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结构预测分析AI 解读

Abstract This paper shows how the structural representation of a vector autoregressive model can support forecast analysis. We offer a unified framework that formalizes how the structural form of the model can help form a narrative for two key statistics in real-time VAR forecasting: the forecast errors at the outturn of the data, and the consequent revisions of the forecast. We conduct an exercise on the UK, focusing on the inflation surge that started in 2022. We show that…

极端高温持续性的估计与推断AI 解读

Summary: We propose a non-parametric framework for estimating the extremal index that captures the persistence of extreme observations. The framework provides unified and simple procedures for verifying the well-known local dependence condition $D^{(d)}(u_{n})$, which characterizes the extremal index yet is often assessed through heuristic checks, and for selecting d (a key parameter for estimation) when the condition holds. Under a general $\phi$-mixing condition, we…

气候常态与异常值的估计AI 解读

Summary The quantification of the interannual component of variability in climatological time series is essential for the assessment and prediction of the El Niño–Southern Oscillation phenomenon. This is achieved by estimating the deviation of a climate variable from normal conditions, defined by its baseline level and seasonal patterns. Climate normals are currently estimated by simple arithmetic averages calculated over the most recent 30-year period ending in a year…

大型非高斯SVAR及其货币政策应用AI 解读

Summary We develop a large structural vector autoregression (SVAR) that is statistically identified using higher-order moments under non-Gaussian, mutually independent shocks, without imposing economically motivated restrictions. Estimation is performed via an efficient Gibbs sampler that scales well with dimensionality, and we introduce an estimator of the deviance information criterion to facilitate model comparison, including the evaluation of overidentifying economic…

Summary We adapt double machine learning to macroeconomic time series by combining regularized nuisance estimation with reverse cross-fitting. This deterministic scheme exploits time reversibility to use time-reversed auxiliary blocks and, unlike neighbour-deletion designs, avoids buffer blocks, thereby improving sample usage. We derive conditions for asymptotic validity and show in simulations that the estimator performs well in realistic finite samples across the designs…

非高斯结构向量自回归中的时变冲击传导AI 解读

Summary This paper analyses possibly time-varying shock transmission in structural vector autoregressive (VAR) models when the reduced-form VAR coefficients are time invariant and the shocks are identified through non-Gaussianity. To check for possible time variation in the impulse responses, we propose Wald tests for two situations: (1) homoskedastic and (2) heteroskedastic structural shocks with changes in the unconditional variances. For the latter case, the challenge is…

结合统计识别与潜在内生代理变量估计财政乘数AI 解读

Summary Different proxy variables used in fiscal policy structural vector autoregressions (SVARs) lead to contradicting conclusions regarding the size of fiscal multipliers. Our analysis suggests that the conflicting results may stem from violations of the proxy exogeneity assumptions. We propose a novel approach to include proxy variables in a Bayesian non-Gaussian SVAR, tailored to accommodate potentially endogenous proxies. Using our model, we find that increasing…

正则化广义协方差估计量AI 解读

Summary This paper proposes the Regularized Generalized Covariance (RGCov) estimator, a ridge-type extension of the Generalized Covariance (GCov) estimator for high-dimensional stationary time series. By regularizing the GCov objective function, which involves the inverse of the covariance matrix, RGCov improves numerical stability while preserving positive definiteness. Under suitable conditions, the new estimator is consistent, asymptotically normal, and…

经济脆弱性具有状态依赖性AI 解读

Summary A novel dynamic model for joint estimation of multiple quantiles of a time series conditionally on a set of covariates is presented. The model preserves quantile monotonicity and allows for a clear interpretation of covariate effects across quantiles. Model parameters are estimated using a two-step M-estimator. The resulting estimator is consistent, and its finite sample properties are analyzed through simulations. The new model is used to study the impact of…

利用可能相依冲击的非高斯性识别结构向量自回归AI 解读

Summary We complement previous partial global identification results for the non-Gaussian structural vector autoregressive (SVAR) model by showing that in the absence of co-skewness among the strucural shocks, the skewed shocks are identified and in the absence of excess co-kurtosis, the shocks with non-zero excess kurtosis are identified. The former case has the advantage that dependent conditional heteroskedasticity is allowed for. In each case, the remaining shocks are…

短暂不稳定情形下的预测能力比较AI 解读

Summary We consider forecast comparison in the presence of instability when this affects only a short period of time. We demonstrate that global tests do not perform well in this case because they were not designed to capture very short-lived instabilities, and their power vanishes altogether when the magnitude of the shock is very large. We then discuss non-parametric approaches that are more suitable to detect such situations. We illustrate these results in a Monte Carlo…

局部投影的统计推断AI 解读

Summary Inference for impulse responses estimated with local projections presents interesting challenges and opportunities. Analysts typically want to assess the precision of individual estimates, explore the dynamic evolution of the response over particular regions, and generally determine whether the impulse generates a response that is any different from the null of no effect. Each of these goals requires a different approach to inference. In this article, we provide an…

时间序列回归中的稳健推断AI 解读

Summary Least squares regression with heteroskedasticity consistent standard errors (‘OLS-HC regression’) has proved very useful in cross-section environments. However, several major difficulties, which are generally overlooked, must be confronted when transferring the HC technology to time-series environments via heteroskedasticity and autocorrelation consistent standard errors (‘OLS-HAC regression’). First, in plausible time-series environments, OLS parameter estimates can…

随机系数模型中的最大化似然比检验AI 解读

Summary In a recent contribution, we developed a family of cumulative sum-based change-point tests in the context of a random coefficient autoregressive model of order 1. In the current paper, we complement the results in that contribution by studying the (maximally selected) likelihood ratio statistic. We show that this has power versus breaks occurring even as close as periods from the beginning/end of sample; moreover, the use of quasi-maximum likelihood-based estimates…

平滑转换向量自回归模型的易实施贝叶斯估计AI 解读

Summary We develop a tractable way of estimating the parameters ruling the nonlinearity in the popular smooth transition VAR model, and identify structural shocks using external instruments. This jointly offers an alternative to the option of identifying shocks recursively and calibrating key parameters. In an illustration, we show that monetary policy shocks generate larger effects on economic activity during economic expansions compared to economic recessions. We then…

Summary In this paper, we estimate impulse responses by local projections in high-dimensional settings. We use the desparsified (de-biased) lasso to estimate the high-dimensional local projections, while leaving the impulse response parameter of interest unpenalized. We establish the uniform asymptotic normality of the proposed estimator under general conditions. Finally, we demonstrate small sample performance through a simulation study and consider two canonical…

门槛非线性与民主—增长关系AI 解读

Summary This paper investigates the relationship between democracy and economic growth in the context of a linear index threshold regression model. We first introduce the baseline model with endogeneity and propose a two-step smoothed generalised method of moments estimation method. We establish the consistency and derive the asymptotic distributions of the proposed estimators. We then extend the approach to a dynamic panel context and employ the model to explore the impact…

含局部二次趋势的单位根新检验AI 解读

Summary This paper proposes a new test for unit root processes with a partial quadratic trend on an unknown break date, denoted as the URQ process herein. Such a process is extremely similar to the explosive bubble process, and both can capture the sharp rise in prices. We develop the asymptotic distributions under the local-to-unity hypothesis, which covers the URQ null and explosive root alternatives. Simulations show that the test has good finite sample performances and…

向量误差修正指数模型：表示、估计与识别AI 解读

Summary This paper extends the multivariate index autoregressive model to the case of cointegrated time series of order (1,1). In this new modelling, namely the vector error-correction index model (VECIM), the first differences of series are driven by some linear combinations of the variables, namely the indexes. When the indexes are significantly fewer than the variables, the VECIM achieves a substantial dimension reduction with reference to the vector error correction…

由后验揭示先验：英国通胀预测的应用AI 解读

Summary A Bayesian typically uses data and a prior to produce a posterior. We shall follow the opposite route, using data and the posterior information to reveal the prior. We then apply this theory to inflation forecasts by the Bank of England and the National Institute of Economic and Social Research in an attempt to get some insight into the prior beliefs of the policy makers in these two institutions, especially under the uncertainties about the Brexit referendum, the…

参数位于参数空间边界时的惩罚准似然估计与模型选择AI 解读

Summary We consider here penalized likelihood-based estimation and model selection applied to econometric time series models, which allow for nonnegativity (boundary) constraints on some or all of the parameters. We establish that joint model selection and estimation result in standard asymptotic Gaussian distributed estimators. The results contrast with nonpenalized estimation, which, as is well-known, leads to nonstandard asymptotic distributions that depend on the unknown…

混合因子结构下的大型协方差矩阵估计AI 解读

Summary We extend the principal orthogonal complement thresholding (POET) framework by J. Fan, Y. Liao, and M. Mincheva (2013) to estimate large covariance matrices with a ‘mixed’ structure of observable and unobservable strong/weak factors, and we call this method the extended POET (ePOET). Especially, the weak factor structure allows the existence of slowly divergent eigenvalues of the covariance matrix that are frequently observed in real data. Under some mild conditions,…

GARCH 时间序列中参数变化区间的检验AI 解读

Summary We develop a uniform test for detecting and dating the integrated or mildly explosive behaviour of a strictly stationary generalized autoregressive conditional heteroskedasticity (GARCH) process. Namely, we test the null hypothesis of a globally stable GARCH process with constant parameters against the alternative that there is an ‘abnormal’ period with changed parameter values. During this period, the parameter-value change may lead to an integrated or mildly…

2022年 Denis Sargan 计量经济学奖AI 解读

The editors of The Econometrics Journal decided to award the 2022 Denis Sargan Econometrics Prize to Chiara Casoli (Fondazione Eni Enrico Mattei, Milan) for her article “Permanent-Transitory decomposition of cointegrated time series via dynamic factor models, with an application to commodity prices” with Riccardo (Jack) Lucchetti in the May 2022 issue of The Econometrics Journal.1 Chiara and Jack’s prize winning article develops a cointegration-based Permanent-Transitory…