scholarly journals Morbidity and pollution: Model specification analysis for time-series data on hospital admissions

1982 ◽  
Vol 9 (4) ◽  
pp. 311-327 ◽  
Author(s):  
Ronald J Krumm ◽  
Philip E Graves
Keyword(s):  
Time Series ◽  
Hospital Admissions ◽  
Time Series Data ◽  
Model Specification ◽  
Series Data ◽  
Specification Analysis

Democratic peace: Does ethnic inclusiveness reduce interstate conflict?

2019 ◽  
pp. 019251211988473
Author(s):  
Seung-Whan Choi ◽  
Henry Noll
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Estimation Method ◽  
Interstate Conflict ◽  
Model Specification ◽  
Series Data ◽  
Democratic Peace ◽  
Interstate Disputes ◽  
Time Series Data Analysis ◽  
Cross National

In this study, we argue that ethnic inclusiveness is an important democratic norm that fosters interstate peace. When two states are socialized into the notion of ethnic tolerance, they acquire the ability to reach cooperative arrangements in time of crisis. Based on cross-national time-series data analysis covering the period 1950–2001, we illustrate how two states that are inclusive of their politically relevant ethnic groups are less likely to experience interstate disputes than states that remain exclusive. This finding was robust, regardless of sample size, intensity of the dispute, model specification, or estimation method. Therefore, we believe in the existence of ethnic peace: ethnic inclusiveness represents an unambiguous force for democratic peace.

scholarly journals METODE SSA PADA DATA PRODUKSI PERIKANAN TANGKAP DI PROVINSI JAWA BARAT

2017 ◽  
Vol 9 (2) ◽  
pp. 95
Author(s):  
Fadhilah Fitri ◽  
Nurul Fiskia Gamayanti ◽  
Gumgum Gunawan
Keyword(s):  
Time Series ◽  
Fish Consumption ◽  
Time Series Data ◽  
Singular Spectrum Analysis ◽  
Model Specification ◽  
Series Data ◽  
West Java ◽  
Forecasting Accuracy ◽  
Nonparametric Approach ◽  
Fish Catch

Indonesia is an archipelagic country where 2/3 of its territory is  ocean. The vastness of Indonesia's oceans is expected to produce abundant sea products that can meet the needs of Indonesian consumers, especially fish. Adequacy of the amount of fish consumption can be assessed through the number of fish catch. Based on data at the Ministry of Marine Affairs and Fisheries in 2015, West Java has a low growth of fish consumption, 6.05% in 2010-2014. Therefore, it is necessary to forecast the results of fish catch for several years ahead so it can be known whether the provision of fish consumption will be fulfilled or not. One method that can be used is Singular Spectrum Analysis (SSA). The SSA method is a flexible method because it uses a nonparametric approach. That is, in its application, this method does not require the model specification of time series data, as well as parametric assumptions. Forecasting accuracy of a method is said to be good if it has a MAPE value less than 20%. MAPE of SSA method forecast is 6.19% so that SSA method is suitable for forecasting of capture fishery production in West Java Province. The forecast for fishery production in West Java Province in 2015 for the first, second, third, and fourth quarter were 53,978.49 Ton, 54,406.91 Ton, 50,889.11 Ton, and 56,896.96 Ton, respectively.

scholarly journals Case Studies in Multi-unit LongitudinalModels with Random Coefficients and Patterned Correlation Structure

2016 ◽  
Vol 29 (2) ◽  
pp. 93-110
Author(s):  
Johannes Ledolter
Keyword(s):  
Time Series ◽  
Model Building ◽  
Time Series Data ◽  
Random Coefficients ◽  
Correlation Structure ◽  
Model Specification ◽  
Series Data ◽  
Data Sets ◽  
Economic Time Series ◽  
Data Set

Modelling issues in multi-unit longitudinal models with random coefficients and patterned correlation structure are illustrated in the context of three data sets. The first data set deals with short time series data on annual death rates and alcohol consumption of twenty-five European countries. The second data set deals with glaceologic time series data on snow temperature at 14 different locations within a small glacier in the Austrian Alps. The third data set consists of annual economic time series on factor productivity, anddomestic and foreign research/development (R&D) capital stocks. A practical model building approach–consisting of model specification, estimation, and diagnostic checking–is outlined in the context of these three data sets.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

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.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data
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