On the relationship between the reliability of parameter estimates and hydrologic time series data used in calibration

1982 ◽  
Vol 18 (1) ◽  
pp. 146-154 ◽  
Author(s):  
George Kuczera
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Parameter Estimates ◽  
Hydrologic Time Series ◽  
The Relationship

Bias in the Estimation of Functional Relationships from Time Series Data

1985 ◽  
Vol 42 (1) ◽  
pp. 147-149 ◽  
Author(s):  
Carl J. Walters
Keyword(s):  
Time Series ◽  
Measurement Errors ◽  
Time Series Data ◽  
Series Data ◽  
Parameter Estimates ◽  
Natural Experiments ◽  
Independent Variables ◽  
Functional Relationships ◽  
Stock Recruitment ◽  
The Relationship

Functional relationships, such as stock–recruitment curves, are generally estimated from time series data where natural "random" factors have generated both deviations from the relationship and also informative variation in the independent variables. Even in the absence of measurement errors, such natural experiments can lead to severely biased parameter estimates. For stock–recruitment models, the bias is misleading for management: the stock will appear too productive when it is low, and too unproductive when it is large. The likely magnitude of such biases can and should be determined for any particular case by Monte Carlo simulations.

Exploring Territorial Differences in the Relationship between Marketing Variables

1977 ◽  
Vol 14 (2) ◽  
pp. 145-155 ◽  
Author(s):  
Dick R. Wittink
Keyword(s):  
Time Series ◽  
Market Share ◽  
Response Function ◽  
Time Series Data ◽  
Series Data ◽  
Price Sensitivity ◽  
Parameter Estimates ◽  
Second Stage ◽  
Marketing Variables ◽  
The Relationship

The parameters of a market share response function are estimated by using time series data separately for several sales territories. In a second stage an attempt is made to explain territorial differences in parameter estimates. It appears that for the brand studied advertising may serve to increase the price sensitivity.

scholarly journals PEMODELAN GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) SEASONAL PADA DATA CURAH HUJAN EMPAT KABUPATEN DI PROVINSI JAWA TENGAH

2019 ◽  
Vol 8 (4) ◽  
pp. 418-427
Author(s):  
Eko Siswanto ◽  
Hasbi Yasin ◽  
Sudarno Sudarno
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Space Time ◽  
Series Data ◽  
Inverse Distance Weighted ◽  
Modeling And Forecasting ◽  
Distance Weighted ◽  
The Relationship ◽  
Inverse Distance

In many applications, several time series data are recorded simultaneously at a number of locations. Time series data from nearby locations often to be related by spatial and time. This data is called spatial time series data. Generalized Space Time Autoregressive (GSTAR) model is one of space time models used to modeling and forecasting spatial time series data. This study applied GTSAR model to modeling volume of rainfall four locations in Jepara Regency, Kudus Regency, Pati Regency, and Grobogan Regency. Based on the smallest RMSE mean of forecasting result, the best model chosen by this study is GSTAR (11)-I(1)12 with the inverse distance weighted. Based on GSTAR(11)-I(1)12 with the inverse distance weighted, the relationship between the location shown on rainfall Pati Regency influenced by the rainfall in other regencies. Keywords: GSTAR, RMSE, Rainfall

scholarly journals An Empirical Analysis of Energy Consumption, FDI and High Quality Development Based on Time Series Data of Zhejiang Province

2020 ◽  
Vol 17 (9) ◽  
pp. 3321
Author(s):  
Shaolong Zeng ◽  
Yiqun Liu ◽  
Junjie Ding ◽  
Danlu Xu
Keyword(s):  
Time Series ◽  
Economic Development ◽  
Energy Consumption ◽  
Open Economy ◽  
High Speed ◽  
Time Series Data ◽  
Rapid Development ◽  
Series Data ◽  
High Quality ◽  
The Relationship

This paper aims to identify the relationship among energy consumption, FDI, and economic development in China from 1993 to 2017, taking Zhejiang as an example. FDI is the main factor of the rapid development of Zhejiang’s open economy, which promotes the development of the economy, but also leads to the growth in energy consumption. Based on the time series data of energy consumption, FDI inflow, and GDP in Zhejiang from 1993 to 2017, we choose the vector auto-regression (VAR) model and try to identify the relationship among energy consumption, FDI, and economic development. The results indicate that there is a long-run equilibrium relationship among them. The FDI inflow promotes energy consumption, and the energy consumption promotes FDI inflow in turn. FDI promotes economic growth indirectly through energy consumption. Therefore, improving the quality of FDI and energy efficiency has become an inevitable choice to achieve the transition of Zhejiang’s economy from high speed growth to high quality growth.

scholarly journals Visibility Graph Power Geometric Aggregation Operator and Its Application in Water, Energy and Food Efficiency Evaluation

2020 ◽  
Vol 17 (11) ◽  
pp. 3891
Author(s):  
Lihua Liu ◽  
Jing Huang ◽  
Huimin Wang
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Coupling Efficiency ◽  
Aggregation Operator ◽  
Visibility Graph ◽  
Series Data ◽  
Efficiency Evaluation ◽  
Geometric Operator ◽  
The Relationship

In the real decision-making process, there are so many time series values that need to be aggregated. In this paper, a visibility graph power geometric (VGPG) aggregation operator is developed, which is based on the complex network and power geometric operator. Time series data are converted into a visibility graph. A visibility matrix is developed to denote the links among different time series values. A new support function based on the distance of two values are proposed to measure the support degree of each other when the two time series values have visibility. The VGPG operator considers not only the relationship but also the similarity degree between two values. Meanwhile, some properties of the VGPG operator are also investigated. Finally, a case study for water, energy, and food coupling efficiency evaluation in China is illustrated to show the effectiveness of the proposed operator. Comparative analysis with the existing research is also offered to show the advantages of the proposed method.

Influence of systemic banking crisis and currency crisis on the relationship of export and economic growth

2017 ◽  
Vol 10 (1) ◽  
pp. 82-110
Author(s):  
Syed Ali Raza ◽  
Mohd Zaini Abd Karim
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Financial Crises ◽  
Currency Crises ◽  
Time Series Data ◽  
Banking Crises ◽  
Series Data ◽  
Content Type ◽  
Relationship Of ◽  
The Relationship

Purpose This study aims to investigate the influence of systemic banking crises, currency crises and global financial crisis on the relationship between export and economic growth in China by using the annual time series data from the period of 1972 to 2014. Design/methodology/approach The Johansen and Jeuuselius’ cointegration, auto regressive distributed lag bound testing cointegration, Gregory and Hansen’s cointegration and pooled ordinary least square techniques with error correction model have been used. Findings Results indicate the positive and significant effect of export of goods and services on economic growth in both long and short run, whereas the negative influence of systemic banking crises and currency crises over economic growth is observed. It is also concluded that the impact of export of goods and service on economic growth becomes insignificant in the presence of systemic banking crises and currency crises. The currency crises effect the influence of export on economic growth to a higher extent compared to systemic banking crises. Surprisingly, the export in the period of global financial crises has a positive and significant influence over economic growth in China, which conclude that the global financial crises did not drastically affect the export-growth nexus. Originality/value This paper makes a unique contribution to the literature with reference to China, being a pioneering attempt to investigate the effects of systemic banking crises and currency crises on the relationship of export and economic growth by using long-time series data and applying more rigorous econometric techniques.

Correlation analysis of continuous emotional response to music: Correcting for the effects of serial correlation

2001 ◽  
Vol 5 (1_suppl) ◽  
pp. 213-236 ◽  
Author(s):  
Emery Schubert
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Emotional Response ◽  
Serial Correlation ◽  
Time Series Data ◽  
Second Order ◽  
Series Data ◽  
First Order ◽  
The Relationship ◽  
Pearson Correlations

Publications of research concerning continuous emotional responses to music are increasing. The developing interest brings with it a need to understand the problems associated with the analysis of time series data. This article investigates growing concern in the use of conventional Pearson correlations for comparing time series data. Using continuous data collected in response to selected pieces of music, with two emotional dimensions for each piece, two falsification studies were conducted. The first study consisted of a factor analysis of the individual responses using the original data set and its first-order differenced transformation. The differenced data aligned according to theoretical constraints better than the untransformed data, supporting the use of first-order difference transformations. Using a similar method, the second study specifically investigated the relationship between Pearson correlations, difference transformations and the critical correlation coefficient above which the conventional correlation analysis remains internally valid. A falsification table was formulated and quantified through a hypothesis index function. The study revealed that correlations of undifferenced data must be greater than 0.75 for a valid interpretation of the relationship between bivariate emotional response time series data. First and second-order transformations were also investigated and found to be valid for correlation coefficients as low as 0.24. Of the three version of the data (untransformed, first-order differenced, and second-order differenced), first-order differenced data produced the fewest problems with serial correlation, whilst remaining a simple and meaningful transformation.

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