scholarly journals Modeling the Covid-19 Epidemic Using Time Series Econometrics

2020 ◽  
Author(s):  
Adam Goliński ◽  
Peter Spencer
Keyword(s):  
Time Series ◽  
Logistic Model ◽  
Classical Model ◽  
Realistic Model ◽  
Statistical Characteristics ◽  
Asian Countries ◽  
Time Series Econometrics ◽  
Case Count ◽  
The Uk ◽  
Empirical Stance

AbstractThe classic ‘logistic’ model has provided a realistic model of the behavior of Covid-19 in China and many East Asian countries. Once these countries passed the peak, the daily case count fell back, mirroring its initial climb in a symmetric way, just as the classic model predicts. However, in Italy and Spain, and now the UK and many other Western countries, the experience has been very different. The daily count has fallen back gradually from the peak but remained stubbornly high. The reason for the divergence from the classical model remain unclear. We take an empirical stance on this issue and develop a model that is based upon the statistical characteristics of the time series. With the possible exception of China, the workhorse logistic model is decisively rejected against more flexible alternatives.

Forward-looking financial risk management and the housing market in the United Kingdom: is there a role for sentiment indicators?

2020 ◽  
Vol 21 (5) ◽  
pp. 659-678
Author(s):  
Frederik Kunze ◽  
Tobias Basse ◽  
Miguel Rodriguez Gonzalez ◽  
Günter Vornholz
Keyword(s):  
Time Series ◽  
Housing Market ◽  
House Prices ◽  
Market Survey ◽  
Content Type ◽  
Time Series Econometrics ◽  
Property Prices ◽  
The Uk ◽  
Toda And Yamamoto ◽  
Forward Looking

Purpose In the current low-interest market environment, more and more asset managers have started to consider to invest in property markets. To implement adequate and forward-looking risk management procedures, this market should be analyzed in more detail. Therefore, this study aims to examine the housing market data from the UK. More specifically, sentiment data and house prices are examined, using techniques of time-series econometrics suggested by Toda and Yamamoto (1995). The monthly data used in this study is the RICS Housing Market Survey and the Nationwide House Price Index – covering the period from January 2000 to December 2018. Furthermore, the authors also analyze the stability of the implemented Granger causality tests. In sum, the authors found clear empirical evidence for unidirectional Granger causality from sentiment indicator to the house prices index. Consequently, the sentiment indicator can help to forecast property prices in the UK. Design/methodology/approach By investigating sentiment data for house prices using techniques of time-series econometrics (more specifically the procedure suggested by Toda and Yamamoto, 1995), the research question whether sentiment indicators can be helpful to predict property prices in the UK is analyzed empirically. Findings The empirical results show that the RICS Housing Market Survey can help to predict the house prices in the UK. Practical implications Given these findings, the information provided by property market sentiment indicators certainly should be used in a forward-looking early warning system for house prices in the UK. Originality/value To authors’ knowledge, this is the first paper that uses the procedure suggested by Toda and Yamaoto to search for suitable early warning indicators for investors in UK real estate assets.

Has the SARS-CoV-2 outbreak had an impact on uptake of digital cessation support in UK smokers? An interrupted time series analysis (Preprint)

2020 ◽  
Author(s):  
Olga Perski ◽  
Aleksandra Herbec ◽  
Lion Shahab ◽  
Jamie Brown
Keyword(s):  
Time Series ◽  
Smoking Cessation ◽  
Secondary Analysis ◽  
Interrupted Time Series ◽  
Step Change ◽  
Outcome Variable ◽  
Primary Analysis ◽  
Increasing Trend ◽  
The Uk ◽  
Temporary Closure

BACKGROUND The SARS-CoV-2 outbreak may motivate smokers to attempt to stop in greater numbers. However, given the temporary closure of UK stop smoking services and vape shops, smokers attempting to quit may instead seek out digital support, such as websites and smartphone apps. OBJECTIVE We examined, using an interrupted time series approach, whether the SARS-CoV-2 outbreak has been associated with a step change or increasing trend in UK downloads of an otherwise popular smoking cessation app, Smoke Free. METHODS Data were from daily and non-daily adult smokers in the UK who had downloaded the Smoke Free app between 1 January 2020 and 31 March 2020 (primary analysis) and 1 January 2019 and 31 March 2020 (secondary analysis). The outcome variable was the number of downloads aggregated at the 12-hourly (primary analysis) or daily level (secondary analysis). The explanatory variable was the start of the SARS-CoV-2 outbreak, operationalised as 1 March 2020 (primary analysis) and 15 January 2020 (secondary analysis). Generalised Additive Mixed Models adjusted for relevant covariates were fitted. RESULTS Data were collected on 45,105 (primary analysis) and 119,881 (secondary analysis) users. In both analyses, there was no evidence for a step change or increasing trend in downloads attributable to the start of the SARS-CoV-2 outbreak. CONCLUSIONS In the UK, between 1 January 2020 and 31 March 2020, and between 1 January 2019 and 31 March 2020, there was no evidence that the SARS-CoV-2 outbreak has been associated with a surge in downloads of a popular smoking cessation app. CLINICALTRIAL osf.io/zan2s

Time-series econometrics

2014 ◽  
pp. 199-200
Author(s):  
Michelle C. Baddeley ◽  
Diana V. Barrowclough
Keyword(s):  
Time Series ◽  
Time Series Econometrics

scholarly journals SARIMA Approach to Generating Synthetic Monthly Rainfall in the Sinú River Watershed in Colombia

Atmosphere ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 602
Author(s):  
Luisa Martínez-Acosta ◽  
Juan Pablo Medrano-Barboza ◽  
Álvaro López-Ramos ◽  
John Freddy Remolina López ◽  
Álvaro Alberto López-Lambraño
Keyword(s):  
Time Series ◽  
Water Resources ◽  
Autocorrelation Function ◽  
Information Criterion ◽  
Monthly Rainfall ◽  
Statistical Characteristics ◽  
Rainfall Time Series ◽  
River Watershed ◽  
Statistical Measures ◽  
Sarima Models

Seasonal Auto Regressive Integrative Moving Average models (SARIMA) were developed for monthly rainfall time series. Normality of the rainfall time series was achieved by using the Box Cox transformation. The best SARIMA models were selected based on their autocorrelation function (ACF), partial autocorrelation function (PACF), and the minimum values of the Akaike Information Criterion (AIC). The result of the Ljung–Box statistical test shows the randomness and homogeneity of each model residuals. The performance and validation of the SARIMA models were evaluated based on various statistical measures, among these, the Student’s t-test. It is possible to obtain synthetic records that preserve the statistical characteristics of the historical record through the SARIMA models. Finally, the results obtained can be applied to various hydrological and water resources management studies. This will certainly assist policy and decision-makers to establish strategies, priorities, and the proper use of water resources in the Sinú river watershed.

scholarly journals On the Connection between the GEP Performances and the Time Series Properties

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1853
Author(s):  
Alina Bărbulescu ◽  
Cristian Ștefan Dumitriu
Keyword(s):  
Time Series ◽  
Goodness Of Fit ◽  
Functional Form ◽  
Financial Time Series ◽  
Gene Expression Programming ◽  
Parametric Models ◽  
Statistical Characteristics ◽  
Data Series ◽  
Financial Time ◽  
Data Generating Process

Artificial intelligence (AI) methods are interesting alternatives to classical approaches for modeling financial time series since they relax the assumptions imposed on the data generating process by the parametric models and do not impose any constraint on the model’s functional form. Even if many studies employed these techniques for modeling financial time series, the connection of the models’ performances with the statistical characteristics of the data series has not yet been investigated. Therefore, this research aims to study the performances of Gene Expression Programming (GEP) for modeling monthly and weekly financial series that present trend and/or seasonality and after the removal of each component. It is shown that series normality and homoskedasticity do not influence the models’ quality. The trend removal increases the models’ performance, whereas the seasonality elimination results in diminishing the goodness of fit. Comparisons with ARIMA models built are also provided.

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

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.

A CARBON FOOTPRINT TIME SERIES OF THE UK – RESULTS FROM A MULTI-REGION INPUT–OUTPUT MODEL

2010 ◽  
Vol 22 (1) ◽  
pp. 19-42 ◽  
Author(s):  
Thomas Wiedmann ◽  
Richard Wood ◽  
Jan C. Minx ◽  
Manfred Lenzen ◽  
Dabo Guan ◽  
...  
Keyword(s):  
Time Series ◽  
Carbon Footprint ◽  
Input Output ◽  
The Uk

scholarly journals The MoT3 assay does not distinguish between Magnaporthe oryzae wheat and rice blast isolates from Bangladesh

2018 ◽  
Author(s):  
Dipali Rani Gupta ◽  
Claudia Sarai Reyes Avila ◽  
Joe Win ◽  
Darren M. Soares ◽  
Lauren S. Ryder ◽  
...  
Keyword(s):  
Magnaporthe Oryzae ◽  
Rice Blast ◽  
Dna Amplification ◽  
Diagnostic Tools ◽  
Diagnostic Assay ◽  
Asian Countries ◽  
Wheat Blast ◽  
New Hosts ◽  
Grass Hosts ◽  
The Uk

ABSTRACTThe blast fungus Magnaporthe oryzae is comprised of lineages that exhibit varying degrees of specificity on about 50 grass hosts, including rice, wheat and barley. Reliable diagnostic tools are essential given that the pathogen has a propensity to jump to new hosts and spread to new geographic regions. Of particular concern is wheat blast, which has suddenly appeared in Bangladesh in 2016 before spreading to neighboring India. In these Asian countries, wheat blast strains are now co-occurring with the destructive rice blast pathogen raising the possibility of genetic exchange between these destructive pathogens. We assessed the recently described MoT3 diagnostic assay and found that it did not distinguish between wheat and rice blast isolates from Bangladesh. The assay is based on primers matching the WB12 sequence corresponding to a fragment of the M. oryzae MGG_02337 gene annotated as a short chain dehydrogenase. These primers could not reliably distinguish between wheat and rice blast isolates from Bangladesh based on DNA amplification experiments performed in separate laboratories in Bangladesh and in the UK. In addition, comparative genomics of the WB12 sequence revealed a complex underlying genetic structure with related sequences across M. oryzae strains and in both rice and wheat blast isolates. We, therefore, caution against the indiscriminate use of this assay to identify wheat blast.

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