UK macroeconomic forecasting with many predictors: Which models forecast best and when do they do so?

Block factor methods offer an attractive approach to forecasting with many predictors. These extract the information in these predictors into factors reflecting different blocks of variables (e.g. a price block, a housing block, a financial block, etc.). However, a forecasting model which simply includes all blocks as predictors risks being over-parameterized. Thus, it is desirable to use a methodology which allows for different parsimonious forecasting models to hold at different points in time. In this paper, we use dynamic model averaging and dynamic model selection to achieve this goal. These methods automatically alter the weights attached to different forecasting models as evidence comes in about which has forecast well in the recent past. In an empirical study involving forecasting output growth and inflation using 139 UK monthly time series variables, we find that the set of predictors changes substantially over time. Furthermore, our results show that dynamic model averaging and model selection can greatly improve forecast performance relative to traditional forecasting methods.     

Intrinsic heterogeneity in expectation formation

We introduce the concept of Misspecification Equilibrium to dynamic macroeconomics. Agents choose between a list of misspecified econometric models and base their selection on relative forecast performance. A Misspecification Equilibrium is a stochastic process in which agents forecast optimally given their choices, with forecast model parameters and predictor proportions endogenously determined. Under appropriate conditions, the Misspecification Equilibrium will exhibit Intrinsic Heterogeneity, in which all predictors are used at all times, even in the neoclassical limit in which only the most successful predictors are used. This equilibrium is attainable under least-squares learning and dynamic predictor selection based on average profits.     

Random forests and selected samples

This paper presents a procedure for recovering causal coefficients from selected samples that uses random forests, a popular machine-learning algorithm. This proposed method makes few assumptions regarding the selection equation and the distribution of the error terms. Our Monte Carlo results indicate that our method performs well, even when the selection and outcome equations contain the same variables, as long as the selection equation is nonlinear. The method can also be used when there are many variables in the selection equation. We also compare the results of our procedure with other parametric and semiparametric methods using real data.     

FORECASTING US INFLATION USING DYNAMIC GENERAL‐TO‐SPECIFIC MODEL SELECTION

We forecast US inflation using a standard set of macroeconomic predictors and a dynamic model selection and averaging methodology that allows the forecasting model to change over time. Pseudo out‐of‐sample forecasts are generated from models identified from a multipath general‐to‐specific algorithm that is applied dynamically using rolling regressions. Our results indicate that the inflation forecasts that we obtain employing a short rolling window substantially outperform those from a well‐established univariate benchmark, and contrary to previous evidence, are considerably robust to alternative forecast periods.     

Variable selection problem in the censored regression models

In this paper we discuss the variable selection problem for the censored regression models. The Schme-Hahn (1979) estimator for the censored normal model and the Buckley-James (1979) estimator for the non-parametric censored model are discussed. It is shown, through the EM algorithm, that the variable selection problem for these estimators can be converted into a variable selection problem in a standard linear regression model. We show that the expectation of maximum likelihood residuals converges to zero in large samples.     

Computing open-loop noncooperative solution in discrete dynamic games

The purpose of this paper is to introduce a new algorithm for the approximation of non-quadratic, non-linear open-loop Nash Cournot equilibrium in a difference game of fixed duration (multiperiod) and initial state. The algorithm based on adaptive search procedure called genetic algorithm has been used to optimize strategies for N-person dynamic games. Since genetic algorithms require little knowledge of the problem itself, computations based on these algorithms are very attractive to complex dynamic optimization problems. The empirical evidences are also provided to show the success of the algorithm developed. A typical example in US macroeconomic policy selection for 1933-1936 yields evidence of political inference in the economy.     

电子行业的供应商评价与选择策略

评价和选择适合的供应商是企业实施供应链管理的关键,中国电子制造企业在供应商评选工作方面还存在一些问题。电子行业企业应该根据成本以及对企业的影响这两个维度对所采购项目进行分类,分别确定评价指标权重,并采用不同的方法评选供应商。同时,对于供应商的评选是个动态过程,应该持续进行。     

Dynamic risk management in petroleum project investment based on a variable precision rough set model

In this paper, an adaptive algorithm is designed for dynamic risk management in petroleum project investment based on a variable precision rough set (VPRS) model. In risk management, at each stage of decision-making, experts are invited to identify risk indices and support the decision-maker in evaluating the risk exposure (RE) of individual projects. The VPRS model is used to mine risk rules and determine the significance of risk indices from RE decision tables. Considering that there are multiple risks involved in any petroleum project investment, we use multi-objective programming to obtain the optimal selection of projects with minimum RE, where the significance of risk indices is assigned to each of the corresponding multi-objective functions as a weight. Moreover, we develop a risk ranking model to measure the degree of risk for individual projects in a portfolio. Finally, a numerical example based on a Chinese petroleum company's investments in overseas projects is presented to illustrate the proposed approach, and then conclusions are drawn.     

Can oil prices help predict US stock market returns? Evidence using a dynamic model averaging (DMA) approach

Crude oil price behaviour has fluctuated wildly since 1973 which has a major impact on key macroeconomic variables. Although the relationship between stock market returns and oil price changes has been scrutinized excessively in the literature, the possibility of predicting future stock market returns using oil prices has attracted less attention. This paper investigates the ability of oil prices to predict S&P 500 price index returns with the use of other macroeconomic and financial variables. Including all the potential variables in a forecasting model may result in an over-fitted model. So instead, dynamic model averaging (DMA) and dynamic model selection (DMS) are applied to utilize their ability of allowing the best forecasting model to change over time while parameters are also allowed to change. The empirical evidence shows that applying the DMA/DMS approach leads to significant improvements in forecasting performance in comparison to other forecasting methodologies and the performance of these models are better when oil prices are included within predictors.     

基于Rhino+Grasshopper的风景环境 复杂地形道路选线设计算法模型研究

在风景园林规划设计中，当前使用ArcGIS作为道路自动选线工具的研究较多，但该方法在小区域内无法取得较为精准的选线结果。基于Rhino+Grasshopper平台，对Dijkstra最短路径算法、遗传算法及道路选线影响因素进行研究，构建了参数化风景环境道路自动选线模型。使用参数化设计生成的路径符合风景区低影响开发和低成本建设的原则，且路径曲线最大限度满足风景区道路极限平曲线现行规范值。构建的参数化模型可以解决所有同类型问题且便于调整和优化，为风景环境道路选线提供了新的解决方法。     

A new index of financial conditions

We use factor augmented vector autoregressive models with time-varying coefficients and stochastic volatility to construct a financial conditions index that can accurately track expectations about growth in key US macroeconomic variables. Time-variation in the models׳ parameters allows for the weights attached to each financial variable in the index to evolve over time. Furthermore, we develop methods for dynamic model averaging or selection which allow the financial variables entering into the financial conditions index to change over time. We discuss why such extensions of the existing literature are important and show them to be so in an empirical application involving a wide range of financial variables.     

A medium-N approach to macroeconomic forecasting

This paper considers methods for forecasting macroeconomic time series in a framework where the number of predictors, N, is too large to apply traditional regression models but not sufficiently large to resort to statistical inference based on double asymptotics. Our interest is motivated by a body of empirical research suggesting that popular data-rich prediction methods perform best when N ranges from 20 to 40. In order to accomplish our goal, we resort to partial least squares and principal component regression to consistently estimate a stable dynamic regression model with many predictors as only the number of observations, T, diverges. We show both by simulations and empirical applications that the considered methods, especially partial least squares, compare well to models that are widely used in macroeconomic forecasting.     

Forecasting China's GDP growth using dynamic factors and mixed-frequency data

Forecasting GDP growth is important and necessary for Chinese government to set GDP growth target. To fully and efficiently utilize macroeconomic and financial information, this paper attempts to forecast China's GDP growth using dynamic predictors and mixed-frequency data. The dynamic factor model is first applied to select dynamic predictors among large amount of monthly macroeconomic and daily financial data and then the mixed data sampling regression is applied to forecast quarterly GDP growth based on the selected monthly and daily predictors. Empirical results show that forecasts using dynamic predictors and mixed-frequency data have better accuracy comparing to traditional forecasting methods. Moreover, forecasts with leads and forecast combination can further improve forecast performance.     

Identification of the effects of dynamic treatments by sequential conditional independence assumptions

This paper approaches the dynamic analysis of the effects of training programs for the unemployed in West Germany, or in general the effects of sequences of interventions, from a potential outcome perspective. The identifying power of different assumptions concerning the connection between the dynamic selection process and the outcomes of different sequences is discussed. When participation in the particular sequence of programs is decided period by period depending on its success so far, many parameters of interest are no longer identified. Nevertheless, some interesting dynamic forms of the average treatment effect are identified by a sequential randomization assumption. Based on this approach, we present some new results on the effectiveness of West-German training programs.     

多元化还是专一性更好

高管职业路径多元化还是专一性更能促进企业创新？基于手工整理的CEO职业背景数据，通过静态面板回归、动态DID分析以及工具变量2SLS分析得出，CEO的多元化职业路径更有助于增加企业创新投资；从劳动力市场层面的CEO外部选择视角出发，研究发现，失败容忍机制是二者关系的影响渠道；根据企业特征，将其划分为行业主导者和跟随者后发现，信息学习机制是二者之间的影响渠道。最后，通过从创新投入到产出的全过程检验发现，CEO多元化职业路径通过影响创新投入，有助于提升滞后两年的专利产出数量与质量。     

Determinants of specialist physician ambulatory visits: a neurology example

Background: Economic theory argues that specialization in medicine improves efficiency. Current literature suggests that access to and utilization of specialist care vary widely based on many determinants. Thus, understanding the determinants of specialist physician ambulatory care utilization is integral to healthcare policy.

Objectives: The objective is to investigate the individual and community determinants of specialist ambulatory care utilization—specifically neurologists. The aim was to find predictors of specialist utilization and to identify the particular determinants that can be modified by regulatory or legislative action.

Methods: A large claims database, Truven Health Analytics? Marketscan data, was used from 2007–2010 as the sample. These data are supplemented with data from the American Academy of Neurology (for geographic distribution of neurologists) and the US Census American FactFinder (for community demographic factors). Multivariate regression analysis was run to test the hypotheses. Several robustness tests of our models were included.

Results: Most importantly, neurologists per capita has a meaningful impact on utilization. Additionally, the difference in neurologist usage by neurological condition is an important factor. It was also found that union status, age, comorbidities, and diagnosis are significant individual level determinants, and that the percentage of Hispanic residents and median income are significant community level determinants.

Conclusions: There are two predictors believed to be the most important. The first is the unique neurologists per 1,000 capita variable, which shows a small increase in the number of neurologists would be correlated with a small increase in the probability of seeing a neurologist. We suggest that this is within policymakers’ control, and policymakers should consider this action in the face of the predicted shortage. The second is what appears to be possible sorting by neurologists of patients based on diagnosis – the large difference in the fraction of patients seeing a neurologist by disease.     

基于属性约简的核电企业供应商选择方法研究

作为我国核电行业的改革目标之一,核电供应链体系的构建具有重要意义,其中供应商作为供应链体系的基础,其评价与选择尤为关键。由于核电行业的一些特殊性质,核电企业的供应商评价十分复杂,需要综合考虑供应商交货能力等许多因素。因此使用了粗糙集与神经网络相结合的方法对核电企业的供应商评价指标进行分析,利用粗糙集理论的属性约简过程将冗余属性以及冲突对象删除,然后基于神经网络进行供应商分类,有效降低数据维数以及分类过程的计算复杂度,提高供应商选择的准确性。     

On estimating long-run effects in models with lagged dependent variables

A common procedure in economics is to estimate long-run effects from models with lagged dependent variables. For example, macro panel studies frequently are concerned with estimating the long-run impacts of fiscal policy, international aid, or foreign investment.Our analysis points out the hazards of this practice. We use Monte Carlo experiments to demonstrate that estimating long-run impacts from dynamic models produces unreliable results.Biases can be substantial, sample ranges very wide, and hypothesis tests can be rendered useless in realistic data environments. There are three reasons for this poor performance. First, OLS estimates of the coefficient of a lagged dependent variable are downwardly biased in finite samples. Second, small biases in the estimate of the lagged, dependent variable coefficient are magnified in the calculation of long-run effects. And third, and perhaps most importantly, the statistical distribution associated with estimates of the LRP is complicated, heavy-tailed, and difficult to use for hypothesis testing. While many of the underlying problems have been long-known in the literature, the continued widespread use of the associated empirical procedures suggests that researchers are unaware of the extent and severity of the estimation problems. This study aims to illustrate their practical importance for applied research.     

应急物流条件下的路径选择问题研究

面对日益频发的各种灾害,尤其特大灾害,应急条件下的物流管理越来越受到人们的重视.针对已存的关于应急物流的路径选择问题研究文献中,普遍将物资运输的运输速度视作常量,或者以降低速度,以获得高可靠性,牺牲了应急救灾的宝贵时间,本文将运输过程中灾难对道路在时间上和空间上,以及人们在应对灾难的特殊思维考虑在内,建立起了两个模型,分别通过经典算法Dijkstra标号算法和动态规划法求解.然后以具体的实例,验证了这两种算法可行性和对比之下的优劣性.最后,得出把动态规划思想应用于应急状态下的路径选择问题的研究中是可取的.     

Error correction modelling and dynamic specifications as a conduit to outperforming the random walk in exchange rate forecasting

The proposition that dynamic exchange rate models can outperform the random walk in out-of-sample forecasting, in the sense that they produce lower mean square errors, is examined and disputed. By using several dynamic versions of three macroeconomic exchange rate models, it is demonstrated that dynamic specifications outperform the corresponding static models but improvement in the forecasting power may not be sufficient for the dynamic models to perform better than the random walk. The results are explained by suggesting that any dynamic specification or transformation of the static model leads to the introduction of a lagged dependent variable, which in effect is a random walk component. The analysis leads to the conclusion that it is implausible to aim at beating the random walk by augmenting a static model with a random walk component.