Simulation Estimation of Dynamic Panel Discrete Choice Models Using the t Distributions

In this paper a practical robust simulation estimator is proposed for the dynamic panel data discrete choice models using the $t$ distribution. The maximum simulated likelihood estimators are obtained through a recursive algorithm formulated by Geweke–Hajivassiliou–Keane simulators. Monte Carlo experiments indicate that the proposed robust simulation estimators perform well under the errors with longer than normal tails for a small simulation size, even with the initial conditions problem.     

A robust forward weighted Lagrange multiplier test for conditional heteroscedasticity

Statistical tests routinely adopted for detecting nonlinear components in time series rely on the auxiliary regression of ARMA lagged residuals, and the Lagrange multiplier test to detect ARCH components is an example. The size distortion of such test suggests adopting a weighted test, where the weights are computed through a forward search algorithm. Simulations show that the forward weighted robust test is preferable to the classical Lagrange test and to existing robust tests, which are based on backward weighted regression or on estimated autocorrelation function. The forward weighted robust test is applied to daily financial and quarterly macroeconomic time series, showing its usefulness in detecting ARCH effects, even when outliers are present.     

销售电价与用电需求的面板数据协整建模

提出基于面板数据协整的销售电价与用电需求的关系分析方法,该方法同时利用时间和截面数据,改善小样本问题,解决变量内生性和序列相关所导致的伪回归问题,从而准确描述不同地区不同时间上销售电价和用电需求之间的协整关系。通过对1990到2005年美国各州销售电价与用电需求的面板单位根检验和面板协整检验,发现销售电价和用电需求是非平稳的面板数据,并且它们之间存在着显著的面板协整关系。在此基础上,提出基于截面加权回归的固定效应模型,用以准确描述销售电价和用电需求面板数据的长期均衡关系。     

Testing for Unit Roots in Panel Data Using a Wavelet Ratio Method

For testing unit root in single time series, most tests concentrate on the time domain. Recently, Fan and Gençay (Econom Theory 26:1305–1331, 2010) proposed a wavelet ratio test which took advantage of the information from the frequency domain by using a wavelet spectrum methodology. This test shows a better power than many time domain based unit root tests including the Dickey–Fuller (J Am Stat Assoc 74:427–431, 1979) type of test in the univariate time series case. On the other hand, various unit root tests in multivariate time series have appeared since the pioneering work of Levin and Lin (Unit root test in panel data: new results, University of California at San Diego, Discussion Paper, 1993). Among them, the Im–Pesaran–Shin (IPS) (J Econ 115(1):53–74, 1997) test is widely used for its straightforward implementation and robustness to heterogeneity. The IPS test is a group mean test which uses the average of the test statistics for each single series. As the test statistics in each series can be flexible, this paper will apply the wavelet ratio statistic to give a comparison with the test by using Dickey–Fuller t statistic in the single series. Simulation results show a gain in power by employing the wavelet ratio test instead of the Dickey–Fuller t statistic in the panel data case. As the IPS test is sensitive to cross sectional dependence, we further compare the robustness of both test statistics when there exists cross correctional dependence among the units in the panel data. Finally we apply a residual based wavestrapping methodology to reduce the over biased size problem brought up by the cross correlation for both test statistics.     

Measuring similarity of individuals in description logics over the refinement space of conjunctive queries

Sensor networks, communication and financial networks, web and social networks are becoming increasingly important in our day-to-day life. They contain entities which may interact with one another. These interactions are often characterized by a form of autocorrelation, where the value of an attribute at a given entity depends on the values at the entities it is interacting with. In this situation, the collective inference paradigm offers a unique opportunity to improve the performance of predictive models on network data, as interacting instances are labeled simultaneously by dealing with autocorrelation. Several recent works have shown that collective inference is a powerful paradigm, but it is mainly developed with a fully-labeled training network. In contrast, while it may be cheap to acquire the network topology, it may be costly to acquire node labels for training. In this paper, we examine how to explicitly consider autocorrelation when performing regression inference within network data. In particular, we study the transduction of collective regression when a sparsely labeled network is a common situation. We present an algorithm, called CORENA (COllective REgression in Network dAta), to assign a numeric label to each instance in the network. In particular, we iteratively augment the representation of each instance with instances sharing correlated representations across the network. In this way, the proposed learning model is able to capture autocorrelations of labels over a group of related instances and feed-back the more reliable labels predicted by the transduction in the labeled network. Empirical studies demonstrate that the proposed approach can boost regression performances in several spatial and social tasks.     

The enemy within: Autocorrelation bias in content analysis of narratives

Many content analysis studies involving temporal data are biased by some unknown dose of autocorrelation. The effect of autocorrelation is to inflate or deflate the significant differences that may exist among the different parts of texts being compared. The solution consists in removing effects due to autocorrelation, even if the latter is not statistically significant. Procedures such as Crosbie's (1993) ITSACORR remove the effect of at least first-order autocorrelations and can be used with small samples. The AREG procedure of SPSS (1994) and the AUTOREG procedure of SAS (1993) can be employed to detect and remove first-order autocorrelations, and higher-order ones too in the case of AUTOREG, while several methods specifically intended for small samples (Huitema and McKean, 1991, 1994) have been developed. Four examples of content analysis studies with and without autocorrelation are discussed.     

A New Approach to Unit Root Testing

A novel simulation based approach to unit root testing is proposed in this paper. The test is constructed from the distinct orders in probability of the OLS parameter estimates obtained from a spurious and an unbalanced regression, respectively. While the parameter estimate from a regression of two integrated and uncorrelated time series is of order O _p (1), the estimate is of order O _p (T ⁻¹) if the dependent variable is stationary. The test statistic is constructed as an interquantile range from the empirical distribution obtained from regressing the standardized data sufficiently often on controlled random walks. GLS detrending (Elliott et al., Econometrica 64(4):813–836, 1996) and spectral density variance estimators (Perron and Ng, Econom Theory 14(5):560–603, 1998) are applied to account for deterministic terms and residual autocorrelation in the data. A Monte Carlo study confirms that the proposed test has favorable empirical size properties and is powerful in local-to-unity neighborhoods. As an empirical illustration, we test the purchasing power parity hypothesis for a sample of G7 economies.     

基于小波变换多维时间序列最新变化点检测

现代数据科学中存在大量的多维时间序列数据,检测多维时间序列中的最新变化点对于短期预测很重要。一种改进的方法被提出,以检测此类多维时间序列数据中最新变化点。通过使用小波变换,将多维时间序列中的变化点检测问题转化为相对较容易的多维面板数据中的变化点检测问题。该方法旨在跨时间序列合并信息,以便优先推断多个序列中同一时间点的最...     

Analyzing survival curves at a fixed point in time for paired and clustered right-censored data

In clinical trials, information about certain time points may be of interest in making decisions about treatment effectiveness. Therefore, rather than comparing entire survival curves, researchers may wish to focus the comparison on fixed time points with potential clinical utility. For two independent samples of right-censored data, Klein et al. (2007) compared survival probabilities at a fixed time point by studying a number of tests based on transformations of the Kaplan-Meier estimators of the survival function. To compare the survival probabilities at a fixed time point for paired right-censored data or clustered right-censored data, however, their approach requires modification. In this paper, we extend the statistics to accommodate possible within-pair and within-cluster correlation. We use simulation studies to present comparative results. Finally, we illustrate the implementation of these methods using two real data sets.     

Testing the martingale difference hypothesis using integrated regression functions

An omnibus test for testing a generalized version of the martingale difference hypothesis (MDH) is proposed. This generalized hypothesis includes the usual MDH, testing for conditional moments constancy such as conditional homoscedasticity (ARCH effects) or testing for directional predictability. A unified approach for dealing with all of these testing problems is proposed. These hypotheses are long standing problems in econometric time series analysis, and typically have been tested using the sample autocorrelations or in the spectral domain using the periodogram. Since these hypotheses cover also nonlinear predictability, tests based on those second order statistics are inconsistent against uncorrelated processes in the alternative hypothesis. In order to circumvent this problem pairwise integrated regression functions are introduced as measures of linear and nonlinear dependence. The proposed test does not require to chose a lag order depending on sample size, to smooth the data or to formulate a parametric alternative model. Moreover, the test is robust to higher order dependence, in particular to conditional heteroskedasticity. Under general dependence the asymptotic null distribution depends on the data generating process, so a bootstrap procedure is considered and a Monte Carlo study examines its finite sample performance. Then, the martingale and conditional heteroskedasticity properties of the Pound/Dollar exchange rate are investigated.     

Semiparametric regression analysis of panel count data with informative observation times

This paper discusses regression analysis of panel count data that arise naturally when recurrent events are considered. For the analysis of panel count data, most of the existing methods have assumed that observation times are completely independent of recurrent events or given covariates, which may not be true in practice. We propose a joint modeling approach that uses an unobserved random variable and a completely unspecified link function to characterize the correlations between the response variable and the observation times. For inference about regression parameters, estimating equation approaches are developed without involving any estimation for latent variables, and the asymptotic properties of the resulting estimators are established. In addition, a technique is provided for assessing the adequacy of the model. The performance of the proposed estimation procedures are evaluated by means of Monte Carlo simulations, and a data set from a bladder tumor study is analyzed as an illustrative example.     

A robust sign test for panel unit roots under cross sectional dependence

A robust sign test is proposed for testing unit roots in cross-sectionally dependent panel data. Large sample Gaussian null asymptotics of the test are established under (fixed N, large T) and, for serially uncorrelated error cases, under (large N, fixed T), where N is the number of panel units and T is the length of time span. The limiting null distribution is valid, even if the error processes are subject to any type of conditional heteroscedasticity. A Monte-Carlo experiment reveals that, compared with other existing tests, the proposed test has a very stable size property for wider classes of error distributions, type of conditional heteroscedasticities, type of cross-sectional correlations, and values of (N,T) while having reasonable power. Especially, for small T like T=5,10,20, the proposed test shows much stabler size performance than other existing tests. The unemployment rates of the 51 states of the USA are analyzed by the proposed method, which reveals some evidence for unit roots in the presence of factor and spatial cross-section correlation.     

The Size and Power of the Bias-Corrected Bootstrap Test for Regression Models with Autocorrelated Errors

This paper is concerned with statistical inference for the coefficient of the linear regression model when the error term follows an autoregressive (AR) model. Past studies have reported severe size distortions, when the data are trending and autocorrelation of the error term is high. In this paper, we consider a test based on the bias-corrected bootstrap, where bias-corrected parameter estimators for the AR and regression coefficients are used. For bias-correction, the jackknife and bootstrap methods are employed. Monte Carlo simulations are conducted to compare size and power properties of the bias-corrected bootstrap test. It is found that the bias-corrected bootstrap test shows substantially improved size properties and exhibits excellent power for most of cases considered. It also appears that bootstrap bias-correction leads to better size and higher power values than jackknife bias-correction. These results are found to be robust to the choice of parameter estimation methods.JEL classifications: C12, C15, C63     

Modeling interregional commodity flows with incorporating network autocorrelation in spatial interaction models: An application of the US interstate commodity flows

Spatial interaction models are frequently used to predict and explain interregional commodity flows. Studies suggest that the effects of spatial structure significantly influence spatial interaction models, often resulting in model misspecification. Competing destinations and intervening opportunities have been used to mitigate this issue. Some recent studies also show that the effects of spatial structure can be successfully modeled by incorporating network autocorrelation among flow data. The purpose of this paper is to investigate the existence of network autocorrelation among commodity origin–destination flow data and its effect on model estimation in spatial interaction models. This approach is demonstrated using commodity origin–destination flow data for 111 regions of the United States from the 2002 Commodity Flow Survey. The results empirically show how network autocorrelation affects modeling interregional flows and can be successfully captured in spatial autoregressive model specifications.     

A Bayesian analysis of an agricultural field trial with three spatial dimensions

Modern technology now has the ability to generate large datasets over space and time. Such data typically exhibit high autocorrelations over all dimensions. The field trial data motivating the methods of this paper were collected to examine the behaviour of traditional cropping and to determine a cropping system which could maximise water use for grain production while minimising leakage below the crop root zone. They consist of moisture measurements made at 15 depths across 3 rows and 18 columns, in the lattice framework of an agricultural field.Bayesian conditional autoregressive (CAR) models are used to account for local site correlations. Conditional autoregressive models have not been widely used in analyses of agricultural data. This paper serves to illustrate the usefulness of these models in this field, along with the ease of implementation in WinBUGS, a freely available software package.The innovation is the fitting of separate conditional autoregressive models for each depth layer, the ‘layered CAR model’, while simultaneously estimating depth profile functions for each site treatment. Modelling interest also lies in how best to model the treatment effect depth profiles, and in the choice of neighbourhood structure for the spatial autocorrelation model. The favoured model fitted the treatment effects as splines over depth, and treated depth, the basis for the regression model, as measured with error, while fitting CAR neighbourhood models by depth layer. It is hierarchical, with separate conditional autoregressive spatial variance components at each depth, and the fixed terms which involve an errors-in-measurement model treat depth errors as interval-censored measurement error. The Bayesian framework permits transparent specification and easy comparison of the various complex models compared.     

基于神经网络的时间序列鲁棒预测

为了实现神经网络预测模型的便棒预测,提出一种基于非线性偏自相关的一般化预测模型辨识方法。该方法通过考察待预测时间序列的当前序列值对各阶历史序列的不可约自依赖,确定神经网络这类非线性自回归模型的自回归阶数。实现表明,该方法可有效地提高神经网络预测模型的鲁棒预测能力。     

Statistical prediction of air pollution levels using non-physical models

This paper treats the prediction problem of air pollution levels at a short range by non-physical models. Main results are given as follows: (i) The prediction accuracy of the pollution levels by time series models is compared by evaluating three performance indices, and it is shown that the multiple linear regression model already proposed is better than the auto-regressive model, the Box-Jenkins' model and the persistence model. (ii) The multiple linear regression model is more improved if the model is classified by weather. (iii) The modeling accuracy is discussed for various sample sizes, and an appropriate sample size is determined from the experiment. (iv) The confidence intervals of the predicted means at a fixed time are calculated, and the combinations of the measurement times and the measured factors that improve the prediction accuracy are chosen. (v) A revised GMDH is proposed and the accuracy by this method is more improved than those by the time series models already presented. (vi) The Kalman filtering method is applied to the prediction of pollution levels, and the measured factors that improve the prediction accuracy are chosen.     

Autocorrelation Descriptor for Efficient Co‐Alignment of 3D Shape Collections

Co‐aligning a collection of shapes to a consistent pose is a common problem in shape analysis with applications in shape matching, retrieval and visualization. We observe that resolving among some orientations is easier than others, for example, a common mistake for bicycles is to align front‐to‐back, while even the simplest algorithm would not erroneously pick orthogonal alignment. The key idea of our work is to analyse rotational autocorrelations of shapes to facilitate shape co‐alignment. In particular, we use such an autocorrelation measure of individual shapes to decide which shape pairs might have well‐matching orientations; and, if so, which configurations are likely to produce better alignments. This significantly prunes the number of alignments to be examined, and leads to an efficient, scalable algorithm that performs comparably to state‐of‐the‐art techniques on benchmark data sets, but requires significantly fewer computations, resulting in 2–16× speed improvement in our tests.     

Population shift bias in tests of space–time interaction

Common to all tests of space–time interaction is the assumption that the population underlying the events of interest exhibits a trajectory of growth that is consistent through time and across space. In practice, however, this assumption is often untenable and, when violated, can introduce population shift bias into the results of these tests. While this problem is widely recognized, more work remains to compare its effect across tests and to determine the extent to which it is a problem for study short periods. This paper quantifies and compares the population shift bias present in the results of the Knox, Mantel, and Jacquez tests of space–time interaction. A simulation study is carried out which quantifies the bias present in each test across a variety of population movement scenarios. Results show a positive relationship between population shift bias and the heterogeneity in population growth across all the tests. They also demonstrate variability in the size of the bias across the three tests for space–time interaction considered. Finally, the results illustrate that population shift bias can be a serious problem for short study periods. Collectively, these findings suggest that an unbiased approach to assessing the significance of space–time interaction test results is needed whenever spatially heterogeneous population change is identified within a study area.     

Likelihood analysis of the multivariate ordinal probit regression model for repeated ordinal responses

We consider the analysis of longitudinal ordinal data, meaning regression-like analysis when the response variable is categorical with ordered categories, and is measured repeatedly over time (or space) on the experimental or sampling units. Particular attention is given to the multivariate ordinal probit regression model, in which the correlation between ordered categorical responses on the same unit at different times (or locations) is modeled with a latent variable that has a multivariate normal distribution. An algorithm for maximum likelihood analysis of this model is proposed and the analysis is demonstrated on an example. Simulations clarify the extent to which maximum likelihood estimators can be more efficient than generalized estimating equations (GEE) estimators of regression coefficients and the extent to which likelihood ratio tests can be more accurate than tests based on standard errors and approximate normality of GEE estimators.