refunds for the period 2021/22 to be R242.7 billion, while the VAT refunds forecast for the period 2022/23 and 2023/24 amounts to R254.6 billion and R267.3 billion, respectively. Furthermore, VAT refunds contribute on average 17.5% to the total tax for the forecast period of 2021/22-2023/24. The study also indicates that the growth in VAT refunds is influenced by the growth in domestic VAT collections, increasing employment rate and the growth in both agriculture and construction GDP. The estimated level of VAT refunds can serve as an important consideration in the national budgeting processes in South Africa. Adequate provisions can be made to enable proper planning and distributions to government departments. To our knowledge, this study is the first of its kind for South Africa. In summary, the South African tax authority should not deviate from the primary goal of building sound VAT systems based on improved voluntary compliance through effective systems of self-assessment

Did OPEC change its behaviour after the November 2014 meeting?

We analyse the behaviour of OPEC as a group by formulating a theoretical model that encompasses the perfect competition model and various forms of the imperfect competition model. By confronting the theoretical model with quarterly data for the period from 1992 to 2013 within the context of a cointegrated vector autoregressive (CVAR) model, we find support for the imperfect competition hypothesis regarding the output decisions of OPEC. We also find that a dynamic equilibrium correction model with imperfect competition is stable in-sample. However, a forecasting exercise for the period from 2014 to 2018 reveals that the model breaks down following the November 2014 meeting at which OPEC decided to keep its supply unchanged despite the huge oil price drop in advance. The model systematically underpredicts OPEC production over the forecast period and by as much as 2.5 million barrels per day at the end of 2016. During 2018, however, the model forecasts OPEC production quite well. Our findings suggest that the behaviour of OPEC did indeed change significantly after the November 2014 meeting.

Trend Analysis in Sugarcane Growth in Mumias Sugar Belt, Western Kenya; for the Period 1985-2015

The study was carried out to examine trends in the output and acreage in the Mumias Sugar belt from the period 1985-2015. We used secondary data collected from Mumais Sugar Company records for the period 1985-2015 for the study. The trend analysis of sugarcane production in the Mumias Sugar Belt is important, where sugarcane is the major cash crop and absorbs a majority of the agrarian population in the region. The study used the expert modeler, an autoregressive integrated moving average (ARIMA), to predict the output. The forecast period was 2016 through March 2021 and employed two scenarios: I) forecast with +2 harvesting age predictor modification and ii) forecast with +10 hectares predictor modification. The predicted value showed good agreement with the observed values from the series plot, indicating that the model has a good predictive ability. The application of the model revealed that the results in the prediction tables show that, in each of the six forecasted quarters, increasing the harvesting age by two months is expected to generate about 4.52 more tons of yields per hectare than increasing area harvested by 10 hectares that would decrease the yield by 0.01 tons per hectare. The study recommends research and development on sugarcane varieties that mature early, making sugarcane-based Agri- enterprises and sustainable. In addition, Mumias Sugar Company should seek profitable techniques to increase the recovery per cent, and farmers seek good management practices to increase the efficiency of the sugarcane farms in the sugar belt.

Selecting a Model for Forecasting

We investigate forecasting in models that condition on variables for which future values are unknown. We consider the role of the significance level because it guides the binary decisions whether to include or exclude variables. The analysis is extended by allowing for a structural break, either in the first forecast period or just before. Theoretical results are derived for a three-variable static model, but generalized to include dynamics and many more variables in the simulation experiment. The results show that the trade-off for selecting variables in forecasting models in a stationary world, namely that variables should be retained if their noncentralities exceed unity, still applies in settings with structural breaks. This provides support for model selection at looser than conventional settings, albeit with many additional features explaining the forecast performance, and with the caveat that retaining irrelevant variables that are subject to location shifts can worsen forecast performance.

Future Trends in Cassava Production: Indicators and its Implications for Food Supply in Nigeria

The growing demand for cassava and its products has continued to stretch the supply of cassava globally. Nigeria is a leading producer of cassava in the world yet, there are concerns that if appropriate policy strategies are not adopted to increase production, the current fragile situation of food insecurity in Nigeria may be worsened. Besides the increasing number of gigantic cassava-based industries spring up in Nigeria, the rapidly growing population of consumers is another factor that may further disrupt the relatively stable cassava market in Nigeria in the future. Therefore, “ceteris paribus”, the study determined the appropriate quantitative models to forecast the trends in cassava production indictors in Nigeria. Using the historical series (1961 – 2018), 12-year period (2019 -2030) forecasts were made for each of the production indicators as follows: 106 million tonnes (production output), 7.7 tonnes/ha (yield) and 9.6 million hectares (cropped area) in 2030. The study extrapolated the expected food supply from the expected production output in the forecast period using the 2014 FAO estimates of food supply per caput. Thus, in 2030, cassava food supply per caput was found to decline from 267 Kcal/capita/day in 2014 to 239 Kcal/capita/day. The study concludes that despite keeping the future demand of the growing cassava-based industries constant, cassava production is expected to continually increase but future food supply per caput would decline. However, the growing cassava-based industries globally is expected to hugely influence the future cassava market dynamics.

Geomagnetic secular variation forecast using the NASA GEMS ensemble Kalman filter: A candidate SV model for IGRF-13

We have produced a 5-year mean secular variation (SV) of the geomagnetic field for the period 2020–2025. We use the NASA Geomagnetic Ensemble Modeling System (GEMS), which consists of the NASA Goddard geodynamo model and ensemble Kalman filter (EnKF) with 400 ensemble members. Geomagnetic field models are used as observations for the assimilation, including gufm1 (1590–1960), CM4 (1961–2000) and CM6 (2001–2019). The forecast involves a bias correction scheme that assumes that the model bias changes on timescales much longer than the forecast period, so that they can be removed by successive forecast series. The algorithm was validated on the time period 2010-2015 by comparing with CM6 before being applied to the 2020–2025 time period. This forecast has been submitted as a candidate predictive model of IGRF-13 for the period 2020–2025. Graphical abstract

Forecasting the COVID-19 Diffusion in Italy and the Related Occupancy of Intensive Care Units

This paper provides a model-based method for the forecast of the total number of currently COVID-19 positive individuals and of the occupancy of the available intensive care units in Italy. The predictions obtained—for a time horizon of 10 days starting from March 29th—will be provided at a national as well as at a more disaggregated level, following a criterion based on the magnitude of the phenomenon. While those regions hit the most by the pandemic have been kept separated, those less affected regions have been aggregated into homogeneous macroareas. Results show that—within the forecast period considered (March 29th–April 7th)—all of the Italian regions will show a decreasing number of COVID-19 positive people. The same will be observed for the number of people who will need to be hospitalized in an intensive care unit. These estimates are valid under constancy of the government’s current containment policies. In this scenario, northern regions will remain the most affected ones, whereas no significant outbreaks are foreseen in the southern regions.