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经济预测的面板数据方法：偏差校正平均预测AI 解读

In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the (feasible) bias-corrected average forecast. Using panel-data sequential asymptotics we show that it is potentially superior to other techniques in several contexts. In particular, it is asymptotically equivalent to the conditional expectation, i.e., has an optimal limiting mean-squared error. We also…

分位数、期望分位数与样条AI 解读

A time-varying quantile can be fitted by formulating a time series model for the corresponding population quantile and iteratively applying a suitably modified state space signal extraction algorithm. It is shown that such quantiles satisfy the defining property of fixed quantiles in having the appropriate number of observations above and below. Like quantiles, time-varying expectiles can be estimated by a state space signal extraction algorithm and they satisfy properties…

融入判断信息的预测AI 解读

This article shows how to account for nonsample information in the classical forecasting framework. We explicitly incorporate two elements: a default decision and a probability reflecting the confidence associated with it. Starting from the default decision, the new estimator increases the objective function only as long as its first derivatives are statistically different from zero. It includes as a special case the classical estimator and has clear analogies with Bayesian…

利用大型实时数据集比较 Greenbook 与约化式预测AI 解读

Many recent articles have found that atheoretical forecasting methods using many predictors give better predictions for key macroeconomic variables than various small-model methods. The practical relevance of these results is open to question, however, because these articles generally use ex post revised data not available to forecasters and because no comparison is made to best actual practice. We provide some evidence on both of these points using a new large dataset of…

实时数据集修订过程中的信息AI 解读

Rationality of early release data is typically tested using linear regressions. Thus, failure to reject the null does not rule out the possibility of nonlinear dependence. This paper proposes two tests that have power against generic nonlinear alternatives. A Monte Carlo study shows that the suggested tests have good finite sample properties. Additionally, we carry out an empirical illustration using a real-time dataset for money, output, and prices. Overall, we find…

模型不确定性下利用英国货币总量进行实时预测AI 解读

A popular account for the demise of the U.K.’s monetary targeting regime in the 1980s blames the fluctuating predictive relationships between broad money and inflation and real output growth. Yet ex post policy analysis based on heavily revised data suggests no fluctuations in the predictive content of money. In this paper, we investigate the predictive relationships for inflation and output growth using both real-time and heavily revised data. We consider a large set of…

实时数据下的预测能力相等检验AI 解读

This paper examines the asymptotic and finite-sample properties of tests of equal forecast accuracy applied to direct, multistep predictions from both nonnested and nested linear regression models. In contrast to earlier work in the literature, our asymptotics take account of the real-time, revised nature of the data. Monte Carlo simulations indicate that our asymptotic approximations yield reasonable size and power properties in most circumstances. The paper concludes with…

领先指标对经济周期峰值的领先时间是否长于谷值？AI 解读

We develop a novel Markov switching vector autoregressive model to investigate the possibility that leading indicators have different lead times at business cycle peaks and at troughs. In this model, coincident and leading indicators share a common Markov state process, but their cycles are nonsynchronous, with the nonsynchronicity varying across regimes. An application shows that on average the Conference Board’s Composite Leading Index leads the Composite Coincident Index…

预测专业预测者的预测AI 解读

Surveys of forecasters, containing respondents’ predictions of future values of key macroeconomic variables, receive a lot of attention in the financial press, from investors and from policy makers. They are apparently widely perceived to provide useful information about agents’ expectations. Nonetheless, these survey forecasts suffer from the crucial disadvantage that they are often quite stale, as they are released only infrequently. In this article, we propose MIDAS…

预测者进入与退出条件下的预测组合AI 解读

Combination of forecasts from survey data is complicated by the frequent entry and exit of individual forecasters which renders conventional least squares regression approaches infeasible. We explore the consequences of this issue for existing combination methods and propose new methods for bias-adjusting the equal-weighted forecast or applying combinations on an extended panel constructed by back-filling missing observations using an EM algorithm. Through simulations and an…

经济运行状况的实时测度AI 解读

We construct a framework for measuring economic activity at high frequency, potentially in real time. We use a variety of stock and flow data observed at mixed frequencies (including very high frequencies), and we use a dynamic factor model that permits exact filtering. We illustrate the framework in a prototype empirical example and a simulation study calibrated to the example.

基于马尔可夫转换截距自回归的经济周期非对称性检验AI 解读

Implications of two concepts of asymmetry—deepness and steepness—are investigated for autoregressive processes with a Markov-switching intercept. The formulas for the skewness of these processes and the skewness of the first differences of these processes are derived. The parameter restrictions leading to nondeepness and nonsteepness are presented for the special case of a first-order autoregression and two states. It is shown that these restrictions imply that previous…

具有单位根的异方差时间序列AI 解读

In this paper we provide a unified theory, and associated invariance principle, for the large-sample distributions of the Dickey–Fuller class of statistics when applied to unit root processes driven by innovations displaying nonstationary stochastic volatility of a very general form. These distributions are shown to depend on both the spot volatility and the integrated variation associated with the innovation process. We propose a partial solution (requiring any leverage…

预测回归中基于限制似然的偏差缩减与近乎精确尺寸检验AI 解读

Difficulties with inference in predictive regressions are generally attributed to strong persistence in the predictor series. We show that the major source of the problem is actually the nuisance intercept parameter, and we propose basing inference on the restricted likelihood, which is free of such nuisance location parameters and also possesses small curvature, making it suitable for inference. The bias of the restricted maximum likelihood (REML) estimates is shown to be…

持久数据下动态面板模型的 GMM 估计与推断AI 解读

In this paper we consider generalized method of moments–based (GMM-based) estimation and inference for the panel AR(1) model when the data are persistent and the time dimension of the panel is fixed. We find that the nature of the weak instruments problem of the Arellano–Bond (Arellano and Bond, 1991, Review of Economic Studies 58, 277–297) estimator depends on the distributional properties of the initial observations. Subsequently, we derive local asymptotic approximations…

异质依赖数据下核估计量的一致收敛速度AI 解读

The main uniform convergence results of Hansen (2008, Econometric Theory 24, 726–748) are generalized in two directions: Data are allowed to (a) be heterogeneously dependent and (b) depend on a (possibly unbounded) parameter. These results are useful in semiparametric estimation problems involving time-inhomogeneous models and/or sampling of continuous-time processes. The usefulness of these results is demonstrated by two applications: kernel regression estimation of a…

打开黑箱：大横截面结构因子模型AI 解读

This paper shows how large-dimensional dynamic factor models are suitable for structural analysis. We argue that all identification schemes employed in structural vector autoregression (SVAR) analysis can be easily adapted in dynamic factor models. Moreover, the “problem of fundamentalness,” which is intractable in SVARs, can be solved, provided that the impulse-response functions are sufficiently heterogeneous. We provide consistent estimators for the impulse-response…

具有 GARCH 误差的单位根过程的最小绝对偏差估计AI 解读

This paper considers a local least absolute deviation estimation for unit root processes with generalized autoregressive conditional heteroskedastic (GARCH) errors and derives its asymptotic properties under only finite second-order moment for both errors and innovations. When the innovations are symmetrically distributed, the asymptotic distribution of the estimated unit root is shown to be a functional of a bivariate Brownian motion, and then two unit root tests are…

整合过程非线性变换的弱收敛：多元情形AI 解读

We consider weak convergence of sample averages of nonlinearly transformed stochastic triangular arrays satisfying a functional invariance principle. A fundamental paradigm for such processes is constituted by integrated processes. The results obtained are extensions of recent work in the literature to the multivariate and non-Gaussian case. As admissible nonlinear transformation, a new class of functionals (so-called locally p -integrable functions) is introduced that…

Student t 混合自回归模型及其在厚尾金融数据中的应用AI 解读

We introduce the class of Student t-mixture autoregressive models, which is promising for financial time series modelling. The model is able to capture serial correlations, time-varying means and volatilities, and the shape of the conditional distributions can be time varied from short-tailed to long-tailed, or from unimodal to multimodal. The use of t-distributed errors in each component of the model allows conditional leptokurtic distributions that account for the commonly…

计数时间序列的负二项模型AI 解读

We study generalized linear models for time series of counts, where serial dependence is introduced through a dependent latent process in the link function. Conditional on the covariates and the latent process, the observation is modelled by a negative binomial distribution. To estimate the regression coefficients, we maximize the pseudolikelihood that is based on a generalized linear model with the latent process suppressed. We show the consistency and asymptotic normality…

多元时间序列频域检验的自助法：谱密度比较应用AI 解读

Summary We propose a general bootstrap procedure to approximate the null distribution of non-parametric frequency domain tests about the spectral density matrix of a multivariate time series. Under a set of easy-to-verify conditions, we establish asymptotic validity of the bootstrap procedure proposed. We apply a version of this procedure together with a new statistic to test the hypothesis that the spectral densities of not necessarily independent time series are equal. The…

自适应变系数时空模型AI 解读

Summary We propose an adaptive varying-coefficient spatiotemporal model for data that are observed irregularly over space and regularly in time. The model is capable of catching possible non-linearity (both in space and in time) and non-stationarity (in space) by allowing the auto-regressive coefficients to vary with both spatial location and an unknown index variable. We suggest a two-step procedure to estimate both the coefficient functions and the index variable, which is…