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因子模型的张量主成分分析AI 解读

Modern empirical analysis often relies on high-dimensional panel datasets with non-negligible cross-sectional and time-series correlations. Factor models are natural for capturing such dependencies. A tensor factor model describes the $d$-dimensional panel as a sum of a reduced rank component and an idiosyncratic noise, generalizing traditional factor models for two-dimensional panels. We consider a tensor factor model corresponding to the notion of a reduced multilinear…

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机器学习时间序列回归及其即时预测应用AI 解读

This article introduces structured machine learning regressions for high-dimensional time series data potentially sampled at different frequencies. The sparse-group LASSO estimator can take advantage of such time series data structures and outperforms the unstructured LASSO. We establish oracle inequalities for the sparse-group LASSO estimator within a framework that allows for the mixing processes and recognizes that the financial and the macroeconomic data may have heavier…

回归模型大量零约束的检验及混频格兰杰因果应用AI 解读

This paper proposes a new test for a large set of zero restrictions in regression models based on a seemingly overlooked, but simple, dimension reduction technique. The procedure involves multiple parsimonious regression models where key regressors are split across simple regressions. Each parsimonious regression model has one key regressor and other regressors not associated with the null hypothesis. The test is based on the maximum of the squared parameters of the key…

动态预测计量建模中的大数据AI 解读

The modern economic environment is awash in Big Data. The business models of many of today’s largest and most innovative firms, for example, are intimately connected to data collection and processing. Closely related, all of science is now similarly awash in Big Data, due to massive advances in data capture and storage technology, typically in conjunction with the internet.1 Time-series econometrics is no exception, and the time-series community is beginning to confront Big…

宏观经济学与混频数据的现实AI 解读

Many time series are sampled at different frequencies. When we study co-movements between such series we usually analyze the joint process sampled at a common low frequency. This has consequences in terms of potentially mis-specifying the co-movements and hence the analysis of impulse response functions—a commonly used tool for economic policy analysis. We introduce a class of mixed frequency VAR models that allows us to measure the impact of high frequency data on low…

混频数据的格兰杰因果检验AI 解读

We develop Granger causality tests that apply directly to data sampled at different frequencies. We show that taking advantage of mixed frequency data allows us to better recover causal relationships when compared to the conventional common low frequency approach. We also show that the new causality tests have higher local asymptotic power as well as more power in finite samples compared to conventional tests. In an empirical application involving U.S. macroeconomic…

全球金融危机期间的央行宏观预测：欧洲央行与纽约联储的经验AI 解读

This article documents macroeconomic forecasting during the global financial crisis by two key central banks: the European Central Bank and the Federal Reserve Bank of New York. The article is the result of a collaborative effort between staff at the two institutions, allowing us to study the time-stamped forecasts as they were made throughout the crisis. The analysis does not exclusively focus on point forecast performance. It also examines methodological contributions,…

评论AI 解读

The article by Hu and Tsay (2013) relates to an important area of research on analyzing multivariate time series with dynamic heteroscedasticity and addresses the dimensionality problem of traditio...

宏观预测者是否应使用日度金融数据，以及如何使用？AI 解读

We introduce easy-to-implement, regression-based methods for predicting quarterly real economic activity that use daily financial data and rely on forecast combinations of mixed data sampling (MIDAS) regressions. We also extract a novel small set of daily financial factors from a large panel of about 1000 daily financial assets. Our analysis is designed to elucidate the value of daily financial information and provide real-time forecast updates of the current (nowcasting)…

混合采样频率回归模型AI 解读

We study regression models that involve data sampled at different frequencies. We derive the asymptotic properties of the NLS estimators of such regression models and compare them with the LS estimators of a traditional model that involves aggregating or equally weighting data to estimate a model at the same sampling frequency. In addition we propose new tests to examine the null hypothesis of equal weights in aggregating time series in a regression model. We explore the…

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…

存在辅助模型时的结构变化检验AI 解读

Several estimation procedures such as the efficient method of moments (EMM) of Gallant and Tauchen (1996, Econometric Theory 12, 657–681) and indirect inference procedure of Gouriéroux, Monfort, and Renault (1993, Journal of Applied Econometrics 8, S85–S118) involve two models, an auxiliary one and a model of interest. The role played by both models poses challenges and provides new opportunities for hypothesis testing beyond the usual Wald-, Lagrange multiplier–, and…

金融市场波动率动态中的多重结构突变检测AI 解读

Abstract The paper evaluates the performance of several recently proposed tests for structural breaks in the conditional variance dynamics of asset returns. The tests apply to the class of ARCH and SV type processes as well as data‐driven volatility estimators using high‐frequency data. In addition to testing for the presence of breaks, the statistics identify the number and location of multiple breaks. We study the size and power of the new tests for detecting breaks in the…

周期性状态转换模型的贝叶斯推断AI 解读

We present a general class of nonlinear time-series Markov regime-switching models for seasonal data which may exhibit periodic features in the hidden Markov process as well as in the laws of motion in each of the regimes. This class of models allows for non-trivial dependencies between seasonal, cyclical and long-term patterns in the data. To overcome the computational burden we adopt a Bayesian approach to estimation and inference. This paper contains two empirical…

季节调整与其他数据变换AI 解读

In this paper, it is shown that the case for using optimal signal extraction filters is not all that convincing once it is recognized that seasonal adjustment is typically not the only transformation applied to data. Seasonal adjustment is viewed as any general linear filter. All other data transformations are also assumed to be linear. While optimal filters always dominate uniform filters, their dominance critically depends on performing seasonal adjustment and the other…

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周期结构与季节单位根检验AI 解读

The standard testing procedures for seasonal unit roots developed so far have been based mainly on time-invariant autoregressive integrated moving average (ARIMA) processes with AR polynomials involving seasonal differencing. One attractive alternative is to use periodic ARMA models. Convenient procedures are presented for testing for the presence of unit roots at the zero and seasonal frequencies in periodic time series. The limiting distributions are derived and tabulated…

动态回归与滤波数据：小样本滤波效应的拉普拉斯近似AI 解读

It is common for an applied researcher to use filtered data, like seasonally adjusted series, for instance, to estimate the parameters of a dynamic regression model. In this paper, we study the effect of (linear) filters on the distribution of parameters of a dynamic regression model with a lagged dependent variable and a set of exogenous regressors. So far, only asymptotic results are available. Our main interest is to investigate the effect of filtering on the small sample…

季节调整是线性还是非线性的数据滤波过程？AI 解读

The authors investigate whether seasonal adjustment procedures are linear data transformations. This question was addressed by A. H. Young (1968) and is important for the estimation of regression models with seasonally adjustment data. The authors focus on the X-11 program and rely on simulation evidence, involving linear unobserved component autorgressive integrated moving average models. They define and test a set of properties for the adequacy of a linear approximation to…

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商业周期的周期性结构AI 解读

This article tests whether an economic recovery is equally likely to occur in any month of the year. I test this hypothesis with a homogeneous Markov switching-regime model and use the turning-point dates for business cycles marked by the National Bureau of Economic Research and Romer. I tend to find equal probabilities to switch from recession to expansions. In particular, the spring and the month of December appear to have higher incidence of economic recovery. My results…

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单位根检验与季节调整的统计陷阱：美国战后实际国民生产总值的案例AI 解读

Unit-root tests applied to the postwar seasonally adjusted quarterly gross national product series strongly support the null hypothesis of the presence of a unit root in the data-generating process. Evidence reported here with seasonally unadjusted data is far less conclusive. It is explained why seasonal-adjustment procedures may alter the outcome of conventional tests.