Should Single-Equation Dynamic Gasoline Demand Models Include Moving Average Terms?

2008 ◽  
Author(s):  
Robert V. Breunig
Keyword(s):  
Moving Average ◽  
Single Equation ◽  
Gasoline Demand ◽  
Demand Models

scholarly journals Discriminating among alternative habit formation schemes in single-equation demand models

1981 ◽  
Vol 13 (3) ◽  
pp. 399-409 ◽  
Author(s):  
Richard D. Green ◽  
Rulon D. Pope ◽  
Tim T. Phipps
Keyword(s):  
Habit Formation ◽  
Single Equation ◽  
Demand Models

Assessing the Effects of Terrorism on Tourism by Use of Time Series Methods

2003 ◽  
Vol 9 (2) ◽  
pp. 179-190 ◽  
Author(s):  
Brian W. Sloboda
Keyword(s):  
Time Series ◽  
Impact Analysis ◽  
Moving Average ◽  
Single Equation ◽  
Autoregressive Moving Average ◽  
Short Term ◽  
Explanatory Variables ◽  
Use Of Time ◽  
Dependent Variables ◽  
Armax Model

This paper presents an assessment of the effects of terrorism on tourism by using time series methods, namely the ARMAX (autoregressive moving average with explanatory variables) model. This is a single-equation approach, which has the ability to provide impact analysis easily. The use of the ARMAX model allows for the general shape of the lag distribution of the impacts of the explanatory variables based on the ratio of lag polynomials for the independent and dependent variables. The ARMAX models, like the ARIMA models, provide for a short-term assessment of terrorist incidents on tourism.

An Examination of Demand Functions for Beef, Pork, and Broilers

1979 ◽  
Vol 8 (2) ◽  
pp. 299-311
Author(s):  
John F. Yanagida ◽  
Roger K. Conway
Keyword(s):  
Empirical Research ◽  
Single Equation ◽  
The Other ◽  
Total Output ◽  
Demand Functions ◽  
Response System ◽  
Demand Models ◽  
Short Run ◽  
Production Cycles

Empirical research on single equation, competitive demand models has employed either price dependent or quantity dependent forms. The price dependent models assume that total output is predetermined in the short-run due to the role of past prices and to production cycles as the case of the livestock industry (Heien). On the other hand, quantity dependency can be described as a supply response system.

Automatic Identification of Autoregressive Integrated Moving Average Time Series

1982 ◽  
Vol 14 (3) ◽  
pp. 156-166 ◽  
Author(s):  
Chin-Sheng Alan Kang ◽  
David D. Bedworth ◽  
Dwayne A. Rollier
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Automatic Identification ◽  
Autoregressive Integrated Moving Average

Smoothing Facilitates the Detection of Coupled Responses in Psychophysiological Time Series

2000 ◽  
Vol 14 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Joni Kettunen ◽  
Niklas Ravaja ◽  
Liisa Keltikangas-Järvinen
Keyword(s):  
Time Series ◽  
Type I Error ◽  
Electrodermal Activity ◽  
Moving Average ◽  
Type I ◽  
Facial Electromyography ◽  
Response Characteristics ◽  
Smoothing Methods ◽  
Response Systems ◽  
Different Response

Abstract We examined the use of smoothing to enhance the detection of response coupling from the activity of different response systems. Three different types of moving average smoothers were applied to both simulated interbeat interval (IBI) and electrodermal activity (EDA) time series and to empirical IBI, EDA, and facial electromyography time series. The results indicated that progressive smoothing increased the efficiency of the detection of response coupling but did not increase the probability of Type I error. The power of the smoothing methods depended on the response characteristics. The benefits and use of the smoothing methods to extract information from psychophysiological time series are discussed.

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.

A novel approach to white liquor control in a continuous digester using the Box-Jenkins methodology to predict chip bulk density

TAPPI Journal ◽  
2015 ◽  
Vol 14 (6) ◽  
pp. 395-402
Author(s):  
FLÁVIO MARCELO CORREIA ◽  
JOSÉ VICENTE HALLAK D’ANGELO ◽  
SUELI APARECIDA MINGOTI
Keyword(s):  
Bulk Density ◽  
Moving Average ◽  
Pulp Mill ◽  
Statistical Decision ◽  
White Liquor ◽  
Novel Approach ◽  
Cooking Process ◽  
Eucalyptus Kraft Pulp ◽  
Relevant Variables ◽  
Kraft Cooking

Alkali charge is one of the most relevant variables in the continuous kraft cooking process. The white liquor mass flow rate can be determined by analyzing the chip bulk density fed to the process. At the mills, the total time for this analysis usually is greater than the residence time in the digester. This can lead to an increasing error in the mass of white liquor added relative to the specified alkali charge. This paper proposes a new approach using the Box-Jenkins methodology to develop a dynamic model for predicting chip bulk density. Industrial data were gathered on 1948 observations over a period of 12 months from a Kamyr continuous digester at a bleached eucalyptus kraft pulp mill in Brazil. Autoregressive integrated moving average (ARIMA) models were evaluated according to different statistical decision criteria, leading to the choice of ARIMA (2,0,2) as the best forecasting model, which was validated against a new dataset gathered during 2 months of operations. A combination of predictors has shown more accurate results compared to those obtained by laboratory analysis, allowing a reduction of around 25% of the chip bulk density error to the alkali addition amount.

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