Mapping Forest Fuels through Vegetation Phenology: The Role of Coarse-Resolution Satellite Time-Series

In this paper, we test whether the short-run econometric conditions for the basic assumptions of the Ohlson valuation model hold, and then we relate these results with the fulfillment of the short-run econometric conditions for this model to be effective. Better future modeling motivated us to analyze to what extent the assumptions involved in this seminal model are not good enough approximations to solve the firm valuation problem, causing poor model performance. The model is based on the well-known dividend discount model and the residual income valuation model, and it adds a linear information model, which is a time series model by nature. Therefore, we adopt the time series approach. In the presence of non-stationary variables, we focus our research on US-listed firms for which more than forty years of data with the required cointegration properties to use error correction models are available. The results show that the clean surplus relation assumption has no impact on model performance, while the unbiased accounting property assumption has an important effect on it. The results also emphasize the uselessness of forcing valuation models to match the value displacement property of dividends.

Download Full-text

Calibrating the CreditRisk+ Model at Different Time Scales and in Presence of Temporal Autocorrelation †

Mathematics ◽

10.3390/math9141679 ◽

2021 ◽

Vol 9 (14) ◽

pp. 1679

Author(s):

Jacopo Giacomelli ◽

Luca Passalacqua

Keyword(s):

Time Series ◽

Default Risk ◽

Real Life ◽

Statistical Error ◽

Real Data ◽

Sampling Period ◽

Calibration Procedure ◽

Industry Standards ◽

Default Rate

The CreditRisk+ model is one of the industry standards for the valuation of default risk in credit loans portfolios. The calibration of CreditRisk+ requires, inter alia, the specification of the parameters describing the structure of dependence among default events. This work addresses the calibration of these parameters. In particular, we study the dependence of the calibration procedure on the sampling period of the default rate time series, that might be different from the time horizon onto which the model is used for forecasting, as it is often the case in real life applications. The case of autocorrelated time series and the role of the statistical error as a function of the time series period are also discussed. The findings of the proposed calibration technique are illustrated with the support of an application to real data.

Download Full-text

Seasonality in extra-pulmonary tuberculosis notifications in Germany 2004-2014- a time series analysis

BMC Public Health ◽

10.1186/s12889-021-10655-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Tanja Charles ◽

Matthias Eckardt ◽

Basel Karo ◽

Walter Haas ◽

Stefan Kröger

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Disease Onset ◽

Age Groups ◽

Age Group ◽

Pulmonary Tb ◽

Series Analysis ◽

Disease Site ◽

Extra Pulmonary

Abstract Background Seasonality in tuberculosis (TB) has been found in different parts of the world, showing a peak in spring/summer and a trough in autumn/winter. The evidence is less clear which factors drive seasonality. It was our aim to identify and evaluate seasonality in the notifications of TB in Germany, additionally investigating the possible variance of seasonality by disease site, sex and age group. Methods We conducted an integer-valued time series analysis using national surveillance data. We analysed the reported monthly numbers of started treatments between 2004 and 2014 for all notified TB cases and stratified by disease site, sex and age group. Results We detected seasonality in the extra-pulmonary TB cases (N = 11,219), with peaks in late spring/summer and troughs in fall/winter. For all TB notifications together (N = 51,090) and for pulmonary TB only (N = 39,714) we did not find a distinct seasonality. Additional stratified analyses did not reveal any clear differences between age groups, the sexes, or between active and passive case finding. Conclusion We found seasonality in extra-pulmonary TB only, indicating that seasonality of disease onset might be specific to the disease site. This could point towards differences in disease progression between the different clinical disease manifestations. Sex appears not to be an important driver of seasonality, whereas the role of age remains unclear as this could not be sufficiently investigated.

Download Full-text

Role of Foreign Banks Entry in Promoting Financial Inclusion - "A Time series analysis of Five Permanent Members of UN Security Council"

SSRN Electronic Journal ◽

10.2139/ssrn.3850307 ◽

2021 ◽

Author(s):

Amjad Ali Memon ◽

Niaz Ghumro ◽

Suresh Kumar Oad Rajput ◽

Ahsan Ali Memon

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Security Council ◽

Financial Inclusion ◽

Foreign Banks ◽

Series Analysis ◽

Un Security Council

Download Full-text

TIME SERIES MODELLING OF EPIDEMICS: LEADING INDICATORS, CONTROL GROUPS AND POLICY ASSESSMENT

National Institute Economic Review ◽

10.1017/nie.2021.21 ◽

2021 ◽

Vol 257 ◽

pp. 83-100

Author(s):

Andrew Harvey

Keyword(s):

Time Series ◽

Data Science ◽

Leading Indicators ◽

Control Groups ◽

Balanced Growth ◽

Time Series Modelling ◽

Key Variables ◽

The Uk ◽

The Relationship

This article shows how new time series models can be used to track the progress of an epidemic, forecast key variables and evaluate the effects of policies. The univariate framework of Harvey and Kattuman (2020, Harvard Data Science Review, Special Issue 1—COVID-19, https://hdsr.mitpress.mit.edu/pub/ozgjx0yn) is extended to model the relationship between two or more series and the role of common trends is discussed. Data on daily deaths from COVID-19 in Italy and the UK provides an example of leading indicators when there is a balanced growth. When growth is not balanced, the model can be extended by including a non-stationary component in one of the series. The viability of this model is investigated by examining the relationship between new cases and deaths in the Florida second wave of summer 2020. The balanced growth framework is then used as the basis for policy evaluation by showing how some variables can serve as control groups for a target variable. This approach is used to investigate the consequences of Sweden’s soft lockdown coronavirus policy in the spring of 2020.

Download Full-text

The Role of Stock Markets in Current Account Dynamics: A Time-Series Approach

SSRN Electronic Journal ◽

10.2139/ssrn.878873 ◽

2004 ◽

Author(s):

Benoit Mercereau

Keyword(s):

Time Series ◽

Current Account ◽

Stock Markets ◽

Current Account Dynamics ◽

Time Series Approach

Download Full-text

Does EO NDVI seasonal metrics capture variations in species composition and biomass due to grazing in semi-arid grassland savannas?

Biogeosciences ◽

10.5194/bg-12-4407-2015 ◽

2015 ◽

Vol 12 (14) ◽

pp. 4407-4419 ◽

Cited By ~ 13

Author(s):

J. L. Olsen ◽

S. Miehe ◽

P. Ceccato ◽

R. Fensholt

Keyword(s):

Time Series ◽

Species Composition ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Regional Scale ◽

Grazing Intensity ◽

Test Site ◽

Grazing Pressure ◽

Modis Ndvi ◽

Coarse Resolution

Abstract. Most regional scale studies of vegetation in the Sahel have been based on Earth observation (EO) imagery due to the limited number of sites providing continuous and long term in situ meteorological and vegetation measurements. From a long time series of coarse resolution normalized difference vegetation index (NDVI) data a greening of the Sahel since the 1980s has been identified. However, it is poorly understood how commonly applied remote sensing techniques reflect the influence of extensive grazing (and changes in grazing pressure) on natural rangeland vegetation. This paper analyses the time series of Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI metrics by comparing it with data from the Widou Thiengoly test site in northern Senegal. Field data include grazing intensity, end of season standing biomass (ESSB) and species composition from sizeable areas suitable for comparison with moderate – coarse resolution satellite imagery. It is shown that sampling plots excluded from grazing have a different species composition characterized by a longer growth cycle as compared to plots under controlled grazing or communal grazing. Also substantially higher ESSB is observed for grazing exclosures as compared to grazed areas, substantially exceeding the amount of biomass expected to be ingested by livestock for this area. The seasonal integrated NDVI (NDVI small integral; capturing only the signal inherent to the growing season recurrent vegetation), derived using absolute thresholds to estimate start and end of growing seasons, is identified as the metric most strongly related to ESSB for all grazing regimes. However plot-pixel comparisons demonstrate how the NDVI/ESSB relationship changes due to grazing-induced variation in annual plant species composition and the NDVI values for grazed plots are only slightly lower than the values observed for the ungrazed plots. Hence, average ESSB in ungrazed plots since 2000 was 0.93 t ha−1, compared to 0.51 t ha−1 for plots subjected to controlled grazing and 0.49 t ha−1 for communally grazed plots, but the average integrated NDVI values for the same period were 1.56, 1.49, and 1.45 for ungrazed, controlled and communal, respectively, i.e. a much smaller difference. This indicates that a grazing-induced development towards less ESSB and shorter-cycled annual plants with reduced ability to turn additional water in wet years into biomass is not adequately captured by seasonal NDVI metrics.

Download Full-text