Regression Models for Ordinal Categorical Time Series Data

2016 ◽  
pp. 179-194
Author(s):  
Brajendra C. Sutradhar ◽  
R. Prabhakar Rao
Keyword(s):  
Time Series ◽  
Regression Models ◽  
Time Series Data ◽  
Series Data ◽  
Categorical Time Series

ANALYSIS OF CATEGORICAL TIME-SERIES TEXT RECALL DATA USING A CONNECTIONIST MODEL

1994 ◽  
Vol 02 (03) ◽  
pp. 283-305 ◽  
Author(s):  
R.M. GOLDEN
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Statistical Model ◽  
Prior Knowledge ◽  
Time Series Data ◽  
Statistical Tests ◽  
Series Data ◽  
Connectionist Model ◽  
Recall Data ◽  
Categorical Time Series

Categorical time-series are generated by discrete-time probabilistic dynamical systems which can only be in one of a small number of finite states at any given instant in time. A novel statistical methodology based upon log-linear modelling is proposed for analyzing categorical time-series data which allows one to incorporate a considerable amount of prior knowledge directly into the data analysis. The statistical model can be shown to be formally equivalent to a connectionist (i.e., artificial neural network) model, Methods for model selection and hypothesis testing using the new statistical model for samples with large numbers of observations are then developed using asymptotic statistical theory. To illustrate this new method of categorical time-series data analysis, the model is applied to the analysis of text free recall data from children and adults. These analyses indicated that the model can successfully use the order of recalled text propositions to discriminate among alternative theories of prior knowledge and alternative treatment conditions. The reliability of the large sample statistical tests were also checked using a boot-strap methodology and found to be acceptable.

scholarly journals Factors Influencing Indonesia’s Cashew Export Volume

Agriekonomika ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 205-214
Author(s):  
Oni Ringgu Lero ◽  
Agnes Quartina Pudjiastuti ◽  
Sumarno Sumarno
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Influencing Factors ◽  
Regression Models ◽  
Time Series Data ◽  
Series Data ◽  
Factors Influencing ◽  
Multiple Regression Models ◽  
Export Volume ◽  
Per Capita

Cashews contribute significantly to the Indonesian economy because it is one of the exporting countries. However, volume of exports tends to fluctuate, so it is necessary to identify the influencing factors. This study aims to analyze volume of Indonesian cashew exports and its determinants. Time series data for 8 variables during 1985–2016 were analyzed descriptively by multiple regression models. The results again show fluctuations in export volume and value over 1985–2016 period. Lowest export volume occurred in 1989, but its value was in 1985. Highest export volume and value occurred in 2015. National cashew export volume depends on the domestic cashew price, exchange rate and income per capita. Peanuts and coffee have a complementary relationship with cashews, while sugar has a substitution relationship with this commodity. Cashews are an inferior goods.

scholarly journals Pengaruh penanaman modal dalam negeri, upah minimum dan belanja modal terhadap daya serap tenaga kerja di Provinsi Jambi

2019 ◽  
Vol 14 (1) ◽  
pp. 37-44
Author(s):  
Rizki Kenraraswati ◽  
M. Syurya Hidayat ◽  
Yohanes Vyn Amzar
Keyword(s):  
Time Series ◽  
Multiple Regression ◽  
Minimum Wage ◽  
Regression Models ◽  
Time Series Data ◽  
Capital Expenditure ◽  
Series Data ◽  
Domestic Investment ◽  
Average Growth ◽  
Multiple Regression Models

The study aims to analyze the effect of domestic investment (PMDN), minimum wage (UMP) and capital expenditure (BM) on employment absorption in Jambi Province. The data used is time series data of Jambi Province during the period 2000-2016. Data were analyzed descriptively as well as multiple regression models. The results of the study found that: 1) the average growth of employment is 3.11percent per year, domestic investment is 11.67 percent per year, UMP is 16.44 percent per year and capital expenditure is 20.00 percent per year; 2) Simultaneously PMDN, UMP and BM have a significant effect on employment in Jambi Province. Partially the BM variable does not have a significant effect while the PMDN and UMP variables have a significant effect on employment in Jambi Province.

Prediction in Two­Equation Linear Regression Models

2005 ◽  
Vol 57 (3-4) ◽  
pp. 195-208
Author(s):  
Amitava Dey ◽  
V. K. Sharma ◽  
Himadri Ghosh
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Linear Regression Model ◽  
Regression Models ◽  
Time Series Data ◽  
Generalized Least Squares ◽  
Series Data ◽  
Linear Regression Models ◽  
Error Terms ◽  
Best Linear Unbiased

In regression models using time series data, the errors are generally correlated. The sample residuals contain useful information for predicting post­sample observations. This information, which is generally ignored, has been exploited here in deriving the best linear unbiased predictors in a 2­equation linear regression model. The gain in efficiency of the proposed predictors over the usual generalized least ­ squares predictors has been obtained and the particular case when error terms in the two equations follow AR(l) process has also been disscussed.

Novel Divide and Combine-Based Approach to Estimating Mixture Markov Model for Large Categorical Time Series Data: Application to Study of Clusters using Multiyear Travel Survey Data

2019 ◽  
Vol 2673 (6) ◽  
pp. 236-246
Author(s):  
Jingyue Zhang ◽  
Karthik C. Konduri ◽  
Renjie Chen ◽  
Nalini Ravishanker
Keyword(s):  
Time Series ◽  
Travel Behavior ◽  
Time Series Data ◽  
Markov Models ◽  
Large Data ◽  
Series Data ◽  
Travel Patterns ◽  
Travel Survey ◽  
Data Application ◽  
Categorical Time Series

Over the last few years, as with many other fields, the transportation discipline has been swept by the big data revolution. This revolution has not only brought about tremendous opportunities for conducting interesting data-driven analysis, it has also highlighted challenges associated with using traditional analytical methods to analyze these large datasets. To this end, this paper proposes a new Divide and Combine-based approach to estimating Mixture Markov models for analyzing large categorical time series data. The validity of this approach is demonstrated using a simulation study. Further, the feasibility and applicability is highlighted by conducting a clustering analysis of large activity–travel sequences using multiyear travel survey datasets. In the case study, each individual’s daily activity–travel behavior is characterized as a categorical time series that attempts to capture multiple aspects of travel and activity engagement simultaneously while also incorporating the timing and the schedule of different episodes. The proposed Divide and Combine-based Mixture Markov models are then used to cluster the large data. Subsequently, cluster compositions are explored to understand within and between-cluster differences and their associations with generational cohort factors, socioeconomic attributes, and demographic variables. As a preliminary exploration, the results suggest that travel patterns of individuals over the last three decades can be categorized into three types of travel patterns. Results also provide evidence in support of recent claims about different generational cohorts and their activity–travel behaviors.

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