scholarly journals Model Selection Procedures in Bounds Test of Cointegration: Theoretical Comparison and Empirical Evidence

Economies ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 49 ◽  
Author(s):  
Waqar Badshah ◽  
Mehmet Bulut
Keyword(s):  
Model Selection ◽  
Akaike Information Criterion ◽  
Bayesian Information Criterion ◽  
Selection Process ◽  
Information Criterion ◽  
Small Sample ◽  
Information Criteria ◽  
Path Model ◽  
Sample Sizes ◽  
Bounds Test

Only unstructured single-path model selection techniques, i.e., Information Criteria, are used by Bounds test of cointegration for model selection. The aim of this paper was twofold; one was to evaluate the performance of these five routinely used information criteria {Akaike Information Criterion (AIC), Akaike Information Criterion Corrected (AICC), Schwarz/Bayesian Information Criterion (SIC/BIC), Schwarz/Bayesian Information Criterion Corrected (SICC/BICC), and Hannan and Quinn Information Criterion (HQC)} and three structured approaches (Forward Selection, Backward Elimination, and Stepwise) by assessing their size and power properties at different sample sizes based on Monte Carlo simulations, and second was the assessment of the same based on real economic data. The second aim was achieved by the evaluation of the long-run relationship between three pairs of macroeconomic variables, i.e., Energy Consumption and GDP, Oil Price and GDP, and Broad Money and GDP for BRICS (Brazil, Russia, India, China and South Africa) countries using Bounds cointegration test. It was found that information criteria and structured procedures have the same powers for a sample size of 50 or greater. However, BICC and Stepwise are better at small sample sizes. In the light of simulation and real data results, a modified Bounds test with Stepwise model selection procedure may be used as it is strongly theoretically supported and avoids noise in the model selection process.

scholarly journals Model Selection Process in Time Series Analysis of Production System with Random Output

2018 ◽  
Vol 413 ◽  
pp. 012057 ◽  
Author(s):  
S. O. Ongbali ◽  
A. C. Igboanugo ◽  
S. A. Afolalu ◽  
M. O. Udo ◽  
I. P. Okokpujie
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Time Series Analysis ◽  
Production System ◽  
Selection Process ◽  
Series Analysis

The role of simplicity in an econometric model selection process

2002 ◽  
pp. 207-226
Author(s):  
Antonio Aznar ◽  
M. Isabel Ayuda ◽  
Carmen García-Olaverri
Keyword(s):  
Model Selection ◽  
Econometric Model ◽  
Selection Process

scholarly journals Plasma radiometabolite correction in dynamic PET studies: Insights on the available modeling approaches

2015 ◽  
Vol 36 (2) ◽  
pp. 326-339 ◽  
Author(s):  
Matteo Tonietto ◽  
Gaia Rizzo ◽  
Mattia Veronese ◽  
Masahiro Fujita ◽  
Sami S Zoghbi ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Model Selection ◽  
Kinetic Modeling ◽  
Selection Process ◽  
Comprehensive Analysis ◽  
Dynamic Pet ◽  
Brain Pet ◽  
Alternative Approaches ◽  
Arterial Plasma

Full kinetic modeling of dynamic PET images requires the measurement of radioligand concentrations in the arterial plasma. The unchanged parent radioligand must, however, be separated from its radiometabolites by chromatographic methods. Thus, only few samples can usually be analyzed and the resulting measurements are often noisy. Therefore, the measurements must be fitted with a mathematical model. This work presents a comprehensive analysis of the different models proposed in the literature to describe the plasma parent fraction (PPf) and of the alternative approaches for radiometabolite correction. Finally, we used a dataset of [11C]PBR28 brain PET data as a case study to guide the reader through the PPf model selection process.

scholarly journals Impact of the Dust Aerosol Model on the VIIRS Aerosol Optical Depth (AOD) Product across China

2020 ◽  
Vol 12 (6) ◽  
pp. 991 ◽  
Author(s):  
Yang Wang ◽  
Liangfu Chen ◽  
Jinyuan Xin ◽  
Xinhui Wang
Keyword(s):  
Model Selection ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Southern China ◽  
Selection Process ◽  
Atmospheric Condition ◽  
Regression Equation ◽  
Research Network ◽  
Unexpected Result ◽  
Aerosol Model

The Visible Infrared Imaging Radiometer Suite (VIIRS) has been observing aerosol optical depth (AOD), which is a critical parameter in air pollution and climate change, for more than 7 years since 2012. Due to limited and uneven distribution of the Aerosol Robotic Network (AERONET) station in China, the independent data from the Campaign on Atmospheric Aerosol Research Network of China (CARE-China) was used to evaluate the National Oceanic and Atmospheric Administration (NOAA) VIIRS AOD products in six typical sites and analyze the influence of the aerosol model selection process in five subregions, particularly for dust. Compared with ground-based observations, the performance of all retrievals (except the Shapotou (SPT) site) is similar to other previous studies on a global scale. However, the results illustrate that the AOD retrievals with the dust model showed poor consistency with a regression equation as y = 0.312x + 0.086, while the retrievals obtained from the other models perform much better with a regression equation as y = 0.783x + 0.119. The poor AOD retrieval with the dust model was also verified by a comparison with the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol product. The results show they have a lower correlation coefficient (R) and a higher mean relative error (MRE) when the aerosol model used in the retrieval is identified as dust. According to the Ultraviolet Aerosol Index (UVAI), the frequency of dust type over southern China is inconsistent with the actual atmospheric condition. In addition, a comparison of ground-based Ångström exponent (α) values yields an unexpected result that the dust model percentage exceed 40% when α < 1.0, and the mean α shows a high value of ~0.75. Meanwhile, the α peak value (~1.1) of the “dust” model determined by a satellite retravel algorithm indicate there is some problem in the dust model selection process. This mismatching of the aerosol model may partly explain the low accuracy at the SPT and the systemic biases in regional and global validations.

On the Autoregressive Time Series Model Using Real and Complex Analysis

Forecasting ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 716-728
Author(s):  
Torsten Ullrich
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Closed Form ◽  
Autoregressive Model ◽  
Complex Analysis ◽  
Time Series Data ◽  
Selection Process ◽  
Global Structure ◽  
Series Data ◽  
Time Step

The autoregressive model is a tool used in time series analysis to describe and model time series data. Its main structure is a linear equation using the previous values to compute the next time step; i.e., the short time relationship is the core component of the autoregressive model. Therefore, short-term effects can be modeled in an easy way, but the global structure of the model is not obvious. However, this global structure is a crucial aid in the model selection process in data analysis. If the global properties are not reflected in the data, a corresponding model is not compatible. This helpful knowledge avoids unsuccessful modeling attempts. This article analyzes the global structure of the autoregressive model through the derivation of a closed form. In detail, the closed form of an autoregressive model consists of the basis functions of a fundamental system of an ordinary differential equation with constant coefficients; i.e., it consists of a combination of polynomial factors with sinusoidal, cosinusoidal, and exponential functions. This new insight supports the model selection process.

Latent Class Conjoint Choice Models: A Guide for Model Selection, Estimation, Validation, and Interpretation of Results

Marketing ZFP ◽  
2019 ◽  
Vol 41 (4) ◽  
pp. 3-20
Author(s):  
Friederike Paetz ◽  
Maren Hein ◽  
Peter Kurz ◽  
Winfried Steiner
Keyword(s):  
Model Selection ◽  
Consumer Choice ◽  
Latent Class ◽  
Selection Process ◽  
Theoretical Background ◽  
Preference Heterogeneity ◽  
Choice Data ◽  
Data Set ◽  
Mnl Model ◽  
Interpretation Of Results

The consideration of preference heterogeneity in consumer choice behavior has become state of the art. In addition, the identification of consumer segments remains essential for marketing managers. For disaggregate consumer choice data representing the basis of segmentation, the latent class multinomial logit (MNL) model is currently the most popular approach for estimating segment-specific preferences. After addressing the theoretical background of the latent class MNL model, we use an empirical choice-based conjoint data set to illustrate model estimation and validation, as well as how the estimation results should be interpreted. A particular focus lies on the model selection process, i.e. the determination of an appropriate number of segments. We further work out interpretation pitfalls when the existing preference heterogeneity of consumers is ignored. This will ultimately provide a guide for applying the latent class MNL model regarding model selection, estimation, validation, and interpretation of results both from a statistical and managerial perspective.

Effects of Model Selection on Inference

1991 ◽  
Vol 7 (2) ◽  
pp. 163-185 ◽  
Author(s):  
B.M. Pötscher
Keyword(s):  
Model Selection ◽  
Asymptotic Distribution ◽  
Selection Process ◽  
Asymptotic Properties ◽  
Selection Procedure ◽  
Model Selection Procedure ◽  
Parameter Estimators

The asymptotic properties of parameter estimators which are based on a model that has been selected by a model selection procedure are investigated. In particular, the asymptotic distribution is derived and the effects of the model selection process on subsequent inference are illustrated.

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