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最小修剪平方估计：不含干扰参数的渐近理论AI 解读

The Least Trimmed Squares (LTS) regression estimator is known to be very robust to the presence of “outliers”. It is based on a clear and intuitive idea: in a sample of size n , it searches for the h -subsample of observations with the smallest sum of squared residuals. The remaining $n-h$ observations are declared “outliers”. Fast algorithms for its computation exist. Nevertheless, the existing asymptotic theory for LTS, based on the traditional $\epsilon $ -contamination…

阶跃指示变量饱和法误检率与功效的渐近性质AI 解读

Detecting multiple structural breaks at unknown dates is a central challenge in time-series econometrics. Step-indicator saturation (SIS) addresses this challenge during model selection, and we develop its asymptotic theory for tuning parameter choice. We study its frequency gauge—the false detection rate—and show it is consistent and asymptotically normal. Simulations suggest that a smaller gauge minimizes bias in post-selection regression estimates. For the small gauge…

最小截尾平方估计量成为最大似然估计量的一种模型AI 解读

Abstract The least trimmed squares (LTS) estimator is a popular robust regression estimator. It finds a subsample of h ‘good’ observations among n observations and applies least squares on that subsample. We formulate a model in which this estimator is maximum likelihood. The model has ‘outliers’ of a new type, where the outlying observations are drawn from a distribution with values outside the realized range of h ‘good’, normal observations. The LTS estimator is found to…

非线性与非平稳回归的累积平方和统计量AI 解读

We show that the cumulated sum of squares statistic has a standard Brownian bridge–type asymptotic distribution in nonlinear regression models with (possibly) nonstationary regressors. This contrasts with cumulated sum statistics which have been previously studied and whose asymptotic distribution has been shown to depend on the functional form and the stochastic properties, such as persistence and stationarity, of the regressors. A recursive version of the test is also…

时间序列线性回归中 M 估计量的有界性AI 解读

We show boundedness in probability uniformly in sample size of a general M-estimator for multiple linear regression in time series. The positive criterion function for the M-estimator is assumed lower semicontinuous and sufficiently large for large argument. Particular cases are the Huber-skip and quantile regression. Boundedness requires an assumption on the frequency of small regressors. We show that this is satisfied for a variety of deterministic and stochastic…

过度离散的年龄—时期—队列模型AI 解读

We consider inference and forecasting for aggregate data organized in a two-way table with age and cohort as indices, but without measures of exposure. This is modeled using a Poisson likelihood with an age-period-cohort structure for the mean while allowing for over-dispersion. We propose a repetitive structure that keeps the dimension of the table fixed while increasing the latent exposure. For this, we use a class of infinitely divisible distributions which include a…

共爆炸向量自回归中的理性泡沫检验AI 解读

We derive the parameter restrictions that a standard equity market model implies for a bivariate vector autoregression for stock prices and dividends, and we show how to test these restrictions using likelihood ratio tests. The restrictions, which imply that stock returns are unpredictable, are derived both for a model without bubbles and for a model with a rational bubble. In both cases we show how the restrictions can be tested through standard chi-squared inference. The…

随机与确定性时间趋势下平方累积和检验的渐近行为AI 解读

We generalize the cumulative sum of squares (CUSQ) test to the case of nonstationary autoregressive distributed lag models with deterministic time trends. The test may be implemented with either ordinary least squares residuals or standardized forecast errors. In explosive cases the asymptotic theory applies more generally for the least squares residuals-based test. Preliminary simulations of the tests suggest a very modest difference between the tests and a very modest…

共爆炸过程分析AI 解读

A vector autoregressive model allowing for unit roots as well as an explosive characteristic root is developed. The Granger-Johansen representation shows that this results in processes with two common features: a random walk and an explosively growing process. Cointegrating and coexplosive vectors can be found that eliminate these common factors. The likelihood ratio test for a simple hypothesis on the coexplosive vectors is analyzed. The method is illustrated using data…

非平稳自回归过程的相关图AI 解读

Summary Analysis of time series often involves correlograms and partial correlograms as graphical descriptions of temporal dependence. Two methods are available for computing these statistics: one based on autocorrelations and the other on scaled autocovariances. For a stationary time series the resulting plots are nearly identical. When it comes to time series exhibiting non-stationary features these methods can lead to very different results. This has two consequences:…

含确定性项的一般向量自回归中最小二乘估计量的强一致性AI 解读

A vector autoregression with deterministic terms and with no restrictions to its characteristic roots is considered. Strong consistency results for the least squares statistics are presented. This extends earlier results where deterministic terms have not been considered. In addition the convergence rates are improved compared with earlier results.Comments from S. Johansen are gratefully acknowledged.

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不稳定自回归过程单位根检验的渐近分布AI 解读

UNIT ROOT TESTING has been developed through numerous papers since the work of Ž. Dickey and Fuller 1979 . The idea is to test the hypothesis that the differences of an observed time series do not depend on its levels, or in other words, the levels of the time series have a unit root that can be removed by differencing. While it is in general possible to have multiple unit roots, only the hypothesis of exactly one unit root is considered Ž. here. The available tests…

部分系统中协整秩的渐近推断AI 解读

The likelihood ratio test for cointegrating rank is analyzed for partial (or conditional) systems in the vector autoregressive error-correction model. Under the assumption of weak exogeneity for the cointegrating parameters, the asymptotic distributions are given and tables of critical values are provided. A discussion is given of some of the assumptions of the model, why they are needed, and how they are tested.