scholarly journals Factor Models With Real Data: A Robust Estimation of the Number of Factors

2019 ◽  
Vol 64 (6) ◽  
pp. 2412-2425 ◽  
Author(s):  
Valentina Ciccone ◽  
Augusto Ferrante ◽  
Mattia Zorzi
Keyword(s):  
Robust Estimation ◽  
Real Data ◽  
Factor Models ◽  
Number Of Factors

scholarly journals Robust estimation for multivariate wrapped models

METRON ◽  
2021 ◽  
Author(s):  
Giovanni Saraceno ◽  
Claudio Agostinelli ◽  
Luca Greco
Keyword(s):  
Robust Estimation ◽  
Numerical Study ◽  
Real Data ◽  
Likelihood Method ◽  
Weighted Likelihood ◽  
Finite Sample ◽  
Pearson Residuals ◽  
Data Points ◽  
Wrapped Distributions ◽  
Standard Techniques

AbstractA weighted likelihood technique for robust estimation of multivariate Wrapped distributions of data points scattered on a $$p-$$ p - dimensional torus is proposed. The occurrence of outliers in the sample at hand can badly compromise inference for standard techniques such as maximum likelihood method. Therefore, there is the need to handle such model inadequacies in the fitting process by a robust technique and an effective downweighting of observations not following the assumed model. Furthermore, the employ of a robust method could help in situations of hidden and unexpected substructures in the data. Here, it is suggested to build a set of data-dependent weights based on the Pearson residuals and solve the corresponding weighted likelihood estimating equations. In particular, robust estimation is carried out by using a Classification EM algorithm whose M-step is enhanced by the computation of weights based on current parameters’ values. The finite sample behavior of the proposed method has been investigated by a Monte Carlo numerical study and real data examples.

scholarly journals Dynamic factor models: Does the specification matter?

SERIEs ◽  
2021 ◽  
Author(s):  
Karen Miranda ◽  
Pilar Poncela ◽  
Esther Ruiz
Keyword(s):  
Factor Models ◽  
Model Specification ◽  
Dynamic Factor ◽  
Dynamic Factor Models ◽  
Number Of Factors ◽  
Time Varying Parameters ◽  
Out Of Sample ◽  
Mixed Frequency Data ◽  
Natural Way ◽  
Factor Estimation

AbstractDynamic factor models (DFMs), which assume the existence of a small number of unobserved underlying factors common to a large number of variables, are very popular among empirical macroeconomists. Factors can be extracted using either nonparametric principal components or parametric Kalman filter and smoothing procedures, with the former being computationally simpler and robust against misspecification and the latter coping in a natural way with missing and mixed-frequency data, time-varying parameters, nonlinearities and non-stationarity, among many other stylized facts often observed in real systems of economic variables. This paper analyses the empirical consequences on factor estimation, in-sample predictions and out-of-sample forecasting of using alternative estimators of the DFM under various sources of potential misspecification. In particular, we consider factor extraction when assuming different number of factors and different factor dynamics. The factors are extracted from a popular data base of US macroeconomic variables, widely analyzed in the literature without consensus about the most appropriate model specification. We show that this lack of consensus is only marginally crucial when it comes to factor extraction, but it matters when the objective is out-of-sample forecasting.

scholarly journals Identification of Facet Models by Means of Factor Rotation: A Simulation Study and Data Analysis of a Test for the Berlin Model of Intelligence Structure

2020 ◽  
Vol 80 (5) ◽  
pp. 995-1019
Author(s):  
André Beauducel ◽  
Martin Kersting
Keyword(s):  
Factor Analysis ◽  
Data Analysis ◽  
Sample Size ◽  
Simulation Study ◽  
Simple Structure ◽  
Factor Models ◽  
Minimum Entropy ◽  
Factor Rotation ◽  
Number Of Factors ◽  
Target Rotation

We investigated by means of a simulation study how well methods for factor rotation can identify a two-facet simple structure. Samples were generated from orthogonal and oblique two-facet population factor models with 4 (2 factors per facet) to 12 factors (6 factors per facet). Samples drawn from orthogonal populations were submitted to factor analysis with subsequent Varimax, Equamax, Parsimax, Factor Parsimony, Tandem I, Tandem II, Infomax, and McCammon’s minimum entropy rotation. Samples drawn from oblique populations were submitted to factor analysis with subsequent Geomin rotation and a Promax-based Tandem II rotation. As a benchmark, we investigated a target rotation of the sample loadings toward the corresponding faceted population loadings. The three conditions were sample size ( n = 400, 1,000), number of factors ( q = 4-12), and main loading size ( l = .40, .50, .60). For less than six orthogonal factors Infomax and McCammon’s minimum entropy rotation and for six and more factors Tandem II rotation yielded the highest congruence of sample loading matrices with faceted population loading matrices. For six and more oblique factors Geomin rotation and a Promax-based Tandem II rotation yielded the highest congruence with faceted population loadings. Analysis of data of 393 participants that performed a test for the Berlin Model of Intelligence Structure revealed that the faceted structure of this model could be identified by means of a Promax-based Tandem II rotation of task aggregates corresponding to the cross-products of the facets. Implications for the identification of faceted models by means of factor rotation are discussed.

The Scree Test and the Number of Factors: a Dynamic Graphics Approach

2015 ◽  
Vol 18 ◽  
Author(s):  
Rubén Daniel Ledesma ◽  
Pedro Valero-Mora ◽  
Guillermo Macbeth
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Real Data ◽  
Component Analysis ◽  
Psychological Research ◽  
Scree Plot ◽  
Dynamic Graphics ◽  
Number Of Factors ◽  
Interactive Data ◽  
Scree Test

AbstractExploratory Factor Analysis and Principal Component Analysis are two data analysis methods that are commonly used in psychological research. When applying these techniques, it is important to determine how many factors to retain. This decision is sometimes based on a visual inspection of the Scree plot. However, the Scree plot may at times be ambiguous and open to interpretation. This paper aims to explore a number of graphical and computational improvements to the Scree plot in order to make it more valid and informative. These enhancements are based on dynamic and interactive data visualization tools, and range from adding Parallel Analysis results to "linking" the Scree plot with other graphics, such as factor-loadings plots. To illustrate our proposed improvements, we introduce and describe an example based on real data on which a principal component analysis is appropriate. We hope to provide better graphical tools to help researchers determine the number of factors to retain.

scholarly journals THE FINANCIAL RESOURCES OF STATE SOCIAL INSURANCE: TWO-FACTORS MODELS OF FORMATION

2020 ◽  
Author(s):  
Anatoliy Sydorchuk ◽  
Keyword(s):  
Social Insurance ◽  
Current Practice ◽  
Factor Models ◽  
The State ◽  
Financial Resources ◽  
Absolute Size ◽  
Wage Bill ◽  
Number Of Factors ◽  
Two Factors ◽  
The Impact

The purpose of the article is to consider the impact of a number of factors (the wage fund in the economy, its share in GDP, and state social insurance fees rates) on the result indicator – the amount of the financial resources of the state social insurance. Two-factor models of the formation of financial resources of the state social insurance in the article have investigated. The largest change in the result indicator has caused by the size of the wage fund in the economy and the size of the insurance fees rate is calculated. The current practice of reducing the number of insurance fees in 2016 negatively affected the number of financial resources of state social insurance have investigated. It is determined that the reserve for increasing the result indicator is an increase in the absolute size of the wage fund in the economy, which taxpayers due to certain circumstances do not seek to declare and tax. To a lesser extent, such a reserve is the share of the wage bill in the country's GDP.

A Simulation of Factor Analytic Reliability Varying Sample Size and Number of Variables

1979 ◽  
Vol 45 (2) ◽  
pp. 471-478 ◽  
Author(s):  
George E. Manners ◽  
Donald H. Brush
Keyword(s):  
Sample Size ◽  
Factor Structure ◽  
Random Sampling ◽  
Factor Model ◽  
Sampling Error ◽  
Factor Models ◽  
Factor Analytic ◽  
Number Of Factors ◽  
Alpha Factor ◽  
Analytic Models

An examination of four factor analytic models employing random sampling experiments is undertaken using a methodology and hypothetical population factor structure first employed by Browne (2). The factor models are each explored under four separate conditions, varying sample size and number of variables. Under these limited conditions, it is argued that there are no practical differences among the factor models considered with respect to sampling error in the absence of a Heywood variable. However, with respect to the ability of each model to capture, early and at convergence, the number of factors in the population, the alpha factor model is shown to have the greatest reliability.

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