scholarly journals ESTIMATION FOR THE PREDICTION OF POINT PROCESSES WITH MANY COVARIATES

2017 ◽  
Vol 34 (3) ◽  
pp. 598-627 ◽  
Author(s):  
Alessio Sancetta
Keyword(s):  
Linear Combination ◽  
Point Processes ◽  
Estimation Error ◽  
Asymptotic Properties ◽  
Additive Model ◽  
Rates Of Convergence ◽  
Finite Sample ◽  
Intensity Measure ◽  
Variable Screening ◽  
The Impact

Estimation of the intensity of a point process is considered within a nonparametric framework. The intensity measure is unknown and depends on covariates, possibly many more than the observed number of jumps. Only a single trajectory of the counting process is observed. Interest lies in estimating the intensity conditional on the covariates. The impact of the covariates is modelled by an additive model where each component can be written as a linear combination of possibly unknown functions. The focus is on prediction as opposed to variable screening. Conditions are imposed on the coefficients of this linear combination in order to control the estimation error. The rates of convergence are optimal when the number of active covariates is large. As an application, the intensity of the buy and sell trades of the New Zealand Dollar futures is estimated and a test for forecast evaluation is presented. A simulation is included to provide some finite sample intuition on the model and asymptotic properties.

scholarly journals Quantile Regression with Generated Regressors

Econometrics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 16
Author(s):  
Liqiong Chen ◽  
Antonio F. Galvao ◽  
Suyong Song
Keyword(s):  
Quantile Regression ◽  
Estimation Error ◽  
Asymptotic Variance ◽  
Asymptotic Properties ◽  
Estimation Procedure ◽  
Finite Sample ◽  
Testing Procedures ◽  
Engel Curves ◽  
Generated Regressors ◽  
Two Step Estimation

This paper studies estimation and inference for linear quantile regression models with generated regressors. We suggest a practical two-step estimation procedure, where the generated regressors are computed in the first step. The asymptotic properties of the two-step estimator, namely, consistency and asymptotic normality are established. We show that the asymptotic variance-covariance matrix needs to be adjusted to account for the first-step estimation error. We propose a general estimator for the asymptotic variance-covariance, establish its consistency, and develop testing procedures for linear hypotheses in these models. Monte Carlo simulations to evaluate the finite-sample performance of the estimation and inference procedures are provided. Finally, we apply the proposed methods to study Engel curves for various commodities using data from the UK Family Expenditure Survey. We document strong heterogeneity in the estimated Engel curves along the conditional distribution of the budget share of each commodity. The empirical application also emphasizes that correctly estimating confidence intervals for the estimated Engel curves by the proposed estimator is of importance for inference.

NOISY HIGH FREQUENCY DATA-BASED ESTIMATION OF VOLATILITY FUNCTION WITH APPLICATIONS

2020 ◽  
pp. 1-24
Author(s):  
JINGUAN LIN ◽  
XUGUO YE ◽  
YANYONG ZHAO ◽  
HONGXIA HAO
Keyword(s):  
Stochastic Dynamics ◽  
Stock Exchange ◽  
Asymptotic Properties ◽  
Microstructure Noise ◽  
Finite Sample ◽  
Microstructure Effects ◽  
Intraday Data ◽  
Volatility Function ◽  
The Impact ◽  
Exchange Data

Diffusion models have been widely used to describe the stochastic dynamics of the underlying economic variables. Renò ( 2008 ) introduced a nonparametric estimator of the volatility function, which is based on the estimation of quadratic variation between observations by means of realized variance. However, they may be misleading when one uses intraday data to implement directly the estimator, because intraday data display microstructure effects that could seriously distort the estimation. To filter out the impact of microstructure noise on the estimation of the volatility function, in this paper we propose an improved estimator when there is microstructure noise in the observed price. Also, we show that the proposed estimator has the same asymptotic properties as the Renò estimator when the step of discretization inclines to zero. Some simulations and empirical applications on Shanghai Stock Exchange data from March 3, 2002 to December 31, 2008 are used to illustrate the finite sample performance of the proposed estimator.

scholarly journals Mean Empirical Likelihood Inference for Response Mean with Data Missing at Random

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Hanji He ◽  
Guangming Deng
Keyword(s):  
Empirical Likelihood ◽  
Missing At Random ◽  
Confidence Regions ◽  
Likelihood Inference ◽  
High Dimensional ◽  
Finite Sample ◽  
The Mean ◽  
Data Missing ◽  
Consistency And Asymptotic Normality ◽  
The Impact

We extend the mean empirical likelihood inference for response mean with data missing at random. The empirical likelihood ratio confidence regions are poor when the response is missing at random, especially when the covariate is high-dimensional and the sample size is small. Hence, we develop three bias-corrected mean empirical likelihood approaches to obtain efficient inference for response mean. As to three bias-corrected estimating equations, we get a new set by producing a pairwise-mean dataset. The method can increase the size of the sample for estimation and reduce the impact of the dimensional curse. Consistency and asymptotic normality of the maximum mean empirical likelihood estimators are established. The finite sample performance of the proposed estimators is presented through simulation, and an application to the Boston Housing dataset is shown.

scholarly journals Impact of rs2414096 polymorphism of CYP19 gene on susceptibility of polycystic ovary syndrome and hyperandrogenism in Kashmiri women

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sairish Ashraf ◽  
Shayaq Ul Abeer Rasool ◽  
Mudasar Nabi ◽  
Mohd Ashraf Ganie ◽  
Shariq R. Masoodi ◽  
...  
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Polycystic Ovary ◽  
Clinical Manifestations ◽  
Additive Model ◽  
Genotypic Frequency ◽  
Ovary Syndrome ◽  
Significant Difference ◽  
Cyp19 Gene ◽  
Reproductive Endocrine ◽  
The Impact

AbstractPolycystic ovary syndrome (PCOS) is the most common reproductive endocrine disorder in pre-menopausal women having complex pathophysiology. Several candidate genes have been shown to have association with PCOS. CYP19 gene encodes a key steroidogenic enzyme involved in conversion of androgens into estrogens. Previous studies have reported contradictory results with regard to association of SNP rs2414096 in CYP19 gene with PCOS and hyperandrogenism in different ethnic populations. Present study was aimed to investigate the impact of SNP rs2414096 polymorphism of CYP19 gene on susceptibility of PCOS and hyperandrogenism in Kashmiri women. Further we also studied the genotypic-phenotypic association for various clinical and biochemical parameters of this polymorphism. Case control study. 394 PCOS cases diagnosed on the basis of Rotterdam criteria and age matched 306 healthy women. We found a significant differences in genotypic frequency (χ2 = 18.91, p < 0.05) as well as allele frequency (OR 0.63, CI 0.51–0.78, χ2 = 17.66, p < 0.05) between PCOS women and controls. The genotype–phenotype correlation analysis showed a significant difference in FG score (p = 0.047) and alopecia (p = 0.045) between the three genotypes. Also, the androgen excess markers like DHEAS (p < 0.001), Androstenedione (p < 0.001), Testosterone (p < 0.001) and FAI (p = 0.005) were significantly elevated in GG genotype and showed a significant difference in additive model in PCOS women. rs2414096 polymorphism of CYP19 gene is associated with the risk of PCOS as well as with clinical and biochemical markers of hyperandrogenism, hence suggesting its role in clinical manifestations of PCOS in Kashmiri women.

Multivariate linear time series models

1984 ◽  
Vol 16 (3) ◽  
pp. 492-561 ◽  
Author(s):  
E. J. Hannan ◽  
L. Kavalieris
Keyword(s):  
Limit Theorems ◽  
Analysis Of Algorithms ◽  
Linear Time ◽  
Asymptotic Properties ◽  
Real Data ◽  
Rates Of Convergence ◽  
Structure Theory ◽  
Time Series Models ◽  
Transfer Model ◽  
Rational Transfer

This paper is in three parts. The first deals with the algebraic and topological structure of spaces of rational transfer function linear systems—ARMAX systems, as they have been called. This structure theory is dominated by the concept of a space of systems of order, or McMillan degree, n, because of the fact that this space, M(n), can be realised as a kind of high-dimensional algebraic surface of dimension n(2s + m) where s and m are the numbers of outputs and inputs. In principle, therefore, the fitting of a rational transfer model to data can be considered as the problem of determining n and then the appropriate element of M(n). However, the fact that M(n) appears to need a large number of coordinate neighbourhoods to cover it complicates the task. The problems associated with this program, as well as theory necessary for the analysis of algorithms to carry out aspects of the program, are also discussed in this first part of the paper, Sections 1 and 2.The second part, Sections 3 and 4, deals with algorithms to carry out the fitting of a model and exhibits these algorithms through simulations and the analysis of real data.The third part of the paper discusses the asymptotic properties of the algorithm. These properties depend on uniform rates of convergence being established for covariances up to some lag increasing indefinitely with the length of record, T. The necessary limit theorems and the analysis of the algorithms are given in Section 5. Many of these results are of interest independent of the algorithms being studied.

Evaluation of the daily cycle in the simulation of the ClimEx large-ensemble

2021 ◽  
Author(s):  
Anne-Marie Begin
Keyword(s):  
Climate Change ◽  
Estimation Error ◽  
Natural Variability ◽  
Extreme Weather Events ◽  
Large Ensemble ◽  
Daily Cycle ◽  
Minimum Number ◽  
The Mean ◽  
Mean Cycle ◽  
The Impact

&lt;p&gt;To estimate the impact of climate change on our society we need to use climate projections based on numerical models. These models make it possible to assess the effects on climate of the increase in greenhouse gases (GHG) as well as natural variability. We know that the global average temperature will increase and that the occurrence, intensity and spatio-temporal distribution of extreme precipitations will change. These extreme weather events cause droughts, floods and other natural disasters that have significant consequences on our life and environment. Precipitation is a key variable in adapting to climate change.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;This study focuses on the ClimEx large ensemble, a set of 50 independent simulations created to study the effect of climate change and natural variability on the water network in Quebec. This dataset consists of simulations produced using the Canadian Regional Climate Model version 5 (CRCM5) at 12 km of resolution driven by simulations from the second generation Canadian Earth System Model (CanESM2) global model at 310 km of resolution.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;The aim of the project is to evaluate the performance of the ClimEx ensemble in simulating the daily cycle and representing extreme values.&amp;#160; To get there, 30 years of hourly time series for precipitation and 3 hourly for temperature are analyzed. The simulations are compared with the values from the simulation of CRCM5 driven by ERA-Interim reanalysis, the ERA5 reanalysis and Environment and Climate Change Canada (ECCC) stations. An evaluation of the sensitivity of different statistics to the number of members is also performed.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;The daily cycle of precipitation from ClimEx shows mainly non-significant correlations with the other datasets and its amplitude is less than the observation datas from ECCC stations. For temperature, the correlation is strong and the amplitude of the cycle is similar to observations. ClimEx provides a fairly good representation of the 95, 97, 99&lt;sup&gt;th&lt;/sup&gt; quantiles for precipitation. For temperature it represents a good distribution of quantiles but with a warm bias in southern Quebec. For precipitation hourly maximum, ClimEx shows values 10 times higher than ERA5.&amp;#160; For temperature, minimum and maximum values may exceed the ERA5 limit by up to 20&amp;#176;C. For precipitation, the minimum number of members for the estimation of the 95 and 99&lt;sup&gt;th&lt;/sup&gt;&lt;sup&gt;&lt;/sup&gt;quantiles and the mean cycle is between 15 and 50 for an estimation error of less than 5%. For the 95, 99&lt;sup&gt;th&lt;/sup&gt; quantiles of temperature, the minimum number of members is between 1 and 17 and for the mean cycle 1 to 2 members are necessary to obtain an estimation error of less than 0.5&amp;#176;C.&lt;/p&gt;

Eviction and Voter Turnout: The Political Consequences of Housing Instability

2021 ◽  
pp. 003232922110507
Author(s):  
Gillian Slee ◽  
Matthew Desmond
Keyword(s):  
Voter Turnout ◽  
Causal Effect ◽  
Additive Model ◽  
The United States ◽  
Data Set ◽  
Endogeneity Bias ◽  
Political Consequences ◽  
Housing Costs ◽  
Election Results ◽  
The Impact

In recent years, housing costs have outpaced incomes in the United States, resulting in millions of eviction filings each year. Yet no study has examined the link between eviction and voting. Drawing on a novel data set that combines tens of millions of eviction and voting records, this article finds that residential eviction rates negatively impacted voter turnout during the 2016 presidential election. Results from a generalized additive model show eviction’s effect on voter turnout to be strongest in neighborhoods with relatively low rates of displacement. To address endogeneity bias and estimate the causal effect of eviction on voting, the analysis treats commercial evictions as an instrument for residential evictions, finding that increases in neighborhood eviction rates led to substantial declines in voter turnout. This study demonstrates that the impact of eviction reverberates far beyond housing loss, affecting democratic participation.

Functional Linear Regression

2018 ◽  
Author(s):  
Hervé Cardot ◽  
Pascal Sarda
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Linear Models ◽  
Estimation Error ◽  
Asymptotic Properties ◽  
Principal Component Regression ◽  
Principal Component ◽  
Penalized Least Squares ◽  
Open Problems ◽  
Functional Linear Regression

This article presents a selected bibliography on functional linear regression (FLR) and highlights the key contributions from both applied and theoretical points of view. It first defines FLR in the case of a scalar response and shows how its modelization can also be extended to the case of a functional response. It then considers two kinds of estimation procedures for this slope parameter: projection-based estimators in which regularization is performed through dimension reduction, such as functional principal component regression, and penalized least squares estimators that take into account a penalized least squares minimization problem. The article proceeds by discussing the main asymptotic properties separating results on mean square prediction error and results on L2 estimation error. It also describes some related models, including generalized functional linear models and FLR on quantiles, and concludes with a complementary bibliography and some open problems.

Densities for stationary random sets and point processes

1984 ◽  
Vol 16 (02) ◽  
pp. 324-346 ◽  
Author(s):  
Wolfgang Weil ◽  
John A. Wieacker
Keyword(s):  
Poisson Process ◽  
Point Processes ◽  
Convex Set ◽  
Compact Convex ◽  
Random Sets ◽  
Intensity Measure ◽  
Additive Functionals ◽  
Compact Convex Set ◽  
Classical Integral

For certain stationary random setsX, densitiesDφ(X) of additive functionalsφare defined and formulas forare derived whenKis a compact convex set in. In particular, for the quermassintegrals and motioninvariantX, these formulas are in analogy with classical integral geometric formulas. The case whereXis the union set of a Poisson processYof convex particles is considered separately. Here, formulas involving the intensity measure ofYare obtained.

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