scholarly journals Reduction of COVID-19 Incidence and Nonpharmacologic Interventions: Analysis Using a US County–Level Policy Data Set (Preprint)

2020 ◽  
Author(s):  
Senan Ebrahim ◽  
Henry Ashworth ◽  
Cray Noah ◽  
Adesh Kadambi ◽  
Asmae Toumi ◽  
...  
Keyword(s):  
Large Scale ◽  
Time Series Data ◽  
Main Tool ◽  
The United States ◽  
Policy Formulation ◽  
Contact Tracing ◽  
Series Data ◽  
County Level ◽  
Data Set ◽  
The Relationship

BACKGROUND Worldwide, nonpharmacologic interventions (NPIs) have been the main tool used to mitigate the COVID-19 pandemic. This includes social distancing measures (closing businesses, closing schools, and quarantining symptomatic persons) and contact tracing (tracking and following exposed individuals). While preliminary research across the globe has shown these policies to be effective, there is currently a lack of information on the effectiveness of NPIs in the United States. OBJECTIVE The purpose of this study was to create a granular NPI data set at the county level and then analyze the relationship between NPI policies and changes in reported COVID-19 cases. METHODS Using a standardized crowdsourcing methodology, we collected time-series data on 7 key NPIs for 1320 US counties. RESULTS This open-source data set is the largest and most comprehensive collection of county NPI policy data and meets the need for higher-resolution COVID-19 policy data. Our analysis revealed a wide variation in county-level policies both within and among states (<i>P</i>&lt;.001). We identified a correlation between workplace closures and lower growth rates of COVID-19 cases (<i>P</i>=.004). We found weak correlations between shelter-in-place enforcement and measures of Democratic local voter proportion (R=0.21) and elected leadership (R=0.22). CONCLUSIONS This study is the first large-scale NPI analysis at the county level demonstrating a correlation between NPIs and decreased rates of COVID-19. Future work using this data set will explore the relationship between county-level policies and COVID-19 transmission to optimize real-time policy formulation.

scholarly journals Effects of Money Demand on Trade Balance in Nigeria

2020 ◽  
Vol 6 (351) ◽  
pp. 23-44
Author(s):  
Oluwole Jacob Adeyemi ◽  
Isiaq O. Oseni ◽  
Sheriffdeen A. Tella
Keyword(s):  
Money Demand ◽  
Trade Balance ◽  
Time Series Data ◽  
Series Data ◽  
Data Set ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Long Run ◽  
Monetary Policy Committee ◽  
The Relationship

Previous studies appear to have concentrated on the effects of currency depreciation on trade balance and macroeconomic policy, while the relationship between money demand and trade balance is scantly documented in the literature. This paper therefore examines the effects of money demand on trade balance in Nigeria. For the analysis conducted, annual time series data covering the period ranging from 1986 to 2018 were used along with the Autoregressive Distributed Lag (ARDL) estimation technique. The long‑run coefficient of money demand was positively signed and statistically significant at 5% level. The positive relationship exhibited by the coefficient of money demand in the long run had a significant influence on trade balance. Thus, this implied that a unit percent increase in money demand would lead to a 1.57% significant increase in trade balance. The implication of this finding was that money demand had significantly influenced trade balance, enhancing the production of goods and fostering investment, which had led to increased growth. The paper recommends that the Central Bank of Nigeria through the Monetary Policy Committee should amend qualitative and quantitative credit control policies with the aim of improving lending to enhance the flow of credit to the real and exporting sector of the economy in order to bring about the desired effect on trade balance. However, the study is limited to an analysis of the existence of the relationship between money demand and trade balance using the Nigerian data set.

Who Benefits More From Manufacturing Foreign Direct Investment? Examining Its Earnings Distribution Effects Across Earnings Quantiles

2017 ◽  
Vol 31 (4) ◽  
pp. 285-298 ◽  
Author(s):  
Jeong-Il Park
Keyword(s):  
Foreign Direct Investment ◽  
Direct Investment ◽  
Time Series Data ◽  
The United States ◽  
Community Survey ◽  
Series Data ◽  
Data Set ◽  
Employment Opportunities ◽  
Earnings Distribution ◽  
Fair Compensation

Although several studies suggest foreign manufacturers in the United States may provide access to good quality employment opportunities with fair compensation and stable benefits, the question of who benefits more from the location of manufacturing foreign direct investment (FDI) remains open. Using the National Establishment Time Series data set and individual earnings data from the American Community Survey Public Use Microdata Sample files, this research conducts a quantile regression to estimate the earnings distribution effects that a concentration of manufacturing FDI may have on different earnings groups in Georgia between 2004 and 2010. The research does not measure inequality directly, but the findings both from place-of-work and place-of-residence earnings analyses suggest strong implications relating to the issue of inequality among people. The concentration of manufacturing FDI in a certain area shows the largest distribution effects on area workers in the lower earnings group and residents in the middle earnings group.

The Relationship Between Inflation and Unemployment in Namibia Within the Framework of the Phillips Curve

2021 ◽  
Vol 7 (5) ◽  
pp. 7-16
Author(s):  
Johanna Pangeiko Nautwima ◽  
Asa Romeo Asa
Keyword(s):  
Causal Relationship ◽  
Phillips Curve ◽  
Time Series Data ◽  
Policy Formulation ◽  
Least Square ◽  
Series Data ◽  
Causality Test ◽  
Long Run ◽  
Short Run ◽  
The Relationship

This study intended to empirically validate the applicability of the Phillips Curve in Namibia since independence, using semi-annual time series data, and taking into account the periods of the annus horribilis of the global financial crises and the Coronavirus Disease pandemic. It further sought to examine the nature of the relationship between inflation and unemployment to determine whether it is short-run or long-run and establish the causal relationship between the variables using various econometric analyses. The unit root tests indicate that the variables were stationary in their level forms, implying the absence of the long-run relationship. Hence, the Ordinary Least Square (OLS) model was performed to measure the short-run relationship between the variables. Results from the OLS analysis reveal a bidirectional nexus between inflation and unemployment, validating the presence of the Phillips Curve in the Namibian economy. These results correspond to the findings that incorporated the periods of economic shocks; thus, adjudging the critics of the Philips Curve regarding the consideration of economic shockwaves to be nonsensical in the Namibian economy. Finally, Granger causality test was conducted to establish the causal relationship between the variables, and results found inflation and unemployment to be unrelated. Based on these findings, the study recommends policymakers to adopt a policy mix, skewed to reducing unemployment predominately among the youth since the issues cannot be addressed simultaneously. Lastly, the study suggests future investigations to assess panel analyses on the phenomenon concerning developing countries, particularly those in the same region. It also recommends a significant focus on the determinants of inflation and unemployment since the variables were found to be independent of each other. This will give accurate directives to policymakers in an attempt to address the matter in terms of policy formulation and assimilation when they understand where the issue is deriving from.

scholarly journals Benchmarking a Catchment-Aware Long Short-Term Memory Network (LSTM) for Large-Scale Hydrological Modeling

2019 ◽  
Author(s):  
Frederik Kratzert ◽  
Daniel Klotz ◽  
Guy Shalev ◽  
Günter Klambauer ◽  
Sepp Hochreiter ◽  
...  
Keyword(s):  
Large Scale ◽  
Hydrological Modeling ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Hydrological Models ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Long Short Term Memory

Abstract. Regional rainfall-runoff modeling is an old but still mostly out-standing problem in Hydrological Sciences. The problem currently is that traditional hydrological models degrade significantly in performance when calibrated for multiple basins together instead of for a single basin alone. In this paper, we propose a novel, data-driven approach using Long Short-Term Memory networks (LSTMs), and demonstrate that under a big data paradigm, this is not necessarily the case. By training a single LSTM model on 531 basins from the CAMELS data set using meteorological time series data and static catchment attributes, we were able to significantly improve performance compared to a set of several different hydrological benchmark models. Our proposed approach not only significantly outperforms hydrological models that were calibrated regionally but also achieves better performance than hydrological models that were calibrated for each basin individually. Furthermore, we propose an adaption to the standard LSTM architecture, which we call an Entity-Aware-LSTM (EA-LSTM), that allows for learning, and embedding as a feature layer in a deep learning model, catchment similarities. We show that this learned catchment similarity corresponds well with what we would expect from prior hydrological understanding.

scholarly journals A Twenty-Year Analysis of Winds in California for Offshore Wind Energy Production Using WRF v4.1.2

2021 ◽  
Author(s):  
Alex Rybchuk ◽  
Mike Optis ◽  
Julie K. Lundquist ◽  
Michael Rossol ◽  
Walt Musial
Keyword(s):  
Time Series Data ◽  
Weather Prediction ◽  
Resource Assessment ◽  
Stable Stratification ◽  
The United States ◽  
Offshore Wind ◽  
Series Data ◽  
Commercial Activity ◽  
Data Set ◽  
Wind Resource

Abstract. Offshore wind resource characterization in the United States relies heavily on simulated winds from numerical weather prediction (NWP) models, given the lack of hub-height observations offshore. One such NWP data set used extensively by U.S. stakeholders is the Wind Integration National Dataset (WIND) Toolkit, a 7-year time-series data set produced in 2013 by the National Renewable Energy Laboratory. In this study, we present an update to that data set for offshore California that leverages recent advancements in NWP modeling capabilities and extends the period of record to a full 20 years. The data set predicts a significantly larger wind resource (0.25–1.75 m s−1 stronger), including in three Call Areas that the Bureau of Ocean Energy Management is considering for commercial activity. We conduct a set of yearlong simulations to study factors that contribute to this increase in the modeled wind resource. The largest impact arises from a change in the planetary boundary layer parameterization from the Yonsei University scheme to the Mellor-Yamada-Nakanishi-Niino scheme and their diverging wind profiles under stable stratification. Additionally, we conduct a refined wind resource assessment at the three Call Areas, characterizing distributions of wind speed, shear, veer, stability, frequency of wind droughts, and power production. We find that, depending on the attribute, the new data set can show substantial disagreement with the WIND Toolkit, thereby driving important changes in predicted power.

The Impact of Crack Enforcement on Police Budgets

1998 ◽  
Vol 28 (3) ◽  
pp. 701-724 ◽  
Author(s):  
Mary Bumgarner ◽  
David L. Sjoquist
Keyword(s):  
Urban Areas ◽  
Crack Cocaine ◽  
Time Series Data ◽  
The United States ◽  
Behavioral Changes ◽  
Series Data ◽  
Data Set ◽  
Cocaine Use ◽  
Crack Use ◽  
The Impact

During the 1980s, many urban areas in the United States experienced a widespread expansion in the use of drugs in general and crack cocaine in particular. This expansion of crack use is thought to have resulted in various behavioral changes, e.g., an increase in crime and an increase in expenditures to reduce drug use. This paper examines how local police spending responded to the spread of crack cocaine. We use a pooled cross-section, time series data set consisting of 18 cities over the period 1982 through 1989 to estimate the impact of crack cocaine use on police spending, and find that police expenditures increased significantly as crack cocaine use rose.

scholarly journals Satellite-derived sulfur dioxide (SO<sub>2</sub>) emissions from the 2014–2015 Holuhraun eruption (Iceland)

2019 ◽  
Vol 19 (7) ◽  
pp. 4851-4862 ◽  
Author(s):  
Elisa Carboni ◽  
Tamsin A. Mather ◽  
Anja Schmidt ◽  
Roy G. Grainger ◽  
Melissa A. Pfeffer ◽  
...  
Keyword(s):  
Time Series ◽  
Sulfur Dioxide ◽  
Large Scale ◽  
Climate Model ◽  
Time Series Data ◽  
Series Data ◽  
Data Set ◽  
Model Simulations ◽  
So2 Flux ◽  
Erupted Mass

Abstract. The 6-month-long 2014–2015 Holuhraun eruption was the largest in Iceland for 200 years, emitting huge quantities of sulfur dioxide (SO2) into the troposphere, at times overwhelming European anthropogenic emissions. Weather, terrain and latitude made continuous ground-based or UV satellite sensor measurements challenging. Infrared Atmospheric Sounding Interferometer (IASI) data are used to derive the first time series of daily SO2 mass present in the atmosphere and its vertical distribution over the entire eruption period. A new optimal estimation scheme is used to calculate daily SO2 fluxes and average e-folding time every 12 h. For the 6 months studied, the SO2 flux was observed to be up to 200 kt day−1 and the minimum total SO2 erupted mass was 4.4±0.8 Tg. The average SO2 e-folding time was 2.4±0.6 days. Where comparisons are possible, these results broadly agree with ground-based near-source measurements, independent remote-sensing data and values obtained from model simulations from a previous paper. The results highlight the importance of using high-resolution time series data to accurately estimate volcanic SO2 emissions. The SO2 mass missed due to thermal contrast is estimated to be of the order of 3 % of the total emission when compared to measurements by the Ozone Monitoring Instrument. A statistical correction for cloud based on the AVHRR cloud-CCI data set suggested that the SO2 mass missed due to cloud cover could be significant, up to a factor of 2 for the plume within the first kilometre from the vent. Applying this correction results in a total erupted mass of 6.7±0.4 Tg and little change in average e-folding time. The data set derived can be used for comparisons to other ground- and satellite-based measurements and to petrological estimates of the SO2 flux. It could also be used to initialise climate model simulations, helping to better quantify the environmental and climatic impacts of future Icelandic fissure eruptions and simulations of past large-scale flood lava eruptions.

scholarly journals Automated pipeline framework for processing of large-scale building energy time series data

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0240461
Author(s):  
Arash Khalilnejad ◽  
Ahmad M. Karimi ◽  
Shreyas Kamath ◽  
Rojiar Haddadian ◽  
Roger H. French ◽  
...  
Keyword(s):  
Time Series ◽  
Data Quality ◽  
Large Scale ◽  
Time Series Data ◽  
Building Energy ◽  
The United States ◽  
Series Data ◽  
Building Energy Efficiency ◽  
Large Populations ◽  
Energy Audits

Commercial buildings account for one third of the total electricity consumption in the United States and a significant amount of this energy is wasted. Therefore, there is a need for “virtual” energy audits, to identify energy inefficiencies and their associated savings opportunities using methods that can be non-intrusive and automated for application to large populations of buildings. Here we demonstrate virtual energy audits applied to large populations of buildings’ time-series smart-meter data using a systematic approach and a fully automated Building Energy Analytics (BEA) Pipeline that unifies, cleans, stores and analyzes building energy datasets in a non-relational data warehouse for efficient insights and results. This BEA pipeline is based on a custom compute job scheduler for a high performance computing cluster to enable parallel processing of Slurm jobs. Within the analytics pipeline, we introduced a data qualification tool that enhances data quality by fixing common errors, while also detecting abnormalities in a building’s daily operation using hierarchical clustering. We analyze the HVAC scheduling of a population of 816 buildings, using this analytics pipeline, as part of a cross-sectional study. With our approach, this sample of 816 buildings is improved in data quality and is efficiently analyzed in 34 minutes, which is 85 times faster than the time taken by a sequential processing. The analytical results for the HVAC operational hours of these buildings show that among 10 building use types, food sales buildings with 17.75 hours of daily HVAC cooling operation are decent targets for HVAC savings. Overall, this analytics pipeline enables the identification of statistically significant results from population based studies of large numbers of building energy time-series datasets with robust results. These types of BEA studies can explore numerous factors impacting building energy efficiency and virtual building energy audits. This approach enables a new generation of data-driven buildings energy analysis at scale.

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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