A SMOOTH NONPARAMETRIC CONDITIONAL DENSITY TEST FOR CATEGORICAL RESPONSES

We propose a consistent kernel-based specification test for conditional density models when the dependent variable is categorical/discrete. The method is applicable to popular parametric binary choice models such as the logit and probit specification and their multinomial and ordered counterparts, along with parametric count models, among others. The test is valid when the conditional density function contains both categorical and real-valued covariates. Theoretical support for the test and for a bootstrap-based version of the test is provided. Monte Carlo simulations are conducted to assess the finite-sample performance of the proposed method.

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Smoothed Maximum Score Estimation of Discrete Duration Models

Journal of Risk and Financial Management ◽

10.3390/jrfm12020064 ◽

2019 ◽

Vol 12 (2) ◽

pp. 64 ◽

Cited By ~ 1

Author(s):

Sadat Reza ◽

Paul Rilstone

Keyword(s):

Monte Carlo ◽

Monte Carlo Simulations ◽

Discrete Time ◽

Hazard Rate ◽

Duration Models ◽

Finite Sample ◽

Time Duration ◽

Maximum Score ◽

Finite Sample Properties ◽

Error Distributions

This paper extends Horowitz’s smoothed maximum score estimator to discrete-time duration models. The estimator’s consistency and asymptotic distribution are derived. Monte Carlo simulations using various data generating processes with varying error distributions and shapes of the hazard rate are conducted to examine the finite sample properties of the estimator. The bias-corrected estimator performs reasonably well for the models considered with moderately-sized samples.

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Adjacency matrix comparison for stochastic block models

Random Matrices Theory and Application ◽

10.1142/s2010326319500102 ◽

2019 ◽

Vol 08 (03) ◽

pp. 1950010

Author(s):

Guangren Yang ◽

Songshan Yang ◽

Wang Zhou

Keyword(s):

Monte Carlo ◽

Monte Carlo Simulations ◽

Adjacency Matrix ◽

Real Data ◽

Finite Sample ◽

Adjacency Matrices ◽

Block Models

In this paper, we study whether two networks arising from two stochastic block models have the same connection structures by comparing their adjacency matrices. We conduct Monte Carlo simulations study to examine the finite sample performance of the proposed method. A real data example is used to illustrate the proposed methodology.

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SPECIFICATION TEST FOR CONDITIONAL DISTRIBUTION WITH FUNCTIONAL DATA

Econometric Theory ◽

10.1017/s0266466611000351 ◽

2011 ◽

Vol 28 (2) ◽

pp. 363-386 ◽

Cited By ~ 7

Author(s):

Frederic Ferraty ◽

Alejandro Quintela-del-Río ◽

Philippe Vieu

Keyword(s):

Functional Data ◽

Explanatory Variable ◽

Alternative Hypothesis ◽

Specific Form ◽

Conditional Density ◽

Finite Sample ◽

Specification Test ◽

Test Statistic ◽

Specification Tests ◽

Local Alternative

In this paper we construct a statistic to test a specific form of the conditional density function. The main point of this work is to consider a functional explanatory variable, and the statistic is constructed following recent advances in nonparametric functional data analysis. The asymptotic behavior of the test statistic is studied under both the null hypothesis and some local alternative hypothesis. Then, the finite sample behavior of the method is studied through simulated examples. This paper is one of the first in the setting of nonparametric specification tests when functional data are involved.

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STRUCTURAL CHANGE IN NONSTATIONARY AR(1) MODELS

Econometric Theory ◽

10.1017/s0266466617000317 ◽

2017 ◽

Vol 34 (5) ◽

pp. 985-1017 ◽

Cited By ~ 4

Author(s):

Tianxiao Pang ◽

Terence Tai-Leung Chong ◽

Danna Zhang ◽

Yanling Liang

Keyword(s):

Monte Carlo ◽

Structural Change ◽

Monte Carlo Simulations ◽

Change Point ◽

Indicator Function ◽

The Other ◽

Finite Sample ◽

Mild Conditions ◽

Finite Sample Properties ◽

Image Position

This article revisits the asymptotic inference for nonstationary AR(1) models of Phillips and Magdalinos (2007a) by incorporating a structural change in the AR parameter at an unknown time k0. Consider the model ${y_t} = {\beta _1}{y_{t - 1}}I\{ t \le {k_0}\} + {\beta _2}{y_{t - 1}}I\{ t > {k_0}\} + {\varepsilon _t},t = 1,2, \ldots ,T$, where I{·} denotes the indicator function, one of ${\beta _1}$ and ${\beta _2}$ depends on the sample size T, and the other is equal to one. We examine four cases: Case (I): ${\beta _1} = {\beta _{1T}} = 1 - c/{k_T}$, ${\beta _2} = 1$; (II): ${\beta _1} = 1$, ${\beta _2} = {\beta _{2T}} = 1 - c/{k_T}$; (III): ${\beta _1} = 1$, ${\beta _2} = {\beta _{2T}} = 1 + c/{k_T}$; and case (IV): ${\beta _1} = {\beta _{1T}} = 1 + c/{k_T}$, ${\beta _2} = 1$, where c is a fixed positive constant, and kT is a sequence of positive constants increasing to ∞ such that kT = o(T). We derive the limiting distributions of the t-ratios of ${\beta _1}$ and ${\beta _2}$ and the least squares estimator of the change point for the cases above under some mild conditions. Monte Carlo simulations are conducted to examine the finite-sample properties of the estimators. Our theoretical findings are supported by the Monte Carlo simulations.

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Low-voltage EDS of magnesium ferrite Dendrites in a FEG-SEM

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100164854 ◽

1996 ◽

Vol 54 ◽

pp. 478-479

Author(s):

Matthew T. Johnson ◽

Ian M. Anderson ◽

Jim Bentley ◽

C. Barry Carter

Keyword(s):

Monte Carlo ◽

Quantitative Analysis ◽

Monte Carlo Simulations ◽

Cross Section ◽

Spatial Resolution ◽

Low Voltage ◽

Magnesium Ferrite ◽

X Ray ◽

Interaction Volume ◽

Ferrite Spinel

Energy-dispersive X-ray spectrometry (EDS) performed at low (≤ 5 kV) accelerating voltages in the SEM has the potential for providing quantitative microanalytical information with a spatial resolution of ∼100 nm. In the present work, EDS analyses were performed on magnesium ferrite spinel [(MgxFe1−x)Fe2O4] dendrites embedded in a MgO matrix, as shown in Fig. 1. spatial resolution of X-ray microanalysis at conventional accelerating voltages is insufficient for the quantitative analysis of these dendrites, which have widths of the order of a few hundred nanometers, without deconvolution of contributions from the MgO matrix. However, Monte Carlo simulations indicate that the interaction volume for MgFe2O4 is ∼150 nm at 3 kV accelerating voltage and therefore sufficient to analyze the dendrites without matrix contributions.Single-crystal {001}-oriented MgO was reacted with hematite (Fe2O3) powder for 6 h at 1450°C in air and furnace cooled. The specimen was then cleaved to expose a clean cross-section suitable for microanalysis.

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