Forecasting Inflation Using Constant Gain Least Squares

This paper assesses the usefulness of constant gain least squares when forecasting inflation. An out‐of‐sample forecast exercise is conducted, in which univariate autoregressive models for inflation in Australia, Sweden, the United Kingdom and the United States are used. The results suggest that it is possible to improve the forecast accuracy by employing constant gain least squares instead of ordinary least squares. In particular, when using a gain of 0.05, constant gain least squares generally outperforms the corresponding autoregressive model estimated with ordinary least squares. In fact, at longer forecast horizons, the root mean square forecast error is reliably lowered for all four countries and for all lag lengths considered in the study.     

Estimation of Structural Nonlinear Errors‐in‐Varibles Models by Simulated Least‐Squares Method

This article proposes a simulation approach to obtain least‐squares or generalized least‐squares estimators of structural nonlinear errors‐in‐variables models. The proposed estimators are computationally attractive because they do not need numerical integration nor huge numbers of simulations per observable. In addition, the asymptotic covariance matrix of the estimator has a simple decomposition that may be used to guide selection of appropriate simulation sizes. The method is also useful for models with missing data or imperfect surrogate covariates, where application of conventional least‐squares and maximum‐likelihood methods is restricted by numerical multidimensional integrations.     

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.     

The Institutional Environment for Economic Growth

This article forges an explicit link between an objective measure of political constraints and variation in cross-national growth rates. It derives a new measure of political constraints from a simple spatial model of political interaction that incorporates information on the number of independent branches of government with veto power and the distribution of preferences across and within those branches. The derived variable is found to have a statistically and economically significant impact on growth rates using simple ordinary least squares, three-stage least squares and generalized method of moments estimation techniques.     

Alternate estimation criteria and measures of forecast accuracy

The purpose of this paper is to investigate and illustrate the effect that alternate estimation criteria have on measured forecast accuracy. In most instances forecast evaluation criteria (error measures) differ from the model estimation criterion, the latter most often being the traditional least squares. The results suggest that forecast accuracy may be improved when criteria other than least squares are used for model estimation purposes.     

Improving forecast accuracy of financial vulnerability: PLS factor model approach

We present a factor augmented forecasting model for assessing the financial vulnerability in Korea. Dynamic factor models often extract latent common factors from a large panel of time series data via the method of the principal components (PC). Instead, we employ the partial least squares (PLS) method that estimates target specific common factors, utilizing covariances between predictors and the target variable. Applying PLS to 198 monthly frequency macroeconomic time series variables and the Bank of Korea's Financial Stress Index (KFSTI), our PLS factor augmented forecasting models consistently outperformed the random walk benchmark model in out-of-sample prediction exercises in all forecast horizons we considered. Our models also outperformed the autoregressive benchmark model in short-term forecast horizons. We expect our models would provide useful early warning signs of the emergence of systemic risks in Korea's financial markets.     

A test of Hotelling’s Valuation Principle for nonrenewable resources

A recent study by Cairns and Davis (1998) tested and rejected the Hotelling Valuation Principle (HVP) using cross-sectional data on gold mines. But a replication of that study using the same data suggests the presence of heteroscedastic errors. In contrast to the results of ordinary least squares regressions, robust estimation and weighted least squares results indicate that the HVP may not be rejected at conventional levels of significance. Moreover, the alternative valuation equations proposed by Cairns and Davis can require additional and often unavailable information regarding mineral production. Matched pairs tests indicate that prediction accuracy is roughly comparable across all of the equations examined.Revisions to this paper were completed during a sabbatical at the Life Cycle Institute in the Catholic University of America. I thank Ernest M. Zampelli, Philip Pfaff, two anonymous referees, and the editors for helpful comments. Any errors are my own.First version received: May 2003/Final version received: April 2004     

A new estimator for trade costs and its small sample properties

This paper discusses the estimation of parameters of a traditional transportation model, as it is typically present in so-called Takayama–Judge type spatial price equilibrium models. In contrast to previously used estimation methods, observations of regional prices as well as of trade costs are used in a direct estimation of the first order conditions. The proposed method uses bi-level programming techniques to minimize a weighted least squares criterion under the restriction that the estimated parameters satisfy the Kuhn–Tucker conditions for an optimal solution of the transport model. A penalty function and a smooth reformulation are used to iteratively approximate the complementary slackness conditions. Monte-Carlo simulations are used to trace out some properties of the estimator and compare it with a traditional calibration method. The analysis shows that the proposed technique estimates prices as well as trade costs more precisely than the traditional calibration method. It is suggested to apply the same method to a range of linear and quadratic models.     

面向国土景观风貌管控的中国国家 公园空间布局研究

中国国家公园作为自然保护地的主体,在国土尺度 下保护具有代表性景观风貌发挥着重要作用。中国国家公园体 制建设尚处于起步阶段,国家公园的空间布局成为体制建设的 核心问题。合理规划中国国家公园整体空间布局,有助于在国 土尺度下进行景观风貌管控。景观风貌是一定范围内景观特征 的反映。通过引入景观特征评估方法,制定了用于遴选国家公 园的综合区划方案,分析了国家公园建设潜在区域,并在此基 础上结合分析各国国家公园的面积、离散度等指标,对中国 国家公园空间布局关键指标进行限定,以期对中国国家公园 的整体空间布局提供参考。主要结论如下：1)中国景观特征 综合区划方案包括7个一级景观特征区域、115个二级景观特 征区;2)国土面积的25.12%可以作为国家公园建设潜在区 域;3)建议国家公园设立的数量为65~133个,国家公园总面 积占国土面积的5.23%~10.70%。     

Semiconductor industry cycles: Explanatory factors and forecasting

This paper aims to suggest the best forecasting model for the semiconductor market. A wide range of alternative modern econometric modeling approaches have been implemented, and a large variety of criteria and tests have been employed to assess the out-of-sample forecasting accuracy at various horizons. The results suggest that if a VECM can be an interesting source of information, the Bayesian models are superior forecasting tools compared to univariate and unrestricted VAR models. However, for decision makers a spectral method could be a useful tool, which can be easily implemented. In addition, MS-AR models make it possible to obtain valuable forecasts on turning-points in order to adjust the programming of heavy capital and research investments.     

可持续成长模型下的上市公司财务危机预警

本文依据6种财务可持续成长模型提炼预警指标,基于2003～2007年我国A股市场新增ST公司样本及配对样本的前3年数据,对各模型的驱动因素进行综合预处理,分别得到ST前1～3年的预警指标体系。然后,建立预警模型进行回判和外推。结果表明,各种分析方法在ST前3年可获得较高的判断准确性;财务危机与可持续成长驱动指标之间存在着高度的非线性关系;可持续成长模型能够为开发标准化预警指标体系提供依据。     

城市公园的多维可达性动态评估研究

城市公园的可达性评估除了基于交通(距离、时间、 成本)的可达性外,视觉可达性、心理可达性也是度量居民对 公园服务获得感的重要内容。从交通可达性、视觉可达性和心 理可达性3个维度,构建可达性评价模型和计算方法。选取重 庆市3个城市综合公园作为不同发展时期的代表,对距离公园 边界1km区域内进行实证研究,结果表明：早期到发展期, 3个维度的可达性均明显增加;至成熟期,交通可达性趋于稳 定,视觉可达性下降,心理可达性持续增加。在空间分布上, 交通可达性和心理可达性呈现随距离增加而衰减的趋势,视觉 可达性在不同方位上差异显著;综合可达性由早期的“同心 圆”模式分布逐渐演变为“偏向”式聚集,在某些方位上呈现 跳跃式分布。多维可达性的动态评估能有效挖掘城市公园的潜 在服务功能和价值,客观评估城市公园布局的公平性和居民 对公园的可获得性,让更多的人包括弱势群体能够共享环境 福利,为城市公园周边地区的规划建设和旧城更新提供科学 依据。     

我国主题公园产业的时空布局与理性发展研究

由于各地盲目效仿、缺乏整体布局、增长无序化等原因,我国于上世纪90年代中后期上马的主题公园如同被推倒的多米诺骨牌,绝大多数已走向衰败,甚至关门歇业。在最近几年房地产倍受打压的背景之下,旅游地产商借机圈地,我国主题公园大有＂二次发展＂之势。因此探寻我国主题公园产业的时空布局规律与理性发展已成为该行业健康发展的当务之急。本文以全国300个A级主题公园为研究样本,运用最邻近点指数、Voronoi多边形面积变异系数、基尼系数等研究方法,大尺度大范围分析了中国主题公园的空间分布特征,揭示了我国主题公园在空间上表现为强凝聚分布,在七大地理分区中高度集中,分布的均匀度低,即分布的无序性大,区域布局不合理。最后,运用信息熵度量法对我国主题公园产业空间分布结构进行优化诊断,初步提出了我国主题公园空间布局的优化模型,模拟计算得出各大区目前主题公园实际值与拟合值的误差,从而为下一步国家的宏观调控和理性发展提供决策依据和政策建议。     

Exports and growth: a causality analysis for the pulp and paper industries based on international panel data

This study examines the causal relations between exports and domestic production in the pulp and paper industries. The issue is whether exports are the engine of growth, or whether exports follow growth. The data were time-series of the 15 main exporting countries between 1961 and 1995. The method was Granger-causality analysis with error correction, based on models estimated in three ways: ordinary least squares by country, least squares with dummy variables (LSDV), and seemingly unrelated regression. Regardless of method, the strongest relation was an instantaneous (within a year) feedback between exports and production. The LSDV results implied average multipliers across countries of 1.2 to 1.4 from exports to production, and 0.20 to 0.25 from production to exports, in both industries. Experiments with monthly data on the pulp industries of Canada and the USA showed that temporal aggregation could affect the Granger-causality test results.     

Panel data analysis of multi-factor capital asset pricing models

In this article, we propose MFCAPM panel models with fixed effects and test theories associated with risk exposures and anomalies postulated by Fama and French, and we assess their out-of-sample predictive performances. Based on the portfolios formed by French, we construct 10 panel models, each consisting of 10 portfolios grouped by size deciles, and another 10 panels by value deciles. In the presence of cross-section dependence, the MFCAPM panel model is estimated by the feasible generalized least squares (FGLS) method for the sample period 1963(1)-2018(9). The results show that the market, firm-size and value risk exposures are significant and robust across three-, five- and six-factor panel models. Significant time-fixed effects indicate that there are several portfolios resilient to dot.com bubble peak in 2000, while some others resilient to GFC in 2007. We estimate the models for the in-sample period 1963(1)–1999(12) and generate the out-of-sample portfolio returns for the period 2000(1)–2018(9). We find that portfolio returns forecasts generated by the six-factor panel model are superior to other MFCAPM panel models, mostly due to the momentum factor (investor behaviour) explaining large return variations and volatility exposures. The findings have implications for investors, security traders and portfolio risk managers.     

中关村科技园区创新质量的时空集聚效应研究

创新作为中关村科技园区的基本属性，创新质量决定了中关村科技园区发展质量。科技园区促进区域产业集聚，集聚显著影响科技园区创新，但缺乏对“一区多园”创新质量的综合集聚效应研究。由此，建立中关村科技园区创新质量指标体系，在分析中关村科技园区3种集聚模式的基础上，建立集聚模式对创新质量影响的动态空间面板模型；以2006—2017年中关村科技园区等相关数据为基础，研究不同集聚模式与创新质量的时空效应并对其进行分解分析。结果表明，中关村科技园区创新质量呈现显著时间循环效应和空间溢出效应，各园区创新质量空间集聚存在高-高、低-低等模式并随着时间推移发生跃迁。所有园区受到正向显著的专业化集聚影响，大兴-亦庄园受到负向显著的多样化集聚影响，而房山园、密云园和延庆园受到的多样化集聚影响不显著，海淀园、朝阳园、大兴-亦庄园、东城园、西城园、通州园、门头沟园和顺义园受到市场竞争集聚效应的影响。     

Predicting land use allocation in France: A spatial panel data analysis

Predictions of future land use areas are an important issue as land use patterns significantly impact environmental conditions (biodiversity, water pollution, soil erosion, and climate change) as well as economic and social welfare. In order to improve the prediction accuracy of aggregated land use share models, we propose in this paper a methodological contribution by controlling for both unobserved individual heterogeneity and spatial autocorrelation. Our model is a land use shares model applied to aggregated data in France. Our dataset is a panel which covers both time series observations from 1992 to 2003 and cross-sectional observations by Département (equivalent to NUTS3 regions). We consider four land use classes: (1) agriculture, (2) forest, (3) urban and (4) other use. We investigate the relation between the areas in land in different alternative uses and economic and demographic factors influencing land use decisions. Based on the comparison of prediction accuracy of different model specifications, our findings are threefold: First, controlling for both unobserved individual heterogeneity and spatial autocorrelation outperforms any other specification in which spatial autocorrelation and/or individual heterogeneity are ignored. Second, accounting for cross-equation correlations does not seem to improve the prediction performances and finally, ignoring individual heterogeneity introduces substantial loss in prediction accuracy.     

A novel time-series model based on empirical mode decomposition for forecasting TAIEX

Stock price prediction is regarded as a challenging task of the financial time series prediction process. Time series models have successfully solved prediction problems in many domains, including the stock market. Unfortunately, there are two major drawbacks in stock market by time-series model: (1) some models cannot be applied to the datasets that do not follow the statistical assumptions; and (2) most time-series models which use stock data with many noises involutedly (caused by changes in market conditions and environments) would reduce the forecasting performance. For solving the above problems and promoting the forecasting performance of time-series models, this paper proposes a hybrid time-series support vector regression (SVR) model based on empirical mode decomposition (EMD) to forecast stock price for Taiwan stock exchange capitalization weighted stock index (TAIEX). In order to evaluate the forecasting performances, the proposed model is compared with autoregressive (AR) model and SVR model. The experimental results show that the proposed model is superior to the listing models in terms of root mean squared error (RMSE). And the more fluctuation year (2000–2001) occurs, the better accuracy of proposed model will be obtained.     

Globalization,decentralization and income inequality: The case of China

The current study examines the effect of trade liberalization and expansion of foreign direct investment inflows, together with the pursuit of decentralization, on China's income inequality between 1985 and 2007. As the degrees of integration of the concerned variables are revealed to be different,Stock and Watson (1993) dynamic ordinary least squares method is employed to reveal the cointegrating relationship. The empirical evidence shows that trade liberalization has led to the higher income inequality, discrediting the Stolper–Samuelson theorem in international trade. There is mixed evidence relating to the effect of FDI inflows on income inequality. Decentralization is shown not to influence it.     

Foreign direct investment and business cycle co-movements: The panel data evidence

The previous literature has largely overlooked the possible channels through which foreign direct investment (FDI) might influence business cycle synchronization. In this study we analyze the linkages that exist among FDI, trade and industrial dissimilarity in relation to business cycle co-movements using a panel data set taken from 77 pairs of developed countries. The error component three-stage least squares (EC3SLS) estimates from a simultaneous equations model with panel data are shown to be superior to the estimates obtained from single equation models or simultaneous equations models with cross-sectional data. Our results indicate that FDI serves as a channel of international business cycle transmission that is equally important as the channels of trade and monetary policy. On the contrary, industrial dissimilarity is identified as having an indirect impact on the business cycle correlation through trade and FDI. Furthermore, our findings suggest that in our sample FDI is of the horizontal type and tends to substitute for trade.