A large factor model for forecasting macroeconomic variables in South Africa

This paper uses large Factor Models (FMs), which accommodate a large cross-section of macroeconomic time series for forecasting the per capita growth rate, inflation, and the nominal short-term interest rate for the South African economy. The FMs used in this study contain 267 quarterly series observed over the period 1980Q1-2006Q4. The results, based on the RMSEs of one- to four-quarter-ahead out-of-sample forecasts from 2001Q1 to 2006Q4, indicate that the FMs tend to outperform alternative models such as an unrestricted VAR, Bayesian VARs (BVARs) and a typical New Keynesian Dynamic Stochastic General Equilibrium (NKDSGE) model in forecasting the three variables under consideration, hence indicating the blessings of dimensionality.     

关于建立现代会计信息系统的探索

本文阐述了现代信息技术、新经济环境、现代管理思想、人们的信息需求、现代会计职能等的内容与特点;在此基础上,指出了现代会计信息系统应具有的特征。     

On exact D-optimal designs with 2 two-level factors and n autocorrelated observations

In this paper we consider the exact D-optimal designs for estimation of the unknown parameters in the two factors, each at only two-level, main effects model with autocorrelated errors. The vector of the n random errors in the observed responses is assumed to follow a first-order autoregressive model (AR(1)). The exact D-optimal designs seek the optimal combinations of the design levels as well as the optimal run orders, so that the determinant of the information matrix of BLUEs for the unknown parameters is maximized. Bora-Senta and Moyssiadis (1999) gave some conjectures about the exact D-optimal designs based on their experience of several exhaustive searches. In this paper their conjectures are partially proved to be true.Received: January 2003 / Accepted: October 2003Partially supported by the National Science Council of Taiwan, R.O.C. under grant NSC 91-2115-M-008-013.Supported in part by the National Science Council of Taiwan, R.O.C. under grant NSC 89-2118-M-110-003.     

审计判断的成因及其在审计过程中的应用

审计专业判断是审计人员职业道德、胜任能力和专业经验的有机统一和外在表现,当审计人员面对复杂审计事项和不确定审计环境时,正确的审计判断是审计人员能否作出合理认识、评价、预测和决策的必要条件,也是审计能否成功的关键因素。本文从审计判断的特征、成因入手,探讨了在现代审计模式下审计判断在审计过程中的应用问题。     

[火用]经济学思想在供应链管理中的应用

在[火用]经济学分析的基础上,综合考虑环境因素对供应链系统的影响,引入了环境负效应因子的概念,并运用系统科学的广义子系统理论,对供应链系统中的各股[火用]流进行分析,建立了供应链广义子系统[火用]经济学模型.     

中国物流市场竞争因素分析

围绕影响市场重新分割的4个主要因素:品牌效应、客户需求导向、价格因素以及技术应用程度,分析了中国物流市场的发展变化趋势。     

机电设备招标中的综合评议法

根据国家有关法规对招标评标方法的原则要求,结合实际工作的经验,说明和分析了几种常见的评标方法及其适用范围,同时提出一种定量定性综合衡量的评标方法,即综合评议法。     

Forecasting macroeconomic time series: LASSO-based approaches and their forecast combinations with dynamic factor models

In a data-rich environment, forecasting economic variables amounts to extracting and organizing useful information from a large number of predictors. So far, the dynamic factor model and its variants have been the most successful models for such exercises. In this paper, we investigate a category of LASSO-based approaches and evaluate their predictive abilities for forecasting twenty important macroeconomic variables. These alternative models can handle hundreds of data series simultaneously, and extract useful information for forecasting. We also show, both analytically and empirically, that combing forecasts from LASSO-based models with those from dynamic factor models can reduce the mean square forecast error (MSFE) further. Our three main findings can be summarized as follows. First, for most of the variables under investigation, all of the LASSO-based models outperform dynamic factor models in the out-of-sample forecast evaluations. Second, by extracting information and formulating predictors at economically meaningful block levels, the new methods greatly enhance the interpretability of the models. Third, once forecasts from a LASSO-based approach are combined with those from a dynamic factor model by forecast combination techniques, the combined forecasts are significantly better than either dynamic factor model forecasts or the naïve random walk benchmark.     

Bayesian forecasting and portfolio decisions using dynamic dependent sparse factor models

We extend the recently introduced latent threshold dynamic models to include dependencies among the dynamic latent factors which underlie multivariate volatility. With an ability to induce time-varying sparsity in factor loadings, these models now also allow time-varying correlations among factors, which may be exploited in order to improve volatility forecasts. We couple multi-period, out-of-sample forecasting with portfolio analysis using standard and novel benchmark neutral portfolios. Detailed studies of stock index and FX time series include: multi-period, out-of-sample forecasting, statistical model comparisons, and portfolio performance testing using raw returns, risk-adjusted returns and portfolio volatility. We find uniform improvements on all measures relative to standard dynamic factor models. This is due to the parsimony of latent threshold models and their ability to exploit between-factor correlations so as to improve the characterization and prediction of volatility. These advances will be of interest to financial analysts, investors and practitioners, as well as to modeling researchers.     

A new look at variation in employment growth in Canada: The role of industry,provincial, national and external factors

We examine fluctuations in employment growth using Canadian data from 1976 to 2010. We consider a wide range of models and examine the sensitivity of our findings to modelling assumptions. The results from our most preferred model, which we selected using the Bayesian Information Criteria, indicate that most of the variance in employment growth that is not due to the idiosyncratic error comes from domestic sources, with most of this coming from industry and provincial factors. Overall, we find that external and national factors play a much smaller role in employment fluctuations than in earlier research. We provide some possible explanations for these differences and discuss the implications of our findings for public policy and theory.