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近单位根动态面板中的 Anderson–Hsiao 估计AI 解读

Limit theory is developed for the dynamic panel IV estimator in the presence of an autoregressive root near unity. In the unit root case, Anderson–Hsiao lagged variable instruments satisfy orthogonality conditions but are well known to be irrelevant. For a fixed time series sample size ( T ) IV is inconsistent and approaches a shifted Cauchy-distributed random variate as the cross-section sample size n → ∞. But when T → ∞, either for fixed n or as n → ∞, IV is $\sqrt T$ consi…

针对伪长记忆的多变量检验AI 解读

This paper provides a multivariate score-type test against spurious long memory. We prove the consistency of the test against the alternatives of random level shifts and smooth trends. The test statistic is based on the weighted sum of the partial derivatives of the multivariate local Whittle likelihood function. To apply the test to fractionally cointegrated series, the test statistic is calculated for the linearly transformed system after estimating the cointegrating…

含一般确定性项的协整向量自回归模型AI 解读

In the cointegrated vector autoregression (CVAR) literature, deterministic terms have until now been analyzed on a case-by-case, or as-needed basis. We give a comprehensive unified treatment of deterministic terms in the additive model Xt=γZt+Yt, where Zt belongs to a large class of deterministic regressors and Yt is a zero-mean CVAR. We suggest an extended model that can be estimated by reduced rank regression, and give a condition for when the additive and extended models…

自适应学习回归模型中结构参数的估计AI 解读

This paper examines the ordinary least squares (OLS) estimator of the structural parameters in a simple macroeconomic model in which agents are boundedly rational and use an adaptive learning rule to form expectations of the endogenous variable. The popularity of learning models has recently increased amongst applied economists and policy makers who seek to estimate them empirically. Yet the econometrics of learning models is largely uncharted territory. We consider two…

ZD-GARCH 模型：研究异方差的新方法AI 解读

This paper proposes a first-order zero-drift GARCH (ZD-GARCH(1, 1)) model to study conditional heteroscedasticity and heteroscedasticity together. Unlike the classical GARCH model, the ZD-GARCH(1, 1) model is always non-stationary regardless of the sign of the Lyapunov exponent γ0, but interestingly it is stable with its sample path oscillating randomly between zero and infinity over time when γ0=0. Furthermore, this paper studies the generalized quasi-maximum likelihood…

通过网络连接产生的长记忆AI 解读

Many time series exhibit “long memory”: Their autocorrelation function decays slowly with lag. This behavior has traditionally been modeled via unit roots or fractional Brownian motion and explained via aggregation of heterogeneous processes, nonlinearity, learning dynamics, regime switching, or structural breaks. This paper identifies a different and complementary mechanism for long‐memory generation by showing that it can naturally arise when a large number of simple…

大维VAR协整检验的替代渐近分析AI 解读

Johansen's (1988,1991) likelihood ratio test for cointegration rank of a vector autoregression (VAR) depends only on the squared sample canonical correlations between current changes and past levels of a simple transformation of the data. We study the asymptotic behavior of the empirical distribution of those squared canonical correlations when the number of observations and the dimensionality of the VAR diverge to infinity simultaneously and proportionally. We find that the…

长期共同变动性AI 解读

We develop inference methods about long‐run comovement of two time series. The parameters of interest are defined in terms of population second moments of low‐frequency transformations (“low‐pass” filtered versions) of the data. We numerically determine confidence sets that control coverage over a wide range of potential bivariate persistence patterns, which include arbitrary linear combinations of I(0), I(1), near unit roots, and fractionally integrated processes. In an…

自助法迭代的离散模型AI 解读

The bootstrap can be validated by considering the sequence of P values obtained by bootstrap iteration, rather than asymptotically. If this sequence converges to a random variable with the uniform U(0,1) distribution, the bootstrap is valid. Here, the model is made discrete and finite, characterised by a three-dimensional array of probabilities. This renders bootstrap iteration to any desired order feasible. A unit-root test for a process driven by a stationary MA(1) process…

结构非参数协整回归在最大定义域上的一致收敛速率AI 解读

This paper presents uniform convergence rates for kernel regression estimators, in the setting of a structural nonlinear cointegrating regression model. We generalise the existing literature in three ways. First, the domain to which these rates apply is much wider than the domains that have been considered in the existing literature, and can be chosen so as to contain as large a fraction of the sample as desired in the limit. Second, our results allow the regression…

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…

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…

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…

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…

利用异质性扩展的双层随机前沿估计劳动力进入者与退出者AI 解读

In a seminal paper, Basmann (1985) introduced a serial correlation structure based on an intertemporal adjustment mechanism. Basmann’s 1985 paper of course was built on his previous pioneering work on estimation and identifiability in structural equations leading to 2SLS (Basmann, 1957, 1960). In this paper, we follow a similar path. We derive a non-standard unit root serial correlation formulation for intertemporal adjustments in the labor force participation rate. This…

利用非因果过程建模局部爆炸AI 解读

Summary The non-causal auto-regressive process with heavy-tailed errors has non-linear causal dynamics, which allow for local explosion or asymmetric cycles that are often observed in economic and financial time series. It provides a new model for multiple local explosions in a strictly stationary framework. The causal predictive distribution displays surprising features, such as higher moments than for the marginal distribution, or the presence of a unit root in the Cauchy…

含高阶单整变量的联立协整方程系统识别条件AI 解读

This paper discusses identification of systems of simultaneous cointegrating equations with integrated variables of order two or higher, under constraints on the cointegration parameters. Rank and order conditions for identification are provided for general linear constraints, covering both cross-equation and equation-by-equation restrictions.

离散观测下有限参数连续时间模型的识别限制AI 解读

This paper revisits the question of parameter identification when a linear continuous time model is sampled only at equispaced points in time. Following the framework and assumptions of Phillips (1973), we consider models characterized by first-order, linear systems of stochastic differential equations and use a priori restrictions on the model parameters as identifying restrictions. A practical rank condition is derived to test whether any particular collection of at least $\left\lfloor {n/2} \right\rfloor$…

一类新的二元门限协整模型AI 解读

In this article, we introduce a new class of bivariate threshold VAR cointegration models. In the models, outside a compact region, the processes are cointegrated, while in the compact region, we allow different kinds of possibilities. We show that the bivariate processes form a 1/2-null recurrent system. We also find that the convergence rate for the estimators for the coefficients in the outside regime is T$\sqrt{T}$, while the convergence rate for the estimators for the…

低频污染下分数协整的中频带最小二乘估计AI 解读

This paper introduces a new estimator of the fractional cointegrating vector between stationary long memory processes that is robust to low-frequency contamination such as random level shifts, outliers, Markov switching means, and certain deterministic trends. In particular, the proposed medium band least squares (MBLS) estimator uses sample-size-dependent trimming of frequencies in the vicinity of the origin to account for such contamination. Consistency and asymptotic…

一般预测变量下的面板可预测性检验AI 解读

Summary The difficulty of predicting returns has recently motivated researchers to start looking for tests that are either more powerful or robust to more features of the data. Unfortunately, the way that these tests work typically involves trading robustness for power or vice versa. The current paper takes this as its starting point to develop a new panel‐based approach to predictability that is both robust and powerful. Specifically, while the panel route to increased…

含固定效应与横截面依赖的分数阶单整面板估计AI 解读

We consider a large N,T heterogeneous panel data model with fixed effects, common factors allowing for cross-section dependence, and persistent data and errors, which are assumed fractionally integrated. We propose individual and common-correlation estimates for the slope parameters while error memory parameters are estimated from regression residuals. The individual parameter estimates are all T consistent, asymptotically normal and mutually uncorrelated, irrespective of…

协整模型中平滑结构变化的估计AI 解读

This paper studies nonlinear cointegration models in which the structural coefficients may evolve smoothly over time, and considers time-varying coefficient functions estimated by nonparametric kernel methods. It is shown that the usual asymptotic methods of kernel estimation completely break down in this setting when the functional coefficients are multivariate. The reason for this breakdown is a kernel-induced degeneracy in the weighted signal matrix associated with the…