Dynamic factor analysis with non-linear temporal aggregation constraints

Summary.  The paper estimates an index of coincident economic indicators for the US economy by using time series with different frequencies of observation (monthly and quarterly, possibly with missing values). The model that is considered is the dynamic factor model that was proposed by Stock and Watson, specified in the logarithms of the original variables and at the monthly frequency, which poses a problem of temporal aggregation with a non-linear observational constraint when quarterly time series are included. Our main methodological contribution is to provide an exact solution to this problem that hinges on conditional mode estimation by iteration of the extended Kalman filtering and smoothing equations. On the empirical side the contribution of the paper is to provide monthly estimates of quarterly indicators, among which is the gross domestic product, that are consistent with the quarterly totals. Two applications are considered: the first dealing with the construction of a coincident index for the US economy, whereas the second does the same with reference to the euro area.     

中国劳动力市场动态均衡的实证研究

文章运用蛛网模型对劳动力市场进行动态均衡分析,给出了劳动力市场均衡的条件及影响因素,利用中国劳动力市场的有关数据对其进行实证,并采用时间序列分析方法对中国未来几年的劳动力市场均衡状态进行预测;提出了改善我国劳动力市场均衡状态的思路。     

How Do Behavioral Assumptions Affect Structural Inference? Evidence From a Laboratory Experiment

We develop a model of labor force status (federal employment, nonfederal employment, unemployment, and out of the labor force) that depends on human capital variables, local labor market conditions, and personal characteristics. According to the estimated model for white non-Hispanic males and females a substantial difference exists between blacks and white non-Hispanics even after correction for the control variables. However, the control variables explain almost all of the difference between Hispanics and white non-Hispanics.     

Detecting seasonal unit roots in a structural time series model

In this paper, we propose to detect seasonal unit roots within the context of a structural time series model. Such a model is often found to be useful in practice. Using Monte Carlo simulations, we show that our method works well. We illustrate our approach for several quarterly macroeconomic time series variables.     

Rational Expectations and Macroeconomic Forecasts

The article presents extensive results from testing for bias and serially correlated errors in a collection of time series of quarterly multiperiod forecasts for six variables including real GNP growth, inflation, and unemployment. The analysis covers responses by 79 frequent participants in economic outlook surveys conducted regularly since 1968. It shows much greater incidence of apparently systematic errors for inflation than for the other variables. Also, the tests are more favorable to composite group forecasts than to most of the individual forecast sets.     

Testing nested and non-nested periodically integrated autoregressive models

Periodically integrated time series require a periodic differencing filter to remove the stochastic trend. A non-periodic integrated time series needs the first-difference filter for similar reasons. When the changing sea- sonal fluctuations for the non-periodic integrated series can be described by seasonal dummy variables for which the corresponding parameters are not constant within the sampie, such a series may not be easily & stinguished from a periodically integrated time series. In this paper, nested and non-nested testing procedures are proposed to distinguish between these two alternative stochastic and non-stochastic seasonal processes, When it is assumed there is a single unknown structural break in the seasonal dummy parameters. Several empirical examples using quarterly real macroeconomic time series for the United Kingdom illustrate the nested and non-nested approaches.     

情绪冲击、资产泡沫与经济波动:基于贝叶斯DSGE的分析

本文在Smets和Wouters(2003)、Christiano等(2005)模型基础上,引入驱动股价泡沫的情绪冲击,构建了情绪冲击通过资产价格渠道影响经济波动的动态随机一般均衡模型,并采用我国2000-2016年的季度数据对模型进行贝叶斯估计。研究表明,由于企业面临融资约束,正向情绪冲击带来股价泡沫的上升起到了放松信贷约束的作用,因而企业投资增加,进而触发一系列经济变量的顺周期波动。情绪冲击能够解释我国股票价格波动的552%以及顺周期性;劳动供给冲击、技术冲击、投资专有冲击、金融冲击都是我国经济波动的来源,尽管其对产出、消费、投资、劳动时间和股票价格波动的贡献存在异质性。     

影响中国农村劳动力省际迁移动因的实证分析——基于新劳动力迁移理论

利用2005年中国1%人口抽样调查数据中的农村劳动力省际迁移数据,结合新劳动力迁移经济理论,对logit模型进行了扩展,加入了农村信贷市场、农业生产性固定资产、农业受灾比例和老年抚养比,考察它们对省际迁移的影响。结果显示：农村信贷市场越不完善、农业生产性固定资产原值越高、农业受灾比例越大和老年人抚养比越高的地区,农村劳动力外迁的比例也就越大。因此,在中国农村金融和保险市场不完善的前提下,农村劳动力迁移是农民分散农业风险和稳定家庭收入的一种有效方式。     

A vector of quarters representation for bivariate time series

In this paper it is shown that several models for a bivariate nonstationary quarterly time series are nested in a vector autoregression with cointegration restrictions for the eight annual series of quarterly observations. Or, the Granger Representation Theorem is extended to incorporate, e.g., seasonal and periodic cointegration.     

A vector of quarters representation for bivariate time series

In this paper it is shown that several models for a bivariate nonstationary quarterly time series are nested in a vector autoregression with cointegration restrictions for the eight annual series of quarterly observations. Or, the Granger Representation Theorem is extended to incorporate, e.g., seasonal and periodic cointegration.     

Predictive Accuracy Gain From Disaggregate Sampling in ARIMA Models

We compare the forecast accuracy of autoregressive integrated moving average (ARIMA) models based on data observed with high and low frequency, respectively. We discuss how, for instance, a quarterly model can be used to predict one quarter ahead even if only annual data are available, and we compare the variance of the prediction error in this case with the variance if quarterly observations were indeed available. Results on the expected information gain are presented for a number of ARIMA models including models that describe the seasonally adjusted gross national product (GNP) series in the Netherlands. Disaggregation from annual to quarterly GNP data has reduced the variance of short-run forecast errors considerably, but further disaggregation from quarterly to monthly data is found to hardly improve the accuracy of monthly forecasts.     

Temporal Disaggregation of Economic Time Series using Artificial Neural Networks

Several methods based on smoothing or statistical criteria have been used for deriving disaggregated values compatible with observed annual totals. The present method is based on the artificial neural networks. This article evaluates the use of artificial neural networks (ANNs) for the disaggregation of annual US GDP data to quarterly time increments. A feed-forward neural network with back-propagation algorithm for learning was used. An ANN model is introduced and evaluated in this paper. The proposed method is considered as a temporal disaggregation method without related series. A comparison with previous temporal disaggregation methods without related series has been done. The disaggregated quarterly GDP data compared well with observed quarterly data. In addition, they preserved all the basic statistics such as summing to the annual data value, cross correlation structure among quarterly flows, etc.     

Producing Monthly Estimates of Unemployment and Employment According to the International Labour Office Definition

Monthly unemployment statistics are available in Britain from a monthly count of the number of people claiming unemployment-related benefits. There has been considerable debate on the appropriateness of this measure. Unemployment and employment statistics are available quarterly from the Labour Force Survey (LFS), using International Labour Office (ILO) definitions. In this paper various options for producing monthly unemployment estimates according to the ILO definition are examined. Methods considered are a monthly LFS, calculating rolling averages from the quarterly LFS, and methods which combine LFS and claimant count data. It is proposed that a monthly LFS of 60 000 households be introduced which can produce monthly estimates of total unemployment and more detailed estimates quarterly. Such a survey would also fill an important gap by providing monthly employment statistics which are needed to provide a complete picture of the labour market.     

利用互联网大数据预测季度GDP增速的方法研究

大数据为季度GDP走势预测创新研究带来重要突破口。本文利用百度等网站的互联网大数据,基于代表性高维数据机器学习(和深度学习)模型,对我国2011-2018年季度GDP增速深入进行预测分析。研究发现,对模型中的随机干扰因素作出一定分布的统计假设,有助于降低预测误差,任由模型通过大量数据机械地学习和完善并不总是有利于模型预测能力的提升;采用对解释变量集添加惩罚约束的方法,可以有效地处理互联网大数据维度较高的棘手问题;预测季度GDP增速的最优大数据解释变量集的稳定性较高。     

INDEX TO VOLUME 8 (1 990)

This article provides a simple shrinkage representation that describes the operational characteristics of various forecasting methods designed for a large number of orthogonal predictors (such as principal components). These methods include pretest methods, Bayesian model averaging, empirical Bayes, and bagging. We compare empirically forecasts from these methods with dynamic factor model (DFM) forecasts using a U.S. macroeconomic dataset with 143 quarterly variables spanning 1960–2008. For most series, including measures of real economic activity, the shrinkage forecasts are inferior to the DFM forecasts. This article has online supplementary material.     

A SCORE TEST FOR SEASONAL FRACTIONAL INTEGRATION AND COINTEGRATION

This paper develops a time domain score statistic for testing fractional integration at zero and seasonal frequencies in quarterly time series models. Further, it introduces the notion of fractional cointegration at different frequencies between two seasonally integrated, I(1) series. In testing problems involving seasonal fractional cointegration, it is argued that the alternative hypothesis is one-sided for which the usual score test may not be appropriate. Therefore, based on ideas in Silvapulle and Silvapulle (1995), a one-sided score statistic is constructed. A simulation study finds that the score statistic generally has desirable size and power properties in moderately sized samples. The score test is applied to the quarterly Australian consumption function. The income and consumption series are found to be I(1) at zero and seasonal frequencies and these two series are not cointegrated at any frequency.     

Effects of future population trends on the labor market--analyses based on projections prepared by the Institute for Research on the Labor Market and Occupations

The effects of future population trends, such as demographic aging, declining fertility, and changes in migration, on the labor market in the Federal Republic of Germany are analyzed up to the year 2000. The study is based on projections prepared by the Institute for Research on the Labor Market and Occupations. Topics discussed include demographic trends as a cause of current unemployment, labor market phases and demographic trends since 1950, the projection model used, age-specific projections of the potential labor force, and labor market projections.     

The influence of social programs in source countries on various classes of U.S. immigration

"This article uses a unique set of pooled cross-sectional and time series data to examine the annual rate of U.S. immigration during 1972-1991 from 60 source countries. One distinguishing feature of the article is that it breaks out and cross-classifies various classes of immigrants--numerically limited versus numerically exempt and new immigrant versus adjustment of status. A second distinguishing feature is that it utilizes a unique vector of variables relating to the presence and characteristics of various social programs in source countries. The models developed here emphasize the importance of both differential economic advantage and the ease with which a prospective migrant can transfer skills to the U.S. labor market. Hausman-Taylor instrumental variable estimates of the coefficients indicate that in addition to other factors, social programs in source countries are significant determinants of immigration to the USA." Data are from the Immigration and Naturalization Service's Public Use Files.     

相对经济绩效决定因素的分析——以上海市为例

将人均GDP分解为（劳动）生产率、就业率、就业参与率三个经济变量,并把可持续发展的组成部分经济、环境和社会福利因素作为影响上述三个变量的自变量,构建相应的经济计量模型。基于上海市统计数据的分析表明,就业和科研经费的投入仍旧是经济增长的首要因素,但诸如教育、环境和生活质量等福利因素对经济绩效正起到越来越重要的作用。创新之处在于将经常被忽略的环境和福利因素纳入到经济增长的测量中,这对发达地区或城市实现可持续发展具有重要的理论价值。     

农民工地域歧视与就业机会研究

 本文利用2010年人口普查昆山市经济开发区样本检验了户籍类型、外地户籍以及出生地的不同对劳动力进一级市场以及周工作小时数的影响,结果发现：农业户籍劳动力进入一级市场的概率显著低于非农户籍劳动力,前者的周工作小时数明显更长;户籍登记地和出生地的不同对于劳动力能否获得一级市场的就业机会有显著影响,但是出生地的不同所产生的影响要比户籍登记地的不同所产生的影响更明显,这表明对于农民工进入一级市场的地域歧视主要由出生的不同导致的。并且,户籍登记地和出生地的不同在一级和二级市场上对不同类型劳动力的周工作小时数的影响也有所不同。