Dynamic Asymmetries in U.S. Unemployment

We examine dynamic asymmetries in U.S. unemployment using nonlinear time series models and Bayesian methods. We find strong statistical evidence in favor of a two-regime threshold auto-regressive model. Empirical results indicate that, once we take into account both parameter and model uncertainty, there are economically interesting asymmetries in the unemployment rate. One finding of particular interest is that shocks that lower the unemployment rate tend to have a smaller effect than shocks that raise the unemployment rate. This finding is consistent with unemployment rises being sudden and falls gradual.     

An Application of Vector Time Series Techniques to Macroeconomic Forecasting

A wide variety of time series techniques are now used for generating forecasts of economic variables, with each technique attempting to summarize and exploit whatever regularities exist in a given data set. It appears that many researchers arbitrarily choose one of these techniques. The purpose of this article is to provide an example for which the choice of time series technique appears important; merely choosing arbitrarily among available techniques may lead to suboptimal results.     

Using the Bootstrap as an Aid in Choosing the Approximate Representation for Vector Time Series

In this article, a procedure is presented to use the bootstrap in choosing the best approximation in terms of forecasting performance for the equivalent state-space representation of a vector autoregressive model. It is found that the proposed procedure, which uses each approximant's forecasting performance, can enhance considerably an approach based simply on the estimated Hankel singular values.     

互联网企业广告收入预测研究——基于低频数据的神经网络和时间序列组合模型

神经网络模型对大样本时间序列的拟合效果优于传统时间序列模型,但对于年度、月度、日度等低频时间序列的预测则难以发挥其优势。鉴于此,本文应用传统时间序列模型和神经网络模型,建立Holtwinters-BP组合模型,利用Holtwinters模型分别拟合各解释变量序列,利用BP模型拟合解释变量和自变量的非线性关系,基于某社交新闻类APP的日广告收入数据进行互联网企业广告收入预测研究。通过与循环神经网络(RNN)模型、长短期记忆神经网络(LSTM)模型等预测结果的对比发现:Holtwinters-BP组合模型的预测精度和稳定性更高;证明多维变量对于广告收入的显著影响,多变量模型的预测准确性高于单变量模型;构建的Holtwinters-BP组合模型对于低频数据预测有较好的有效性和适用性。     

Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models

An exact maximum likelihood method is developed for the estimation of parameters in a non-Gaussian nonlinear density function that depends on a latent Gaussian dynamic process with long-memory properties. Our method relies on the method of importance sampling and on a linear Gaussian approximating model from which the latent process can be simulated. Given the presence of a latent long-memory process, we require a modification of the importance sampling technique. In particular, the long-memory process needs to be approximated by a finite dynamic linear process. Two possible approximations are discussed and are compared with each other. We show that an autoregression obtained from minimizing mean squared prediction errors leads to an effective and feasible method. In our empirical study, we analyze ten daily log-return series from the S&P 500 stock index by univariate and multivariate long-memory stochastic volatility models. We compare the in-sample and out-of-sample performance of a number of models within the class of long-memory stochastic volatility models.     

Tests for Structural Changes in Time Series of Counts

We propose methods for detecting structural changes in time series with discrete‐valued observations. The detector statistics come in familiar L2‐type formulations incorporating the empirical probability generating function. Special emphasis is given to the popular models of integer autoregression and Poisson autoregression. For both models, we study mainly structural changes due to a change in distribution, but we also comment for the classical problem of parameter change. The asymptotic properties of the proposed test statistics are studied under the null hypothesis as well as under alternatives. A Monte Carlo power study on bootstrap versions of the new methods is also included along with a real data example.     

Forecasting With Nonspurious Factors in U.S. Macroeconomic Time Series

This study examines the practical implications of the fact that structural changes in factor loadings can produce spurious factors (or irrelevant factors) in forecasting exercises. These spurious factors can induce an overfitting problem in factor-augmented forecasting models. To address this concern, we propose a method to estimate nonspurious factors by identifying the set of response variables that have no structural changes in their factor loadings. Our theoretical results show that the obtained set may include a fraction of unstable response variables. However, the fraction is so small that the original factors are able to be identified and estimated consistently. Moreover, using this approach, we find that a significant portion of 132 U.S. macroeconomic time series have structural changes in their factor loadings. Although traditional principal components provide eight or more factors, there are significantly fewer nonspurious factors. The forecasts using the nonspurious factors can significantly improve out-of-sample performance.     

Automatic Specification Testing for Vector Autoregressions and Multivariate Nonlinear Time Series Models

This article introduces an automatic test for the correct specification of a vector autoregression (VAR) model. The proposed test statistic is a Portmanteau statistic with an automatic selection of the order of the residual serial correlation tested. The test presents several attractive characteristics: simplicity, robustness, and high power in finite samples. The test is simple to implement since the researcher does not need to specify the order of the autocorrelation tested and the proposed critical values are simple to approximate, without resorting to bootstrap procedures. In addition, the test is robust to the presence of conditional heteroscedasticity of unknown form and accounts for estimation uncertainty without requiring the computation of large-dimensional inverses of near-to-singularity covariance matrices. The basic methodology is extended to general nonlinear multivariate time series models. Simulations show that the proposed test presents higher power than the existing ones for models commonly employed in empirical macroeconomics and empirical finance. Finally, the test is applied to the classical bivariate VAR model for GNP (gross national product) and unemployment of Blanchard and Quah (1989 Blanchard, O. and Quah, D. 1989. “The Dynamic Effects of Aggregate Demand and Supply Disturbances,”. The American Economic Review, 79: 655–673. [Web of Science ®] , [Google Scholar]) and Evans (1989 Evans, G. W. 1989. “Output and Unemployment Dynamics in the United States: 1950–1985,”. Journal of Applied Econometrics, 4: 213–238. [Crossref], [Web of Science ®] , [Google Scholar]). Online supplementary material includes proofs and additional details.     

Bayesian Analysis of DSGE Models by S. An and F. Schorfheide

The paper that An and Schorfheide have written is an excellent piece of work and will become a useful reference for teaching and consultation purposes. The paper discusses in an articulate and convincing manner almost everything that one could think of covering in such a review. This makes the task of the commentator difficult. Nevertheless, I will attempt to add few insights on three issues which, in my opinion, play an important role in applied work and in the interpretation of the estimation result. In particular, I will discuss a) the sensitivity of posterior distributions to prior spreads; b) the effects of model misspecification and an approach to model respecification; c) parameter identification and its consequences for posterior inference.     

Bayesian Analysis of DSGE Models by S. An and F. Schorfheide

The paper that An and Schorfheide have written is an excellent piece of work and will become a useful reference for teaching and consultation purposes. The paper discusses in an articulate and convincing manner almost everything that one could think of covering in such a review. This makes the task of the commentator difficult. Nevertheless, I will attempt to add few insights on three issues which, in my opinion, play an important role in applied work and in the interpretation of the estimation result. In particular, I will discuss a) the sensitivity of posterior distributions to prior spreads; b) the effects of model misspecification and an approach to model respecification; c) parameter identification and its consequences for posterior inference.     

Modeling the ECU Against the U.S. Dollar: A Structural Monetary Interpretation

The proposal that a reformed international monetary system be designed around currency blocs has gained some popularity of late. In Europe, the formation of the Euro formalizes the existence of a DM-based currency block. Little academic research has been conducted, however, on the properties of composite currencies such as the Euro. In this article, we examine the properties of the precursor to the Euro—namely, the ECU—against the U.S. dollar using a monetary framework. The article has several novel features: Our modeling strategy involves starting with underlying equilibrium conditions rather than a final reduced form; uses divisia money rather than the more conventional simple-sum money; interprets, in an economic sense, the long-run relationships; and produces an appealing forecasting performance.     

Analysis of the Car Accident Indexes in Spain: A Multiple Time Series Approach

The main objective of this article is to specify and estimate a model for the car accident rates in Spain to improve input for the decision-making process for insurance companies and provide useful information for traffic authorities. The prediction performance of the model is also analyzed in an attempt to verify the improvement in prediction that takes place when more elaborate models are used.     

Seasonal Adjustment Using Structural Time Series Models: An Application and a Comparison With the Census X-11 Method

This article makes the method of seasonal adjustment operational using suitable structural time series models (STM). This so-called STM method is applied to several relevant Dutch macro- economic quarterly and monthly time series. The results are compared with those of the Census X-11 method using several formal criteria as yardsticks. The STM method proves to compete well with the Census X-11 method in this respect.     

A Comparison of Autoregressive Univariate Forecasting Procedures for Macroeconomic Time Series

The actual performance of several automated univariate autoregressive forecasting procedures, applied to 150 macroeconomic time series, are compared. The procedures are the random walk model as a basis for comparison; long autoregressions, with three alternative rules for lag length selection; and a long autoregression estimated by minimizing the sum of absolute deviations. The sensitivity of each procedure to preliminary transformations, data, periodicity, forecast horizon, loss function employed in parameter estimation, and seasonal adjustment procedures is examined. The more important conclusions are that Akaike's lag-length selection criterion works well in a wide variety of situations, the modeling of long memory components becomes important for forecast horizons of three or more periods, and linear combinations of forecasts do not improve forecast quality appreciably.     

The Changing Transmission of Uncertainty Shocks in the U.S.

This article investigates if the impact of uncertainty shocks on the U.S. economy has changed over time. To this end, we develop an extended factor augmented vector autoregression (VAR) model that simultaneously allows the estimation of a measure of uncertainty and its time-varying impact on a range of variables. We find that the impact of uncertainty shocks on real activity and financial variables has declined systematically over time. In contrast, the response of inflation and the short-term interest rate to this shock has remained fairly stable. Simulations from a nonlinear dynamic stochastic general equilibrium (DSGE) model suggest that these empirical results are consistent with an increase in the monetary authorities’ antiinflation stance and a “flattening” of the Phillips curve. Supplementary materials for this article are available online.     

The Revenue Effects of the Kennedy and Reagan Tax Cuts: Some Time Series Estimates

Univariate time series models are estimated for sample periods ending with the enactment of major tax reductions in 1964 and 1981. These models are used to forecast government revenue for the period following the tax cut, and the pattern of forecast errors is examined. Unforecast revenue is negative and large relative to its standard error following the 1981 tax cuts but is close to zero following the 1964 cuts. This disparity occurs because national output behaved differently in the two cases, suggesting that short-run movements in output are dominated by factors other than tax rate changes.     

Asymptotic Theory of Outlier Detection Algorithms for Linear Time Series Regression Models

Outlier detection algorithms are intimately connected with robust statistics that down‐weight some observations to zero. We define a number of outlier detection algorithms related to the Huber‐skip and least trimmed squares estimators, including the one‐step Huber‐skip estimator and the forward search. Next, we review a recently developed asymptotic theory of these. Finally, we analyse the gauge, the fraction of wrongly detected outliers, for a number of outlier detection algorithms and establish an asymptotic normal and a Poisson theory for the gauge.     

对建立中国股票价格指数时间序列模型的探讨

文章对深圳成份指数建立随机游走模型、ARIMA模型及干预分析模型,并比较选择最合适的解释模型,进而得出:从统计分析角度看,干预模型更少使用解释变量,能更准确解释我国证券交易市场价格指数的波动情况;从干预模型的干预变量的选取中,还可以实证地说明我国存在较为明显的政策干预和庄家干预。     

门限自回归下的单位根检验可靠性研究

在非对称的门限自回归模型下,由于传统单位根检验式的误设,会导致单位根检验势下降。本文通过一系列的Monte-Carlo模拟表明：非对称性对ADF和PP检验的检验势会产生较大影响,而对其他四种常用的单位根检验势产生的影响较小,也就是说,在非对称的门限自回归下,非对称性对退势单位根检验势产生的影响较小。模拟中也发现：NP单位根检验对TAR模型和持久性都具有稳健性。     

Pseudo–Modelling of Outliers for Estimation of Regression And Time Series Models

This paper documents situations where the variance inflation model for outliers has undesirable properties. The model is commonly used to accommodate outliers in a Bayesian analysis of regression and time series models. The alternative approach provided here does not suffer from these undesirable properties but gives inferences similar to those of the variance inflation model when this is appropriate. It can be used with regression, time series, and regression with correlated errors in a unified way, and adheres to the scientific principle that inference should be based on the data after obvious outliers have been discarded. Only one parameter is required for outliers; it is interpretable as the a priori willingness to remove observations from the analysis.