Establishment about Short-Term Demand Predictive Model of Automobile — Based on Extensive Correlation Evaluation and Continuous Time Model

The previous models of automobile short-term demand were mainly for single time series. For this disadvantage, the definition of extensive correlation evaluation was proposed, and then the method was discussed to reflect the correlation of factors on automobile demand. Utilizing extensive skills, factors and sub-factors were represented as correlation eigenmatrix which could ensure the level of each factors influences on automobile demand. Accordingly, short-term demand predictive model of automobile was established based on continuous time model.

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Gaussian Estimation of a Two-factor Continuous Time Model of the Short-term Interest Rate

Economic Notes ◽

10.1111/1468-0300.00003 ◽

1999 ◽

Vol 28 (1) ◽

pp. 25-41 ◽

Cited By ~ 4

Author(s):

A. R. Bergstrom ◽

K. B. Nowman

Keyword(s):

Interest Rate ◽

Continuous Time ◽

Short Term ◽

Continuous Time Model ◽

Time Model ◽

Gaussian Estimation

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ESTIMATING CONTINUOUS-TIME MODELS ON THE BASIS OF DISCRETE DATA VIA AN EXACT DISCRETE ANALOG

Econometric Theory ◽

10.1017/s0266466608090452 ◽

2009 ◽

Vol 25 (4) ◽

pp. 1120-1137 ◽

Cited By ~ 9

Author(s):

J. Roderick McCrorie

Keyword(s):

Time Series ◽

Discrete Time ◽

Continuous Time ◽

Financial Time Series ◽

Discrete Data ◽

Discrete Analog ◽

Continuous Time Model ◽

Time Model ◽

Financial Time ◽

Time Modeling

This paper offers a perspective on A.R. Bergstrom’s contribution to continuous-time modeling, focusing on his preferred method of estimating the parameters of a structural continuous-time model using an exact discrete-time analog. Some inherent difficulties in this approach are discussed, which help to explain why, in spite of his prescience, the methods around his time were not universally adopted as he had hoped. Even so, it is argued that Bergstrom’s contribution and legacy is secure and retains some relevance today for the analysis of macroeconomic and financial time series.

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Improved Unit-Specific Event-Based Continuous-Time Model for Short-Term Scheduling of Continuous Processes: Rigorous Treatment of Storage Requirements

Industrial & Engineering Chemistry Research ◽

10.1021/ie060480o ◽

2007 ◽

Vol 46 (6) ◽

pp. 1764-1779 ◽

Cited By ~ 50

Author(s):

Munawar A. Shaik ◽

Christodoulos A. Floudas

Keyword(s):

Continuous Time ◽

Short Term ◽

Continuous Time Model ◽

Time Model ◽

Continuous Processes ◽

Specific Event ◽

Rigorous Treatment ◽

Event Based

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A Continuous-Time, Latent-Variable Model of Time Series Data

Political Analysis ◽

10.1093/pan/mpu020 ◽

2015 ◽

Vol 23 (2) ◽

pp. 278-298 ◽

Cited By ~ 2

Author(s):

Alexander M. Tahk

Keyword(s):

Time Series ◽

Continuous Time ◽

Latent Variable ◽

Time Series Data ◽

Series Data ◽

Continuous Time Model ◽

Variable Model ◽

Time Model ◽

Vector Autoregressive ◽

Irregular Spacing

Many types of time series data in political science, including polling data and events data, exhibit important features'such as irregular spacing, noninstantaneous observation, overlapping observation times, and sampling or other measurement error'that are ignored in most statistical analyses because of model limitations. Ignoring these properties can lead not only to biased coefficients but also to incorrect inference about the direction of causality. This article develops a continuous-time model to overcome these limitations. This new model treats observations as noisy samples collected over an interval of time and can be viewed as a generalization of the vector autoregressive model. Monte Carlo simulations and two empirical examples demonstrate the importance of modeling these features of the data.

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