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Estimation and Forecasting in Models with Multiple Breaks

多重结构突变模型的估计与预测

This paper develops a new approach to change-point modelling that allows the number of change-points in the observed sample to be unknown. The model we develop assumes that regime durations have a Poisson distribution. It approximately nests the two most common approaches: the time-varying parameter (TVP) model with a change-point every period and the change-point model with a small number of regimes. We focus considerable attention on the construction of reasonable…

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Inference of Trends in Time Series

时间序列趋势的统计推断

Summary We consider statistical inference of trends in mean non-stationary models. A test statistic is proposed for the existence of structural breaks in trends. On the basis of a strong invariance principle of stationary processes, we construct simultaneous confidence bands with asymptotically correct nominal coverage probabilities. The results are applied to global warming temperature data and Nile river flow data. Our confidence band of the trend of the global warming…

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Determining the Number of Factors in the General Dynamic Factor Model

一般动态因子模型中的因子数量确定

This article develops an information criterion for determining the number q of common shocks in the general dynamic factor model developed by Forni et al., as opposed to the restricted dynamic model considered by Bai and Ng and by Amengual and Watson. Our criterion is based on the fact that this number q is also the number of diverging eigenvalues of the spectral density matrix of the observations as the number n of series goes to infinity. We provide sufficient conditions…

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Dynamic Integration of Time- and State-Domain Methods for Volatility Estimation

波动率估计中时间域与状态域方法的动态整合

Time- and state-domain methods are two common approaches to nonparametric prediction. Whereas the former uses data predominantly from recent history, the latter relies mainly on historical information. Combining these two pieces of valuable information is an interesting challenge in statistics. We surmount this problem by dynamically integrating information from both the time and state domains. The estimators from these two domains are optimally combined based on a…

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THE REAL PART OF A COMPLEX ARMA PROCESS

复数 ARMA 过程的实部

In what follows all processes referred to are weakly stationary. Let us call the real part of a complex ARMA(p,q) process a Re CARMA(p,q) process. Every real ARMA(p,q) process can trivially be written as a Re CARMA(p,q) process. Provided the moment properties of complex linear processes are appropriately specified, the following inverse result is available: every Re CARMA(p,q) process is spectrally equivalent to a real ARMA(2p,p + q) process or some simpler process. Thus the…

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ON THE STATIONARITY OF MARKOV-SWITCHING GARCH PROCESSES

马尔可夫状态转换 GARCH 过程的平稳性

Generalized autoregressive conditional heteroskedasticity (GARCH) models with Markov-switching regimes are often used for volatility analysis of financial time series. Such models imply less persistence in the conditional variance than the standard GARCH model and potentially provide a significant improvement in volatility forecast. Nevertheless, conditions for asymptotic wide-sense stationarity have been derived only for some degenerated models. In this paper, we introduce…

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A ROBUST BAYESIAN APPROACH FOR UNIT ROOT TESTING

单位根检验的稳健贝叶斯方法

In this paper we deal with the identification of an autoregressive model for an observed time series and the detection of a unit root in its characteristic polynomial. This is a big issue concerned with distinguishing stationary time series from time series for which differencing is required to induce stationarity. We consider a Bayesian approach, and particular attention is devoted to the problem of the sensitivity of the standard Bayesian analysis with respect to the…

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THE INTEGRATION ORDER OF VECTOR AUTOREGRESSIVE PROCESSES

向量自回归过程的单整阶数

We show that the order of integration of a vector autoregressive process is equal to the difference between the multiplicity of the unit root in the characteristic equation and the multiplicity of the unit root in the adjoint matrix polynomial. The equivalence with the standard I(1) and I(2) conditions (Johansen, 1996, Likelihood-Based Inference in Cointegrated Vector Auto-Regressive Models) is proved.I am very grateful to Søren Johansen for his precious insights and his…

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Efficient tests of long‐run causation in trivariate VAR processes with a rolling window study of the money–income relationship

三变量 VAR 过程长期因果关系的有效检验:货币—收入关系的滚动窗口研究

Abstract This paper develops a simple sequential multiple‐horizon non‐causation test strategy for trivariate VAR models (with one auxiliary variable). We apply the test strategy to a rolling window study of money supply and real income, with the price of oil, the unemployment rate and the spread between the Treasury bill and commercial paper rates as auxiliary processes. Ours is the first study to control simultaneously for common stochastic trends, sensitivity of test…

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Money demand function estimation by nonlinear cointegration

利用非线性协整估计货币需求函数

Abstract Conventionally, the money demand function is estimated using a regression of the logarithm of money demand on either the interest rate or the logarithm of the interest rate. This equation is presumed to be a cointegrating regression. In this paper, we aim to combine the logarithmic specification, which models the liquidity trap better than a linear model, with the assumption that the interest rate itself is an integrated process. The proposed technique is robust to…

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Approximately normal tests for equal predictive accuracy in nested models

嵌套模型预测准确性相等的近似正态检验

Forecast evaluation often compares a parsimonious null model to a larger model that nests the null model. Under the null that the parsimonious model generates the data, the larger model introduces noise into its forecasts by estimating parameters whose population values are zero. We observe that the mean squared prediction error (MSPE) from the parsimonious model is therefore expected to be smaller than that of the larger model. We describe how to adjust MSPEs to account for…

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Inference and Model Choice for Sequentially Ordered Hidden Markov Models

按出现顺序标记的隐马尔可夫模型:推断与模型选择

Summary The system equation of a hidden Markov model is rewritten to label the components by order of appearance, and to make explicit the random behaviour of the number of components, mt. We argue that this reformulation is often a good way to achieve identifiability, as it facilitates the interpretation of the posterior density, and the estimation of the number of components that have appeared in a given sample. We develop a sequential Monte Carlo algorithm for estimating…

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Probabilistic Forecasts, Calibration and Sharpness

概率预测、校准与集中度

Summary Probabilistic forecasts of continuous variables take the form of predictive densities or predictive cumulative distribution functions. We propose a diagnostic approach to the evaluation of predictive performance that is based on the paradigm of maximizing the sharpness of the predictive distributions subject to calibration. Calibration refers to the statistical consistency between the distributional forecasts and the observations and is a joint property of the…

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Uniformly root-n consistent density estimators for weakly dependent invertible linear processes

弱依赖可逆线性过程的一致根号样本量收敛密度估计

Convergence rates of kernel density estimators for stationary time series are well studied. For invertible linear processes, we construct a new density estimator that converges, in the supremum norm, at the better, parametric, rate n−1/2. Our estimator is a convolution of two different residual-based kernel estimators. We obtain in particular convergence rates for such residual-based kernel estimators; these results are of independent interest.

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Rank-based estimation for all-pass time series models

全通时间序列模型的秩估计

An autoregressive-moving average model in which all roots of the autoregressive polynomial are reciprocals of roots of the moving average polynomial and vice versa is called an all-pass time series model. All-pass models are useful for identifying and modeling noncausal and noninvertible autoregressive-moving average processes. We establish asymptotic normality and consistency for rank-based estimators of all-pass model parameters. The estimators are obtained by minimizing…

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M-estimation of linear models with dependent errors

相依误差线性模型的 M 估计

We study asymptotic properties of M-estimates of regression parameters in linear models in which errors are dependent. Weak and strong Bahadur representations of the M-estimates are derived and a central limit theorem is established. The results are applied to linear models with errors being short-range dependent linear processes, heavy-tailed linear processes and some widely used nonlinear time series.

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Macroeconomic Volatility, Predictability, and Uncertainty in the Great Moderation

大缓和时期的宏观波动、可预测性与不确定性

I examine the extent to which the large decline in observed real output volatility, that is, the great moderation, can be attributed to changes in macroeconomic uncertainty and macroeconomic predictability using forecasts of future real output growth from the Survey of Professional Forecasters (SPF). The results indicate that both predictability and uncertainty have declined over the great moderation. The results indicate that measuring the decline in macroeconomic…

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Market-Based Measures of Monetary Policy Expectations

基于市场的货币政策预期度量

A number of recent articles have used different financial market instruments to measure near-term expectations of the federal funds rate and the high-frequency changes in these instruments around Federal Open Market Committee announcements to measure monetary policy shocks. This article evaluates the empirical success of a variety of financial market instruments in predicting the future path of monetary policy. All of the instruments we consider provide forecasts that are…

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Intrinsic Bayesian Estimation of Vector Autoregression Impulse Responses

向量自回归脉冲响应的内在贝叶斯估计

We propose an information-theoretic alternative to the conventional Bayesian posterior mean estimator of impulse responses in vector autoregression (VAR) models. The proposed estimator is based on the intrinsic entropy loss function, which is invariant to nonlinear transformations of parameters. Consequently, intrinsic estimation of impulse responses is equivalent to that of VAR parameters. The Bayesian estimator under the entropy loss involves a frequentist expectation of…

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Comparing Density Forecasts via Weighted Likelihood Ratio Tests

用加权似然比检验比较密度预测

We propose a test for comparing the out-of-sample accuracy of competing density forecasts of a variable. The test is valid under general conditions: The data can be heterogeneous and the forecasts can be based on (nested or nonnested) parametric models or produced by semiparametric, nonparametric, or Bayesian estimation techniques. The evaluation is based on scoring rules, which are loss functions defined over the density forecast and the realizations of the variable. We…

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REDUNDANCY OF LAGGED REGRESSORS REVISITED

滞后解释变量冗余性的再考察

In a recent Econometric Theory problem, it was demonstrated that in a conditionally heteroskedastic time series regression with martingale difference errors the use of lagged values of regressors as instruments may not increase the efficiency of estimation relative to ordinary least squares. We provide an example of an analogous phenomenon in a model with serially correlated errors, where the optimal instrumental variables estimator is asymptotically as efficient as the…

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MODIFIED KPSS TESTS FOR NEAR INTEGRATION

针对近积分过程的修正 KPSS 检验

This note suggests a simple modification to the Kwiatkowski, Phillips, Schmidt, and Shin (1992, Journal of Econometrics , 54, 159–178) test (KPSS test) so that it is applicable to testing the null hypothesis of near integration against a unit root alternative. The modified KPSS test is shown not to suffer from the asymptotic size distortion problems of the original KPSS test that are described by Müller (2005, Journal of Econometrics 128, 195–213). The test also has good…

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