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多元随机单位根模型及其衍生品定价应用AI 解读

This paper extends recent findings of Lieberman and Phillips (2014) on stochastic unit root (STUR) models to a multivariate case including asymptotic theory for estimation of the model’s parameters. The extensions are useful for applications of STUR modeling and because they lead to a generalization of the Black–Scholes formula for derivative pricing. In place of the standard assumption that the price process follows a geometric Brownian motion, we derive a new form of the…

时变方差下协整非平稳时间序列的预测AI 解读

In cointegrated vector autoregressive (VAR) models, error correction terms often have indeterminate effects on forecasting, thus we are concerned with inclusion or exclusion of the cointegration relation in forecast. This paper considers the model averaging strategies for cointegrated VAR models with heterogeneous variance or variance breaks. The estimated cointegration rank along with other data information are used to formulate the model averaging weights. This specific…

计量应用中向随机积分的弱收敛AI 解读

Limit theory involving stochastic integrals is now widespread in time series econometrics and relies on a few key results on functional weak convergence. In establishing such convergence, the literature commonly uses martingale and semimartingale structures. While these structures have wide relevance, many applications involve a cointegration framework where endogeneity and nonlinearity play major roles and complicate the limit theory. This paper explores weak convergence…

长自回归何时能够吸收被忽略的参数变化？AI 解读

To construct forecasts for time series exhibiting breaks, the paper examines long autoregressions, where the number of lags is growing with T , and possible breaks are simply ignored. The paper shows that the OLS estimators are still elementwise consistent for the true autoregressive coefficients when neglecting a break in mean, but the sum of the estimators converges to unity. Thanks to this unit-root like behavior of the fitted model, the resulting conditional forecasts…

协整多项式回归的完全修正 OLS 估计与推断AI 解读

This paper develops a fully modified OLS (FM-OLS) estimator for cointegrating polynomial regressions, i.e., regressions that include as explanatory variables deterministic variables, integrated processes, and integer powers of integrated processes. The stationary errors are allowed to be serially correlated and the regressors are allowed to be endogenous. The paper extends the fully modified estimator of Phillips and Hansen (1990) from cointegrating regressions to…

通过典型相关确定误差修正 VARMA 模型的长期与短期动态AI 解读

Summary This article studies a simple, coherent approach for identifying and estimating error‐correcting vector autoregressive moving average (EC‐VARMA) models. Canonical correlation analysis is implemented for both determining the cointegrating rank, using a strongly consistent method, and identifying the short‐run VARMA dynamics, using the scalar component methodology. Finite‐sample performance is evaluated via Monte Carlo simulations and the approach is applied to…

PANICCA：结合横截面均值的面板非平稳性分析AI 解读

Summary The cross‐section average (CA) augmentation approach of Pesaran (A simple panel unit root test in presence of cross‐section dependence. Journal of Applied Econometrics 2007; 22 : 265–312) and Pesaran et al. (Panel unit root test in the presence of a multifactor error structure. Journal of Econometrics 2013; 175 : 94–115), and the principal components‐based panel analysis of non‐stationarity in idiosyncratic and common components (PANIC) of Bai and Ng (A PANIC attack…

含共同随机趋势的面板误差修正检验AI 解读

Summary This paper develops panel data tests for the null hypothesis of no error correction in a model with common stochastic trends. The asymptotic distributions of the new test statistics are derived and simulation results are provided to suggest that they perform well in small samples. Copyright © 2015 John Wiley & Sons, Ltd.

混频数据下连续时间模型的估计AI 解读

This paper derives exact representations for discrete time mixed frequency data generated by an underlying multivariate continuous time model. Allowance is made for different combinations of stock and flow variables as well as deterministic trends, and the variables themselves may be stationary or nonstationary (and possibly cointegrated). The resulting discrete time representations allow for the information contained in high frequency data to be utilised alongside the low…

含 I(1) 与 I(0) 变量的混合系统建模：计量方法AI 解读

Summary This paper considers structural models with both I (1) and I (0) variables. The structural shocks associated with either set of variables could be permanent or transitory. We classify the shocks as (P1,P0) and (T1,T0), where P/T distinguishes permanent/transitory, while 1/0 means they are attached to structural equations with either I (1) or I (0) variables as their ‘dependent’ variable. We show that P0 shocks can affect cointegration analysis and provide a formula…

随机系数面板模型中的统计推断AI 解读

This paper studies the asymptotics of the Weighted Least Squares (WLS) estimator of the autoregressive root in a panel Random Coefficient Autoregression (RCA). We show that, in an RCA context, there is no “unit root problem” : the WLS estimator is always asymptotically normal, irrespective of the average value of the autoregressive root, of whether the autoregressive coefficient is random or not, and of the presence and degree of cross dependence. Our simulations indicate…

混合单整与温和爆炸性回归变量下的稳健计量推断AI 解读

This paper explores in several prototypical models a convenient inference procedure for nonstationary variable regression that enables robust chi-square testing for a wide class of persistent and endogenous regressors. The approach uses the mechanism of self-generated instruments called IVX instrumentation developed by Magdalinos and Phillips (2009b). We first show that these methods remain valid for regressors with local unit roots in the explosive direction and mildly…

宏观建模中的 VARMA 识别：一种新方法AI 解读

This paper develops a new methodology for identifying the structure of VARMA time series models. The analysis proceeds by examining the echelon form and presents a fully automatic, strongly consistent, data driven approach to model specification. A novel feature of the inferential procedures developed here is that they work in terms of a canonical representation based on the Kronecker invariants in which the variables are expressed in the form of scalar dynamic structural…

未知时点可能存在趋势断点时的 VAR 协整秩检验AI 解读

In this paper we consider the problem of testing for the co-integration rank of a vector autoregressive process in the case where a trend break may potentially be present in the data. It is known that un-modelled trend breaks can result in tests which are incorrectly sized under the null hypothesis and inconsistent under the alternative hypothesis. Extant procedures in this literature have attempted to solve this inference problem but require the practitioner to either…

非平稳非参数回归中核加权样本协方差的一致收敛AI 解读

We obtain uniform consistency results for kernel-weighted sample covariances in a nonstationary multiple regression framework that allows for both fixed design and random design coefficient variation. In the fixed design case these nonparametric sample covariances have different uniform asymptotic rates depending on direction, a result that differs fundamentally from the random design and stationary cases. The uniform asymptotic rates derived exceed the corresponding rates…

状态密度时间序列的非平稳性AI 解读

This paper proposes a new framework to analyze the nonstationarity in the time series of state densities, representing either cross-sectional or intra-period distributions of some underlying economic variables. We regard each state density as a realization of Hilbertian random variable, and use a functional time series model to fit a given time series of state densities. This allows us to explore various sources of the nonstationarity of such time series. The potential unit…

异方差向量自回归中的协整参数推断AI 解读

We consider estimation and hypothesis testing on the coefficients of the co-integrating relations and the adjustment coefficients in vector autoregressions driven by shocks which display both conditional and unconditional heteroskedasticity of a quite general and unknown form. We show that the conventional results in Johansen (1996) for the maximum likelihood estimators and associated likelihood ratio tests derived under homoskedasticity do not in general hold under…

存在内生性与长记忆的非参数协整回归AI 解读

This paper explores nonparametric estimation, inference, and specification testing in a nonlinear cointegrating regression model where the structural equation errors are serially dependent and where the regressor is endogenous and may be driven by long memory innovations. Generalizing earlier results of Wang and Phillips (2009a,b, Econometric Theory 25, 710–738, Econometrica 77, 1901–1948), the conventional nonparametric local level kernel estimator is shown to be consistent…

基于广义最小二乘去趋势数据的残差协整检验AI 解读

We provide generalized least‐squares (GLS) detrended versions of single‐equation static regression or residuals‐based tests for testing whether or not non‐stationary time series are cointegrated. Our approach is to consider nearly optimal tests for unit roots and to apply them in the cointegration context. We derive the local asymptotic power functions of all tests considered for a triangular data‐generating process, imposing a directional restriction such that the…

平稳与非平稳自回归中自适应 Lasso 的一致及保守模型选择AI 解读

We show that the adaptive Lasso is oracle efficient in stationary and nonstationary autoregressions. This means that it estimates parameters consistently, selects the correct sparsity pattern, and estimates the coefficients belonging to the relevant variables at the same asymptotic efficiency as if only these had been included in the model from the outset. In particular, this implies that it is able to discriminate between stationary and nonstationary autoregressions and it…

非平稳数据的非参数变换回归AI 解读

We examine a kernel regression estimator for time series that takes account of the error correlation structure as proposed by Xiao et al. (2003, Journal of the American Statistical Association 98, 980–992). We show that this method continues to improve estimation in the case where the regressor is a unit root or a near unit root process.

含趋势协整模型的伪高斯与局部最优秩检验AI 解读

This paper provides pseudo-Gaussian and locally optimal rank-based tests for the cointegration rank in linear cointegrated error-correction models with common trends and i.i.d. elliptical innovations. The proposed tests are asymptotically distribution-free, hence their validity does not depend on the actual distribution of the innovations. The proposed rank-based tests depend on the choice of scores, associated with a reference density that can freely be chosen. Under…

含吸收态的状态切换ARMA贝叶斯推断：事前实际利率动态AI 解读

One goal of this article is to develop an efficient Metropolis–Hastings (MH) algorithm for estimating an ARMA model with a regime-switching mean, by designing a new efficient proposal distribution for the regime-indicator variable. Unlike the existing algorithm, our algorithm can achieve reasonably fast convergence to the posterior distribution even when the latent regime-indicator variable is highly persistent or when there exist absorbing states. Another goal is to…

N 固定时校正横截面依赖的新型面板协整检验AI 解读

In this paper, we propose new cointegration tests for single equations and panels. In both cases, the asymptotic distributions of the tests, which are derived with N fixed and T→∞, are shown to be standard normals. The effects of serial correlation and cross‐sectional dependence are mopped out via long‐run variances. An effective bias correction is derived, which is shown to work well in finite samples, particularly when N is smaller than T. Our panel tests are robust to…