scholarly journals Modelling seasonality in Australian building approvals

2012 ◽  
Vol 12 (1) ◽  
pp. 26-36 ◽  
Author(s):  
Harry M Karamujic
Keyword(s):  
Time Series ◽  
Goodness Of Fit ◽  
South Australia ◽  
Residential Building ◽  
Seasonal Effects ◽  
General Level ◽  
Stochastic Trend ◽  
Out Of Sample ◽  
The Impact ◽  
Modelling Approach

The paper examines the impact of seasonal influences on Australian housing approvals, represented by the State of Victoria[1] building approvals for new houses (BANHs). The prime objective of BANHs is to provide timely estimates of future residential building work. Due to the relevance of the residential property sector to the property sector as whole, BANHs are viewed by economic analysts and commentators as a leading indicator of property sector investment and as such the general level of economic activity and employment. The generic objective of the study is to enhance the practice of modelling housing variables. In particular, the study seeks to cast some additional light on modelling the seasonal behaviour of BANHs by: (i) establishing the presence, or otherwise, of seasonality in Victorian BANHs; (ii) if present, ascertaining is it deterministic or stochastic; (iii) determining out of sample forecasting capabilities of the considered modelling specifications; and (iv) speculating on possible interpretation of the results. To do so the study utilises a structural time series model of Harwey (1989). The modelling results confirm that the modelling specification allowing for stochastic trend and deterministic seasonality performs best in terms of diagnostic tests and goodness of fit measures. This is corroborated with the analysis of out of sample forecasting capabilities of the considered modelling specifications, which showed that the models with deterministic seasonal specification exhibit superior forecasting capabilities. The paper also demonstrates that if time series are characterized by either stochastic trend or seasonality, the conventional modelling approach[2] is bound to be mis-specified i.e. would not be able to identify statistically significant seasonality in time series.According to the selected modeling specification, factors corresponding to June, April, December and November are found to be significant at five per cent level. The observed seasonality could be attributed to the ‘summer holidays’ and ‘the end of financial year’ seasonal effects. [1] Victoria is geographically the second smallest state in Australia. It is also the second most populous state in Australia. Australia has six states (New South Wales, Queensland, South Australia, Tasmania, Victoria, and Western Australia), and two territories (the Northern Territory and the Australian Capital Territory).[2] A modelling approach based on the assumption of deterministic trend and deterministic seasonality.

scholarly journals Decrease in blood donation rates in Japan: a time series analysis

2020 ◽  
Vol 23 ◽  
Author(s):  
Yutaka Owari ◽  
Nobuyuki Miyatake ◽  
Hiromi Suzuki
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Goodness Of Fit ◽  
Blood Donation ◽  
Initial Value ◽  
Post Intervention ◽  
Series Analysis ◽  
Average Analysis ◽  
Slope Difference ◽  
The Impact

ABSTRACT: Objective: To clarify that one of the causes for the decrease in blood donation (BD) rates was the introduction of the 400 ml BD program in 1986. Method: BP rates were monitored over 48 years (1965-2012) and were divided into pre- and post-intervention periods prior to analysis. An interrupted time series analysis was performed using annual data on BD rates, and the impact of the 400 ml BD program was investigated. Results: In a raw series, autoregressive integrated moving average analysis revealed a significant change in slope between the pre- and post-intervention periods in which the intervention factor was the 400 ml BD program. The parameters were as follows: intercept (initial value) = 0.315, confidence interval (CI) = (0.029, 0.601); slope (pre-intervention) = 0.316, CI = (0.293, 0.340); slope difference = -0.435, CI = (-0.462, -0.408); slope (post-intervention) = -0.119, CI = (-0.135, -0.103); all, p = 0.000; goodness-of-fit, R2 = 0.963. After adjusting for stationarity and autocorrelation, the parameters were as follows: intercept (initial value) = -0.699, CI = (-0.838, -0.560); slope (pre-intervention) = 0.136, CI = (0.085, 0.187); slope difference = -0.165, CI = (-0.247, -0.083); slope (post-intervention) = -0.029, CI = (-0.070, 0.012); all, p = 0.000 (except for slope (post-intervention), p = 0.170); goodness-of-fit, R2 = 0.930. Conclusion: One of the causes for decrease in BD rates may be due to the introduction of the 400 ml BD program in Japan.

scholarly journals On the Efficacy of Compact Radar Transponders for InSAR Geodesy: Results of Multi-year Field Tests

2021 ◽  
Author(s):  
Richard Czikhardt ◽  
Hans van der Marel ◽  
Juraj Papco ◽  
Ramon Hanssen
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
High Precision ◽  
Deformation Monitoring ◽  
Seasonal Effects ◽  
Attractive Alternative ◽  
Leg Length ◽  
Short Baseline ◽  
Wide Range ◽  
The Impact

Compact and low-cost radar transponders are an attractive alternative to corner reflectors (CR) for SAR interferometric (InSAR) deformation monitoring, datum connection, and geodetic data integration.Recently, such transponders have become commercially available for C-band sensors, which poses relevant questions on their characteristics in terms of radiometric, geometric, and phase stability. Especially for extended time series and for high-precision geodetic applications, the impact of secular or seasonal effects, such as variations in temperature and humidity, has yet to be proven.Here we address these challenges using a multitude of short baseline experiments with four transponders and six corner reflectors deployed at test sites in the Netherlands and Slovakia. Combined together, we analyzed 980 transponder measurements in Sentinel-1 time series to a maximum extent of 21 months.We find an average Radar Cross Section (RCS) of over 42 dBm2 within a range of up to 15 degrees of elevation misalignment, which is comparable to a triangular trihedral corner reflector with a leg length of 2.0 m. Its RCS shows temporal variations of 0.3--0.7~dBm2 (standard deviation) which is partially correlated with surface temperature changes.The precision of the InSAR phase double-differences over short baselines between a transponder and a stable reference corner reflectors is found to be 0.5-1.2 mm (one sigma). We observe a correlation with surface temperature, leading to seasonal variations of up to +/-3 mm, which should be modeled and corrected for in high precision InSAR applications. For precise SAR positioning, we observe antenna-specific constant internal electronic delays of 1.2-2.1 m in slant-range, i.e., within the range resolution of the Sentinel-1 Interferometric Wide Swath (IW) product, with a temporal variability of less than 20~cm.Comparing similar transponders from the same series, we observe distinctdifferences in performance. Our main conclusion is that these characteristics are favorable for a wide range of geodetic applications. For particular demanding applications, individual calibration of single devices is strongly recommended.

scholarly journals Building Approvals as a Leading Indicator of Property Sector Investment

2012 ◽  
Author(s):  
Harry M Karamujic
Keyword(s):  
Time Series ◽  
Construction Industry ◽  
Banking Industry ◽  
Seasonal Effects ◽  
Structural Time Series ◽  
Leading Indicator ◽  
Seasonal Behaviour ◽  
Out Of Sample ◽  
Cent Level ◽  
Government Construction

Overall, building approvals for new houses (BANHs) are viewed by most economic analysts/commentators as a leading indicator of property investment due to the importance of this sector to the whole economy and employment. This study seeks shed some additional light on modelling this seasonal behaviour of BANHs by: (i) establishing the presence of seasonality in Victorian BANHs; (ii) ascertaining it as to whether is deterministic or stochastic; (iii) estimating out-of-sample forecasting capabilities of the modelling specification; and (iv) speculating on possible interpretation of results. The study utilises a structural time series model of Harvey. Factors corresponding to June, April, December and November are found to be significant at five per cent level. The observed seasonality could be attributed to both the summer holidays and the end of financial year seasonal effects. Irrespective of partially incomplete nature of this research, the findings should be appealing to, among others, researchers, all levels of Government, construction industry and banking industry.  

Structural Model of Service Quality Factors Affecting Loyalty Sustainability

2020 ◽  
Vol 11 (2) ◽  
Author(s):  
Eman Al-erqi ◽  
◽  
Mohd Lizam Mohd Diah ◽  
Najmaddin Abo Mosali ◽  
◽  
...  
Keyword(s):  
International Students ◽  
Service Quality ◽  
Goodness Of Fit ◽  
Structural Model ◽  
Quality Factors ◽  
Factors Affecting ◽  
Measurement Models ◽  
Confirmatory Factor ◽  
The University ◽  
The Impact

This study seeks to address the impact of service quality affecting international student's satisfaction towards loyalty tothe Universiti Tun Hussein Onn Malaysia(UTHM). The aim of thestudy is to develop relationship between service quality factor and loyalty to the university from the international students’ perspectives. The study adopted quantitative approach where data was collected through questionnaire survey and analysed statistically. A total of 246 responses were received and found to be valid. The model was developed and analysed using AMOS-SEM software. Confirmatory factor analysis (CFA) function of the software was to assessed the measurement models and found that all the models achieved goodness of fit. Then path analysis function was used to assessed structural model and found that service qualityfactors have a significant effect on the students’ satisfaction and thus affecting the loyaltyto the university. Hopefully the outcome form this study will benefit the university in providing services especially to the international students.

River-Basin Planning for Control of Nitrate Pollution

1982 ◽  
Vol 14 (4-5) ◽  
pp. 245-252 ◽  
Author(s):  
C S Sinnott ◽  
D G Jamieson
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
River Basin ◽  
Water Resource ◽  
Potable Water ◽  
Potential Problem ◽  
Nitrate Pollution ◽  
Optimal Mix ◽  
Regional Basis ◽  
The Impact

The combination of increasing nitrate concentrations in the River Thames and the recent EEC Directive on the acceptable level in potable water is posing a potential problem. In assessing the impact of nitrates on water-resource systems, extensive use has been made of time-series analysis and simulation. These techniques are being used to define the optimal mix of alternatives for overcoming the problem on a regional basis.

Análise da relação entre O3, NO e NO2 usando técnicas de regressão linear múltipla.

GEOgraphia ◽  
2018 ◽  
Vol 20 (43) ◽  
pp. 124
Author(s):  
Amaury De Souza ◽  
Priscilla V Ikefuti ◽  
Ana Paula Garcia ◽  
Debora A.S Santos ◽  
Soetania Oliveira
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Nitrogen Dioxide ◽  
Multiple Linear Regression ◽  
Quality Parameters ◽  
Environmental Pollutant ◽  
Environmental Research ◽  
Linear Regression Models ◽  
Mato Grosso ◽  
The Impact

Análise e previsão de parâmetros de qualidade do ar são tópicos importantes da pesquisa atmosférica e ambiental atual, devido ao impacto causado pela poluição do ar na saúde humana. Este estudo examina a transformação do dióxido de nitrogênio (NO2) em ozônio (O3) no ambiente urbano, usando o diagrama de séries temporais. Foram utilizados dados de concentração de poluentes ambientais e variáveis meteorológicas para prever a concentração de O3 na atmosfera. Foi testado o emprego de modelos de regressão linear múltipla como ferramenta para a predição da concentração de O3. Os resultados indicam que o valor da temperatura e a presença de NO2 influenciam na concentração de O3 em Campo Grande, capital do Estado do Mato Grosso do Sul. Palavras-chave: Ozônio. Dióxido de nitrogênio. Séries cronológicas. Regressões. ANALYSIS OF THE RELATIONSHIP BETWEEN O3, NO AND NO2 USING MULTIPLE LINEAR REGRESSION TECHNIQUES.Abstract: Analysis and prediction of air quality parameters are important topics of current atmospheric and environmental research due to the impact caused by air pollution on human health. This study examines the transformation of nitrogen dioxide (NO2) into ozone (O3) in the urban environment, using the time series diagram. Environmental pollutant concentration and meteorological variables were used to predict the O3 concentration in the atmosphere. The use of multiple linear regression models was tested as a tool to predict O3 concentration. The results indicate that the temperature value and the presence of NO2 influence the O3 concentration in Campo Grande, capital of the State of Mato Grosso do Sul.Keywords: Ozone. Nitrogen dioxide. Time series. Regressions. ANÁLISIS DE LA RELACIÓN ENTRE O3, NO Y NO2 UTILIZANDO MÚLTIPLES TÉCNICAS DE REGRESIÓN LINEAL.Resumen: Análisis y previsión de los parámetros de calidad del aire son temas importantes de la actual investigación de la atmósfera y el medio ambiente, debido al impacto de la contaminación atmosférica sobre la salud humana. Este estudio examina la transformación del dióxido de nitrógeno (NO2) en ozono (O3) en el entorno urbano, utilizando el diagrama de series de tiempo. Las concentraciones de los contaminantes ambientales de datos y variables climáticas fueron utilizadas para predecir la concentración de O3 en la atmósfera. El uso de múltiples modelos de regresión lineal como herramienta para predecir la concentración de O3 se puso a prueba. Los resultados indican que el valor de la temperatura y la presencia de NO2 influyen en la concentración de O3 en Campo Grande, capital del Estado de Mato Grosso do Sul.Palabras clave: Ozono. Dióxido de nitrógeno. Series de tiempo. Regresiones.

Intervention Modeling

2017 ◽  
Author(s):  
Richard McCleary ◽  
David McDowall ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Intervention Model ◽  
Noise Component ◽  
Autoregressive Integrated Moving Average ◽  
Noise Function ◽  
Modeling Strategy ◽  
The Impact ◽  
True Structure

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.

EFFECTS OF TREND AND PERIODICITY ON THE CORRELATION DIMENSION AND THE LYAPUNOV EXPONENTS

2008 ◽  
Vol 18 (12) ◽  
pp. 3679-3687 ◽  
Author(s):  
AYDIN A. CECEN ◽  
CAHIT ERKAL
Keyword(s):  
Time Series ◽  
Lyapunov Exponent ◽  
Lyapunov Exponents ◽  
Correlation Dimension ◽  
Time Series Data ◽  
Logistic Map ◽  
Model Identification ◽  
Series Data ◽  
Largest Lyapunov Exponent ◽  
The Impact

We present a critical remark on the pitfalls of calculating the correlation dimension and the largest Lyapunov exponent from time series data when trend and periodicity exist. We consider a special case where a time series Zi can be expressed as the sum of two subsystems so that Zi = Xi + Yi and at least one of the subsystems is deterministic. We show that if the trend and periodicity are not properly removed, correlation dimension and Lyapunov exponent estimations yield misleading results, which can severely compromise the results of diagnostic tests and model identification. We also establish an analytic relationship between the largest Lyapunov exponents of the subsystems and that of the whole system. In addition, the impact of a periodic parameter perturbation on the Lyapunov exponent for the logistic map and the Lorenz system is discussed.

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