scholarly journals DYNAMIC PRINCIPAL COMPONENT REGRESSION: APPLICATION TO AGE-SPECIFIC MORTALITY FORECASTING

2019 ◽  
Vol 49 (03) ◽  
pp. 619-645 ◽  
Author(s):  
Han Lin Shang
Keyword(s):  
New York ◽  
Time Series ◽  
Time Series Data ◽  
Principal Component Regression ◽  
Principal Component ◽  
Continuous Variable ◽  
Series Data ◽  
Temporal Dependence ◽  
Mortality Forecasting ◽  
Specific Mortality

AbstractIn areas of application, including actuarial science and demography, it is increasingly common to consider a time series of curves; an example of this is age-specific mortality rates observed over a period of years. Given that age can be treated as a discrete or continuous variable, a dimension reduction technique, such as principal component analysis (PCA), is often implemented. However, in the presence of moderate-to-strong temporal dependence, static PCA commonly used for analyzing independent and identically distributed data may not be adequate. As an alternative, we consider a dynamic principal component approach to model temporal dependence in a time series of curves. Inspired by Brillinger’s (1974, Time Series: Data Analysis and Theory. New York: Holt, Rinehart and Winston) theory of dynamic principal components, we introduce a dynamic PCA, which is based on eigen decomposition of estimated long-run covariance. Through a series of empirical applications, we demonstrate the potential improvement of 1-year-ahead point and interval forecast accuracies that the dynamic principal component regression entails when compared with the static counterpart.

Producers' Response to Changes in Prices and Marketing Policies: A Case Study of Sugarcane and Paddy in Eastern U.P. by S. C. Gupta and A. Majid. New York: Asia Publishing House, 1965.

1968 ◽  
Vol 8 (2) ◽  
pp. 308-309
Author(s):  
Mohammad Irshad Khan
Keyword(s):  
New York ◽  
Time Series ◽  
Time Series Data ◽  
Publishing House ◽  
Economic Incentives ◽  
The Other ◽  
Series Data ◽  
Poor Countries ◽  
Northern India

It is alleged that the agricultural output in poor countries responds very little to movements in prices and costs because of subsistence-oriented produc¬tion and self-produced inputs. The work of Gupta and Majid is concerned with the empirical verification of the responsiveness of farmers to prices and marketing policies in a backward region. The authors' analysis of the respon¬siveness of farmers to economic incentives is based on two sets of data (concern¬ing sugarcane, cash crop, and paddy, subsistence crop) collected from the district of Deoria in Eastern U.P. (Utter Pradesh) a chronically foodgrain deficit region in northern India. In one set, they have aggregate time-series data at district level and, in the other, they have obtained data from a survey of five villages selected from 170 villages around Padrauna town in Deoria.

scholarly journals ANALISIS DAMPAK KEPEMILIKAN SURAT BERHARGA OLEH BPD TERHADAP PERTUMBUHAN EKONOMI DAERAH

2018 ◽  
Vol 14 (1) ◽  
pp. 32-47
Author(s):  
Khairur dan Telisa Aulia Falian Raziqiin ◽  
Telisa Aulia Falian
Keyword(s):  
Economic Growth ◽  
New York ◽  
Time Series ◽  
Regional Development ◽  
Time Series Data ◽  
Financial Institution ◽  
Positive Influence ◽  
Regional Economic Growth ◽  
Series Data ◽  
Regional Economic

Local government-owned banks (BPD), was established in order to help accelerate the development of the area where the BPD located. The expected goals of this study are: To measure the effect of the placement of funds by BPD on regional economic growth, to measure investment lending by BPD to regional economic growth. Population was all the existing Regional Development Bank in Indonesia. Based on data from Bank Indonesia, the number of regional development banks perDesember 2013 as many as 26 banks. The type of data that will be used in this research is time series data (time series) from January 2009 until December 2013 The model that will be used in this research is the use of panel data. Results of research on Analysis of Impact of Ownership of Securities by BPD Against Regional Development, government capital spending, credit productive, ownership of securities by BPD positive effect on GDP, and significantly affect GDP, labor force have a positive influence on the GDP, but the effect was not significant workforce to GDP.Badan Pusat Statistik. Berbagai tahun. Data Realisasi APBD. Badan PusatStatistik, Jakarta. Bank Indonesia. Berbagai tahun. Laporan Publikasi Bank Umum. Bank Indonesia,Jakarta. Budiono. (2001). Ekonomi Moneter Edisi 3. Yogyakarta : BPFE Djojosubroto, Dono Iskandar. (2004). Koordinasi Kebijakan Fiskal dan Moneter di Indonesia Pasca Undang – undang Bank Indonesia 1999. Jakarta : Kompas Dornbusch, Rudiger, Stanley Fischer, Richard Startz. (2004). Makroekonomi. (Yusuf Wibisono, Roy Indra Mirazudin, terjemahan). Jakarta :MediaGlobal Edukasi. Gujarati, Damodar. (1997). Ekonometrika Dasar. (Sumarno Zein, terjemahan).Jakarta : Erlangga. Gultom, Lukdir. (2013). Tantangan Meningkatkan Efisiensi dan Efektifitas BPD sebagai Regional Champion Dalam Pengembangan Usaha Mikro, Kecil dan Menengah di Indonesia, Makalah SESPIBI Angkatan XXXI (Tidak Dipublikasikan). Bank Indonesia. Husnan, Suad. (2003). Dasar – dasar Teori Portofolio dan Analisis Sekuritas.Yogyakarta : UPP AMP YKPN. Kasmir. (2002). Dasar – Dasar Perbankan. Jakarta : PT. Raja Grafindo Persada. Kuncoro, Mudrajad. (2001) Metode Kuantitatif : Teori dan Aplikasi untuk Bisnis dan Ekonomi. Yogyakarta : AMP YKPN. Latumaerissa dan Julius R. (1999). Mengenal Aspek-aspek Operasi Bank Umum. Jakarta : Bumi Aksara. Lipsey, Richard G, et al. (1997). Pengantar Makro Ekonomi. ( Jaka Wasana danKibrandoko, terjemahan). Jakarta :Binarupa Aksara. Mankiw, Gregory. (2000). Macroeconomics Theory. New York : Worth PublisherInc. Nachrowi, Nachrowi D., Hardius Usman. (2006). Pendekatan Populer dan Praktis EKONOMETRIKA untuk Analisis Ekonomi dan Keuangan.Jakarta : Lembaga Penerbit FEUI. Rahmany, A. Fuad. (2004). Era Baru Kebijakan Fiskal : Pemikiran, Konsep dan Implementasi. Jakarta : Penerbit Buku Kompas, hal. 445 – 462. Rivai, Veithzal, Andria Permata Veithzal, Ferry N. Idroes. (2007). Bank and Financial Institution Management : Conventional & Sharia System, Jakarta : RajaGrafindo Persada. Sunarsip. (2008). Relasi Bank Pembangunan Daerah dan Perekonomian Daerah, dimuat dalam Republika, Rabu, 9 Januari 2008. Rubrik Pareto hal.16 Sunarsip. (2011). Transformasi BPD. Dimuat Infobank Edisi Januari 2011. Republik Indonesia, Kementrian Keuangan (2010), Potensi Bank Pembangunan Daerah Sebagai Pendiri Dana Pensiun Lembaga Keuangan,Tim Studi Potensi Bank Pembangunan Daerah Sebagai Pendiri Dana Pensiun. Jakarta.Waluyanto, Rahmat. (2004). Era Baru Kebijakan Fiskal : Pemikiran, Konsep dan Implementasi. Jakarta : Penerbit Buku Kompas, hal. 463 – 508. Wuryandari, Gantiah. (2013). Mengusung Bank Pembangunan Daerah (BPD) Sebagai Bank Fokus Sektor Strategis Dalam Mendukung Pembangunan Nasional, Makalah SESPIBI Angkatan XXXI (Tidak Dipublikasikan). Bank Indonesia.

Effective temporal dependence discovery in time series data

2018 ◽  
Vol 11 (8) ◽  
pp. 893-905 ◽  
Author(s):  
Qingchao Cai ◽  
Zhongle Xie ◽  
Meihui Zhang ◽  
Gang Chen ◽  
H. V. Jagadish ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Temporal Dependence

scholarly journals Analisis Potensi Gempa di Selatan Pulau Jawa Berdasarkan Pengamatan GPS

2020 ◽  
Vol 11 (3) ◽  
pp. 151
Author(s):  
Irwan Meilano ◽  
Agidia L. Tiaratama ◽  
Dudy D. Wijaya ◽  
Putra Maulida ◽  
S. Susilo ◽  
...  
Keyword(s):  
Time Series ◽  
Subduction Zones ◽  
Velocity Vector ◽  
Time Series Data ◽  
Principal Component ◽  
Reference Station ◽  
Tectonic Activity ◽  
Series Data ◽  
Local Data ◽  
Central Java

ABSTRAKPulau Jawa merupakan salah satu pulau yang memiliki kepadatan penduduk tinggi dengan aktivitas tektonik yang sangat aktif. Hal ini dikarenakan Pulau Jawa terletak di zona konvergensi Lempeng Indo-Australia dan Lempeng Eurasia. Aktivitas tektonik ini menghasilkan kegempaan di zona subduksi dan sesar di daratan Penelitian ini menganalisis pola vektor kecepatan yang dihasilkan melalui pengolahan data stasiun pengamatan GPS (Global Positioning System) CORS (Continuously Operating Reference Station) BIG (Badan Informasi Geospasial) di wilayah Pulau Jawa bagian selatan. Data koordinat harian dianalisis dengan metode PCA (Principal Component Analysis) untuk memisahkan sinyal tektonik berupa data deret waktu global dan non-tektonik berupa data deret waktu lokal dengan penerapan aturan pemilihan varian dominan nilai eigen dalam pembetukan PC (Principal Component) dan orthogonal vektor eigen sebagai bobot dalam meminimalkan korelasi. Hasil dari data deret waktu global dan lokal digunakan untuk menghitung besar kecepatan pergeseran dari tahun 2011 sampai 2018. Hasil pengolahan menunjukkan besar resultan vektor kecepatan pada data awal berselang 0,06 sampai 10,46 mm/tahun, pada data global antara 0,06 mm/ tahun sampai 10,39 mm/tahun, dan data lokal sebesar 0,0037 sampai 1,99 mm/tahun. Variasi spasial vektor kecepatan pengamatan GPS data domain PCA menunjukkan variasi pergeseran horizontal di wilayah Banten bergerak ke arah timur laut; Jawa Barat, Daerah Istimewa Yogyakarta, dan Jawa Tengah bergerak ke arah tenggara; dan Jawa Timur bergerak ke arah timur laut. Hasil dari inversi data pergeseran terhadap slip pada zona subduksi, menunjukkan terjadinya kekurangan slip atau terjadi coupling pada zona subduksi Jawa bagian timur dan barat, sementara terjadi kelebihan slip pada bagian tengah yang merupakan efek postseismic dari gempa Pangandaran 2006.Kata kunci: GPS, PCA, potensi gempa, vektor kecepatanABSTRACTJava is one of the island that has a high population density with very active tectonic activity. This is because Java Island is located in the convergence zone of the Indo-Australian Plate and the Eurasian Plate. This tectonic activity produces seismicity in subduction zones and inland faults. This study analyzes the velocity vector patterns generated through data processing of the GPS (Global Positioning System) CORS (Continuously Operating Reference Station) BIG (Geospatial Information Agency) observation station in the southern part of Java. Daily coordinate data were analyzed using PCA (Principal Component Analysis) method to separate time series of tectonic signals as global data and non-tectonic time series data as local data by applying the rules for selecting dominant variants of eigen values for PC formation and orthogonal eigen vectors as weights in minimizing correlations. The results from global and local time series data were used to calculate the magnitude of the displacement velocity from 2011 until 2018. The processing results show the resultant velocity vector in the initial data intermittent 0.06 to 10.46 mm/year, global data from 0.06 to 10.39 mm/year, and local data of 0.0037 to 1.99 mm/year. The spatial variation of the velocity vector in PCA domain data shows the horizontal displacement in the Banten region to the northeast; West Java, Yogyakarta Special Region, Central Java to southeast; and East Java moving to northeast. The results of the inversion of the surface displacement to slip data in the subduction zone show that there is a slip deficiency or coupling occurs in the subduction zones of Eastern and Western Java, while there is excess slip in the Central Java which is a post-seismic effect of the 2006 Pangandaran earthquake.Keywords: earthquake potential, GPS, PCA, velocity vector

scholarly journals Challenges in estimating the impact of vaccination with sparse data

2018 ◽  
Author(s):  
Kayoko Shioda ◽  
Cynthia Schuck-Paim ◽  
Robert J. Taylor ◽  
Roger Lustig ◽  
Lone Simonsen ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Simulation Analysis ◽  
Principal Component ◽  
Population Level ◽  
Series Data ◽  
Synthetic Control ◽  
Vaccine Impact ◽  
Pca Method ◽  
The Impact

ABSTRACTBackgroundThe synthetic control (SC) model is a powerful tool to quantify the population-level impact of vaccines, because it can adjust for trends unrelated to vaccination using a composite of control diseases. Because vaccine impact studies are often conducted using smaller subnational datasets, we evaluated the performance of SC models with sparse time series data. To obtain more robust estimates of vaccine effects from noisy time series, we proposed a possible alternative approach, “STL+PCA” method (seasonal-trend decomposition plus principal component analysis), which first extracts smoothed trends from the control time series and uses them to adjust the outcome.MethodsUsing both the SC and STL+PCA models, we estimated the impact of 10-valent pneumococcal conjugate vaccine (PCV10) on pneumonia hospitalizations among cases <12 months and 80+ years of age during 2004-2014 at the subnational level in Brazil. The performance of these models was also compared using simulation analyses.ResultsThe SC model was able to adjust for trends unrelated to PCV10 in larger states but not in smaller states. The simulation analysis confirmed that the SC model failed to select an appropriate set of control diseases when the time series were sparse and noisy, thereby generating biased estimates of the impact of vaccination when secular trends were present. The STL+PCA approach decreased bias in the estimates for smaller populations.ConclusionsEstimates from the SC model might be biased when data are sparse. The STL+PCA model provides more accurate evaluations of vaccine impact in smaller populations.

scholarly journals Comparison of outlier detection techniques in non-stationary time series data

2021 ◽  
Vol 27 (1) ◽  
pp. 55-60
Author(s):  
Sampson Twumasi-Ankrah ◽  
Simon Kojo Appiah ◽  
Doris Arthur ◽  
Wilhemina Adoma Pels ◽  
Jonathan Kwaku Afriyie ◽  
...  
Keyword(s):  
Time Series ◽  
Outlier Detection ◽  
Mahalanobis Distance ◽  
Time Series Data ◽  
Principal Component ◽  
Stationary Time Series ◽  
Series Data ◽  
Distance Method ◽  
Detection Techniques ◽  
Stationary Time

This study examined the performance of six outlier detection techniques using a non-stationary time series dataset. Two key issues were of interest. Scenario one was the method that could correctly detect the number of outliers introduced into the dataset whiles scenario two was to find the technique that would over detect the number of outliers introduced into the dataset, when a dataset contains only extreme maxima values, extreme minima values or both. Air passenger dataset was used with different outliers or extreme values ranging from 1 to 10 and 40. The six outlier detection techniques used in this study were Mahalanobis distance, depth-based, robust kernel-based outlier factor (RKOF), generalized dispersion, Kth nearest neighbors distance (KNND), and principal component (PC) methods. When detecting extreme maxima, the Mahalanobis and the principal component methods performed better in correctly detecting outliers in the dataset. Also, the Mahalanobis method could identify more outliers than the others, making it the "best" method for the extreme minima category. The kth nearest neighbor distance method was the "best" method for not over-detecting the number of outliers for extreme minima. However, the Mahalanobis distance and the principal component methods were the "best" performed methods for not over-detecting the number of outliers for the extreme maxima category. Therefore, the Mahalanobis outlier detection technique is recommended for detecting outlier in nonstationary time series data.

scholarly journals Dynamic autonomic nervous system patterns differentiate human emotions and manifest in resting physiology

2021 ◽  
Author(s):  
Lorenzo Pasquini ◽  
Fatemeh Noohi ◽  
Christina R. Veziris ◽  
Eena L. Kosik ◽  
Sarah R. Holley ◽  
...  
Keyword(s):  
Time Series ◽  
Nervous System ◽  
Autonomic Nervous System ◽  
Emotional Reactivity ◽  
Time Series Data ◽  
Activity Patterns ◽  
Principal Component ◽  
Series Data ◽  
Dynamic Activity ◽  
Autonomic Nervous

Whether activity in the autonomic nervous system differs during distinct emotions remains controversial. We obtained continuous multichannel recordings of autonomic nervous system activity in healthy adults during a video-based emotional reactivity task. Dimensionality reduction revealed five principal components in the autonomic time series data, and these modes of covariation differentiated periods of baseline from those of video-viewing. Unsupervised clustering of the principal component time series data uncovered separable autonomic states that distinguished among the five emotion-inducing trials. These autonomic states were also detected in baseline physiology but were intermittent and of smaller magnitude. Our results suggest the autonomic nervous system assembles dynamic activity patterns during emotions that are similar across people and are present even during undirected moments of rest.

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