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时变 ARCH 模型中的参数稳定性与半参数推断AI 解读

Summary We develop a complete methodology for detecting time varying or non-time-varying parameters in auto-regressive conditional heteroscedasticity (ARCH) processes. For this, we estimate and test various semiparametric versions of time varying ARCH models which include two well-known non-stationary ARCH-type models introduced in the econometrics literature. Using kernel estimation, we show that non-time-varying parameters can be estimated at the usual parametric rate of…

时变系数贝叶斯VARMA模型的高效估计AI 解读

Summary Empirical work in macroeconometrics has been mostly restricted to using vector autoregressions (VARs), even though there are strong theoretical reasons to consider general vector autoregressive moving averages (VARMAs). A number of articles in the last two decades have conjectured that this is because estimation of VARMAs is perceived to be challenging and proposed various ways to simplify it. Nevertheless, VARMAs continue to be largely dominated by VARs,…

国际经济周期的内生聚类因子方法AI 解读

Summary Factor models have become useful tools for studying international business cycles. Block factor models can be especially useful as the zero restrictions on the loadings of some factors may provide some economic interpretation of the factors. These models, however, require the econometrician to predefine the blocks, leading to potential misspecification. In Monte Carlo experiments, we show that even a small misspecification can lead to substantial declines in fit. We…

潜在面板结构中的组数确定：收入与民主的应用AI 解读

We consider a latent group panel structure as recently studied by Su, Shi, and Phillips (2016), where the number of groups is unknown and has to be determined empirically. We propose a testing procedure to determine the number of groups. Our test is a residual-based Lagrange multiplier-type test. We show that after being appropriately standardized, our test is asymptotically normally distributed under the null hypothesis of a given number of groups and has the power to…

重复观测多元时间序列的条件谱分析：夜间生理应用AI 解读

This article considers the problem of analyzing associations between power spectra of multiple time series and cross-sectional outcomes when data are observed from multiple subjects. The motivating application comes from sleep medicine, where researchers are able to non-invasively record physiological time series signals during sleep. The frequency patterns of these signals, which can be quantified through the power spectrum, contain interpretable information about…

迭代辅助粒子滤波器AI 解读

We present an offline, iterated particle filter to facilitate statistical inference in general state space hidden Markov models. Given a model and a sequence of observations, the associated marginal likelihood L is central to likelihood-based inference for unknown statistical parameters. We define a class of “twisted” models: each member is specified by a sequence of positive functions ψ and has an associated ψ-auxiliary particle filter that provides unbiased estimates of L.…

多变量季节调整、经济恒等式与季节性分类AI 解读

This article extends the methodology for multivariate seasonal adjustment by exploring the statistical modeling of seasonality jointly across multiple time series, using latent dynamic factor models fitted using maximum likelihood estimation. Signal extraction methods for the series then allow us to calculate a model-based seasonal adjustment. We emphasize several facets of our analysis: (i) we quantify the efficiency gain in multivariate signal extraction versus univariate…

含条件异方差的缓冲自回归模型：汇率应用AI 解读

This article introduces a new model called the buffered autoregressive model with generalized autoregressive conditional heteroscedasticity (BAR-GARCH). The proposed model, as an extension of the BAR model in Li et al. (2015), can capture the buffering phenomena of time series in both the conditional mean and variance. Thus, it provides us a new way to study the nonlinearity of time series. Compared with the existing AR-GARCH and threshold AR-GARCH models, an application to…

对低频污染稳健的参数估计AI 解读

We provide methods to robustly estimate the parameters of stationary ergodic short-memory time series models in the potential presence of additive low-frequency contamination. The types of contamination covered include level shifts (changes in mean) and monotone or smooth time trends, both of which have been shown to bias parameter estimates toward regions of persistence in a variety of contexts. The estimators presented here minimize trimmed frequency domain quasi-maximum…

高维时间序列的高斯近似AI 解读

We consider the problem of approximating sums of high dimensional stationary time series by Gaussian vectors, using the framework of functional dependence measure. The validity of the Gaussian approximation depends on the sample size $n$, the dimension $p$, the moment condition and the dependence of the underlying processes. We also consider an estimator for long-run covariance matrices and study its convergence properties. Our results allow constructing simultaneous…

具有分块结构的超大网络稀疏估计AI 解读

Networks with a very large number of nodes appear in many application areas and pose challenges for traditional Gaussian graphical modelling approaches. In this paper, we focus on the estimation of a Gaussian graphical model when the dependence between variables has a block‐wise structure. We propose a penalized likelihood estimation of the inverse covariance matrix, also called Graphical LASSO, applied to block averages of observations, and we derive its asymptotic…

含变量测量误差动态模型的模拟最小距离估计AI 解读

Empirical analysis often involves using inexact measures of the predictors suggested by economic theory. The bias created by the correlation between the mismeasured regressors and the error term motivates the need for instrumental variable estimation. This paper considers a class of estimators that can be used in dynamic models with measurement errors when external instruments may not be available or are weak. The idea is to exploit the relation between the parameters of the…

α 混合三角阵列和的收敛速率及连续时间漂移函数估计应用AI 解读

The convergence rates of the sums of α -mixing (or strongly mixing) triangular arrays of heterogeneous random variables are derived. We pay particular attention to the case where central limit theorems may fail to hold, due to relatively strong time-series dependence and/or the nonexistence of higher-order moments. Several previous studies have presented various versions of laws of large numbers for sequences/triangular arrays, but their convergence rates were not fully…

异质变点推断AI 解读

Summary We propose, a heterogeneous simultaneous multiscale change point estimator called ‘H-SMUCE’ for the detection of multiple change points of the signal in a heterogeneous Gaussian regression model. A piecewise constant function is estimated by minimizing the number of change points over the acceptance region of a multiscale test which locally adapts to changes in the variance. The multiscale test is a combination of local likelihood ratio tests which are properly…

非因果向量自回归过程：表示、识别与半参数估计AI 解读

This paper introduces a representation theorem for a mixed VAR(p) process by distinguishing its causal and noncausal components. That representation is used to discuss the advantages and limitations of second-order identification in a mixed VAR. We show that it is possible to find the numbers of causal or noncausal components of the process from its multivariate autocovariance function, while nonlinear autocovariances are needed to distinguish between them. The paper…

非线性多元协整回归的设定检验AI 解读

This paper considers a general model specification test for nonlinear multivariate cointegrating regressions where the regressor consists of a univariate integrated time series and a vector of stationary time series. The regressors and the errors are generated from the same innovations, so that the model accommodates endogeneity. A new and simple test is proposed, and the resulting asymptotic theory is established. The test statistic is constructed based on a natural…

利用调查预期锚定收益率曲线AI 解读

Summary The dynamic behavior of the term structure of interest rates is difficult to replicate with models, and even models with a proven track record of empirical performance have underperformed since the early 2000s. On the other hand, survey expectations can accurately predict yields, but they are typically not available for all maturities and/or forecast horizons. We show how survey expectations can be exploited to improve the accuracy of yield curve forecasts given by a…

结构 FECM：大规模结构 FAVAR 模型中的协整AI 解读

Summary Starting from the dynamic factor model for nonstationary data we derive the factor‐augmented error correction model (FECM) and its moving‐average representation. The latter is used for the identification of structural shocks and their propagation mechanisms. We show how to implement classical identification schemes based on long‐run restrictions in the case of large panels. The importance of the error correction mechanism for impulse response analysis is analyzed by…

兼具时间与横截面依赖的因子模型的有效估计AI 解读

Summary This paper studies the efficient estimation of large‐dimensional factor models with both time and cross‐sectional dependence assuming ( N , T ) separability of the covariance matrix. The asymptotic distribution of the estimator of the factor and factor‐loading space under factor stationarity is derived and compared to that of the principal component (PC) estimator. The paper also considers the case when factors exhibit a unit root. We provide feasible estimators and…

识别稀疏因子模型中对因子估计有用与无用的变量AI 解读

Summary This paper considers factor estimation from heterogeneous data, where some of the variables—the relevant ones—are informative for estimating the factors, and others—the irrelevant ones—are not. We estimate the factor model within a Bayesian framework, specifying a sparse prior distribution for the factor loadings. Based on identified posterior factor loading estimates, we provide alternative methods to identify relevant and irrelevant variables. Simulations show that…

使用估计因子与特异成分的模型选择AI 解读

Summary This paper provides consistent information criteria for the selection of forecasting models that use a subset of both the idiosyncratic and common factor components of a big dataset. This hybrid model approach has been explored by recent empirical studies to relax the strictness of pure factor‐augmented model approximations, but no formal model selection procedures have been developed. The main difference to previous factor‐augmented model selection procedures is…

具有自回归与异方差扰动的动态空间自回归模型AI 解读

Summary We propose a new class of models specifically tailored for spatiotemporal data analysis. To this end, we generalize the spatial autoregressive model with autoregressive and heteroskedastic disturbances, that is, SARAR(1, 1), by exploiting the recent advancements in score‐driven (SD) models typically used in time series econometrics. In particular, we allow for time‐varying spatial autoregressive coefficients as well as time‐varying regressor coefficients and…

第二次巴勒斯坦大起义中的暴力循环：非线性 VAR 的因果分析AI 解读

Summary We contest Jaeger and Paserman's claim (Jaeger and Paserman , 2008. The cycle of violence? An empirical analysis of fatalities in the Palestinian–Israeli conflict. American Economic Review 98 (4): 1591–1604) that Palestinians did not react to Israeli aggression during Intifada 2. We address the differences between the two sides in terms of the timing and intensity of violence, estimate nonlinear vector autoregression models that are suitable when the linear vector…

贝叶斯变点检测的最优性AI 解读

By introducing suitable loss random variables of detection, we obtain optimal tests in terms of the stopping time or alarm time for Bayesian change-point detection not only for a general prior distribution of change-points but also for observations being a Markov process. Moreover, the optimal (minimal) average detection delay is proved to be equal to $1$ for any (possibly large) average run length to false alarm if the number of possible change-points is finite.