scholarly journals Using GAM functions and Markov-Switching models in an evaluation framework to assess countries’ performance in controlling the COVID-19 pandemic

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Abdinardo M. B. de Oliveira ◽  
Jane M. Binner ◽  
Anandadeep Mandal ◽  
Logan Kelly ◽  
Gabriel J. Power
Keyword(s):  
Time Series ◽  
Regime Switching ◽  
Structural Breaks ◽  
Generalized Additive Models ◽  
Markov Switching ◽  
Additive Models ◽  
Evaluation Framework ◽  
Single Type ◽  
Systems Science ◽  
The World

Abstract Background The COVID-19 pandemic has initiated several initiatives to better understand its behavior, and some projects are monitoring its evolution across countries, which naturally leads to comparisons made by those using the data. However, most “at a glance” comparisons may be misleading because the curve that should explain the evolution of COVID-19 is different across countries, as a result of the underlying geopolitical or socio-economic characteristics. Therefore, this paper contributes to the scientific endeavour by creating a new evaluation framework to help stakeholders adequately monitor and assess the evolution of COVID-19 in countries, considering the occurrence of spikes, "secondary waves" and structural breaks in the time series. Methods Generalized Additive Models were used to model cumulative and daily curves for confirmed cases and deaths. The Root Relative Squared Error and the Percentage Deviance Explained measured how well the models fit the data. A local min-max function was used to identify all local maxima in the fitted values. The pure Markov-Switching and the family of Markov-Switching GARCH models were used to identify structural breaks in the COVID-19 time series. Finally, a quadrants system to identify countries that are more/less efficient in the short/long term in controlling the spread of the virus and the number of deaths was developed. Such methods were applied in the time series of 189 countries, collected from the Centre for Systems Science and Engineering at Johns Hopkins University. Results Our methodology proves more effective in explaining the evolution of COVID-19 than growth functions worldwide, in addition to standardizing the entire estimation process in a single type of function. Besides, it highlights several inflection points and regime-switching moments, as a consequence of people’s diminished commitment to fighting the pandemic. Although Europe is the most developed continent in the world, it is home to most countries with an upward trend and considered inefficient, for confirmed cases and deaths. Conclusions The new outcomes presented in this research will allow key stakeholders to check whether or not public policies and interventions in the fight against COVID-19 are having an effect, easily identifying examples of best practices and promote such policies more widely around the world.

scholarly journals Changes of China’s agri-food exports to Germany caused by its accession to WTO and the 2008 financial crisis

2015 ◽  
Vol 7 (2) ◽  
pp. 262-279 ◽  
Author(s):  
Zhichao Guo ◽  
Yuanhua Feng ◽  
Thomas Gries
Keyword(s):  
Time Series ◽  
Financial Crisis ◽  
Regression Models ◽  
Structural Breaks ◽  
Global Financial Crisis ◽  
Middle Part ◽  
Change Points ◽  
Content Type ◽  
The World ◽  
Before And After

Purpose – The purpose of this paper is to investigate changes of China’s agri-food exports to Germany caused by China’s accession to WTO and the global financial crisis in a quantitative way. The paper aims to detect structural breaks and compare differences before and after the change points. Design/methodology/approach – The structural breaks detection procedures in this paper can be applied to find out two different types of change points, i.e. in the middle and at the end of one time series. Then time series and regression models are used to compare differences of trade relationship before and after the detected change points. The methods can be employed in any economic series and work well in practice. Findings – The results indicate that structural breaks in 2002 and 2009 are caused by China’s accession to WTO and the financial crisis. Time series and regression models show that the development of China’s exports to Germany in agri-food products has different features in different sub-periods. Before 1999, there is no significant relationship between China’s exports to Germany and Germany’s imports from the world. Between 2002 and 2008 the former depends on the latter very strongly, and China’s exports to Germany developed quickly and stably. It decreased, however suddenly in 2009, caused by the great reduction of Germany’s imports from the world in that year. But China’s market share in Germany still had a small gain. Analysis of two categories in agri-food trade also leads to similar conclusions. Comparing the two events we see rather different patterns even if they both indicate structural breaks in the development of China’s agri-food exports to Germany. Originality/value – This paper partly originally proposes two statistical algorithms for detecting different kinds of structural breaks in the middle part and at the end of a short-time series, respectively.

scholarly journals Revisiting Structural Breaks in the Terms of Trade of Primary Commodities (1900–2020)—Markov Switching Models and Finite Mixture Distributions

2021 ◽  
Vol 5 (1) ◽  
pp. 34
Author(s):  
Armand Taranco ◽  
Vincent Geronimi
Keyword(s):  
Time Series ◽  
Regime Switching ◽  
Structural Breaks ◽  
Terms Of Trade ◽  
Finite Mixture ◽  
Mixture Of Distributions ◽  
Data Set ◽  
Markov Switching Models ◽  
Finite Mixture Of Distributions

This paper presents an analysis of the long-term dynamics of the terms of trade of primary commodities (TTPC) using an extended data set for the whole period 1900–2020. Following our original contribution, we implement three approaches of time series—the finite mixture of distributions, the Markov finite mixture of distributions, and the Markov regime-switching model. Our results confirm the hypothesis of the existence of a succession of three different dynamic regimes in the TTPC over the 1900–2020 period. It seems that the uncertainty characterising the long-term dynamic analysis of TTPC is better taken into account with a Markov hypothesis in the transition from one regime to another than without this hypothesis. In addition, this hypothesis improves the quality of the time series segmentation into regimes.

scholarly journals Short-Term and Medium-Term Drought Forecasting Using Generalized Additive Models

2020 ◽  
Vol 12 (10) ◽  
pp. 4006
Author(s):  
Fhumulani Mathivha ◽  
Caston Sigauke ◽  
Hector Chikoore ◽  
John Odiyo
Keyword(s):  
South Africa ◽  
Time Series ◽  
Water Resource Management ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Drought Risk ◽  
Drought Forecasting ◽  
Medium Term ◽  
Drought Conditions ◽  
Semi Arid

Forecasting extreme hydrological events is critical for drought risk and efficient water resource management in semi-arid environments that are prone to natural hazards. This study aimed at forecasting drought conditions in a semi-arid region in north-eastern South Africa. The Standardized Precipitation Evaporation Index (SPEI) was used as a drought-quantifying parameter. Data for SPEI formulation for eight weather stations were obtained from South Africa Weather Services. Forecasting of the SPEI was achieved by using Generalized Additive Models (GAMs) at 1, 6, and 12 month timescales. Time series decomposition was done to reduce time series complexities, and variable selection was done using Lasso. Mild drought conditions were found to be more prevalent in the study area compared to other drought categories. Four models were developed to forecast drought in the Luvuvhu River Catchment (i.e., GAM, Ensemble Empirical Mode Decomposition (EEMD)-GAM, EEMD-Autoregressive Integrated Moving Average (ARIMA)-GAM, and Forecast Quantile Regression Averaging (fQRA)). At the first two timescales, fQRA forecasted the test data better than the other models, while GAMs were best at the 12 month timescale. Root Mean Square Error values of 0.0599, 0.2609, and 0.1809 were shown by fQRA and GAM at the 1, 6, and 12 month timescales, respectively. The study findings demonstrated the strength of GAMs in short- and medium-term drought forecasting.

scholarly journals Moments of Markov-Switching Models

2014 ◽  
Vol 61 (1) ◽  
pp. 131-140
Author(s):  
Anna Petričková
Keyword(s):  
Time Series ◽  
Regime Switching ◽  
Switching Time ◽  
Markov Switching ◽  
Central Moment ◽  
Time Series Models ◽  
Markov Switching Models ◽  
State Dependent ◽  
Switching Models ◽  
Dependent Model

Abstract In this paper we have focused on the class of regime-switching time series models with regimes determined by unobservable variables, concretely Markov-switching models. We have derived 2nd central moment of the MSW models for two cases-state-independent and state-dependent model

scholarly journals Through the Irregular Paths of Inequality: An Analysis of the Evolution of Socioeconomic Inequality in Brazilian States Since 1976

2021 ◽  
Vol 13 (4) ◽  
pp. 2356
Author(s):  
Paulo Mourao ◽  
Alexandre Junqueira
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Gini Coefficient ◽  
Structural Breaks ◽  
Socioeconomic Inequality ◽  
Historical Trends ◽  
Gini Coefficients ◽  
Growth Policy ◽  
The World ◽  
High Degree

Patterns of inequality tend to seriously undermine any attempt at economic growth policy when the inequality is perceived by significant groups of individuals as unjust, inhuman, and insurmountable. One country with a high degree of inequality has been Brazil (usually in the world top-10). Brazil had also witnessed strong dynamics of certain indicators, such as the Gini coefficient, over the last several decades. However, so far, such dynamics have not been properly analyzed, especially considering the significant differences across Brazilian states. For filling that gap, this study used econometric techniques specific to time series and tried to identify structural breaks in the series of Gini coefficients for the 27 Brazilian states since 1976. Results showed a tendency towards an increase in inequality until 1995, followed by a reduction in inequality since 2000. Some cases of Brazilian states were related to the absence of structural breaks, showing a maintenance of historical trends in the evolution of inequality, which raises important policies’ challenges.

scholarly journals Zooplankton abundance trends and patterns in Shelikof Strait, western Gulf of Alaska, USA, 1990–2017

2020 ◽  
Vol 42 (3) ◽  
pp. 334-354 ◽  
Author(s):  
David G Kimmel ◽  
Janet T Duffy-Anderson
Keyword(s):  
Time Series ◽  
Generalized Additive Models ◽  
Zooplankton Community ◽  
Strong Relationship ◽  
Gulf Of Alaska ◽  
Additive Models ◽  
Environmental Data ◽  
Dynamic Factor ◽  
Zooplankton Abundance ◽  
Underlying Trend

Abstract A multivariate approach was used to analyze spring zooplankton abundance in Shelikof Strait, western Gulf of Alaska. abundance of individual zooplankton taxa was related to environmental variables using generalized additive models. The most important variables that correlated with zooplankton abundance were water temperature, salinity and ordinal day (day of year when sample was collected). A long-term increase in abundance was found for the calanoid copepod Calanus pacificus, copepodite stage 5 (C5). A dynamic factor analysis (DFA) indicated one underlying trend in the multivariate environmental data that related to phases of the Pacific Decadal Oscillation. DFA of zooplankton time series also indicated one underlying trend where the positive phase was characterized by increases in the abundance of C. marshallae C5, C. pacificus C5, Eucalanus bungii C4, Pseudocalanus spp. C5 and Limacina helicina and declines in the abundance of Neocalanus cristatus C4 and Neocalanus spp. C4. The environmental and zooplankton DFA trends were not correlated over the length of the entire time period; however, the two time series were correlated post-2004. The strong relationship between environmental conditions, zooplankton abundance and time of sampling suggests that continued warming in the region may lead to changes in zooplankton community composition and timing of life history events during spring.

scholarly journals The effect of climate on the spread of the COVID-19 pandemic: A review of findings, and statistical and modelling techniques

2020 ◽  
Vol 44 (5) ◽  
pp. 591-604 ◽  
Author(s):  
Álvaro Briz-Redón ◽  
Ángel Serrano-Aroca
Keyword(s):  
Linear Models ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Transmission Risk ◽  
Temporal Models ◽  
Wide Range ◽  
The World ◽  
Modelling Techniques ◽  
Spatio Temporal ◽  
Hot Weather

The new SARS-CoV-2 coronavirus has spread rapidly around the world since it was first reported in humans in Wuhan, China, in December 2019 after being contracted from a zoonotic source. This new virus produces the so-called coronavirus 2019 or COVID-19. Although several studies have supported the epidemiological hypothesis that weather patterns may affect the survival and spread of droplet-mediated viral diseases, the most recent have concluded that summer weather may offer partial or no relief of the COVID-19 pandemic to some regions of the world. Some of these studies have considered only meteorological variables, while others have included non-meteorological factors. The statistical and modelling techniques considered in this research line have included correlation analyses, generalized linear models, generalized additive models, differential equations, or spatio-temporal models, among others. In this paper we provide a systematic review of the recent literature on the effects of climate on COVID-19’s global expansion. The review focuses on both the findings and the statistical and modelling techniques used. The disparate findings reported seem to indicate that the estimated impact of hot weather on the transmission risk is not large enough to control the pandemic, although the wide range of statistical and modelling approaches considered may have partly contributed to the inconsistency of the findings. In this regard, we highlight the importance of being aware of the limitations of the different mathematical approaches, the influence of choosing geographical units and the need to analyse COVID-19 data with great caution. The review seems to indicate that governments should remain vigilant and maintain the restrictions in force against the pandemic rather than assume that warm weather and ultraviolet exposure will naturally reduce COVID-19 transmission.

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