Conditional Markov chain and its application in economic time series analysis

Motivated by the great moderation in major US macroeconomic time series, we formulate the regime switching problem through a conditional Markov chain. We model the long‐run volatility change as a recurrent structure change, while short‐run changes in the mean growth rate as regime switches. Both structure and regime are unobserved. The structure is assumed to be Markovian. Conditioning on the structure, the regime is also Markovian, whose transition matrix is structure‐dependent. This formulation imposes interpretable restrictions on the Hamilton Markov switching model. Empirical studies show that this restricted model well identifies both short‐run regime switches and long‐run structure changes in the US macroeconomic data. Copyright © 2010 John Wiley & Sons, Ltd.     

基于门限协整系统的预测方法研究

某些非平稳时间序列，它们在整个区间上并不存在线性协整关系，而存在非线性协整关系。作者运用门限协整的概念，处理非线性协整问题，把整个区间检验为若干个子区间，分别在每个区间上进行线性协整分析。文中讨论了滚动预测方法。最后，通过上证和深证A股指数的门限协整分析，验证了门限协整系统在预测中的优越性。     

Career Management in High‐Performing Organizations: A Set‐Theoretic Approach

The aim of this study is to investigate the elements of organizational career management (OCM) that can lead to strong organizational performance. The growing unpredictability of careers requires a different organizational approach of careers. Yet, new career models all focus on the individual as the central actor, leaving the role of the organization rather underdeveloped. Based on a combined perspective integrating insights from the literature on careers, high performance work systems, and idiosyncratic deals (I‐deals), we address four dimensions of OCM: supportive and developmental practices, development I‐deals, individual responsibility, and consensus. We study their relationships with company performance, thereby including the firm's human capital composition. Surveys were administered to the HR directors of 293 organizations. We apply a relatively new method, fsQCA (fuzzy‐set qualitative comparative analysis), and complement this with more conventional structural equation modeling (SEM). The SEM analyses suggest that only supportive and developmental practices are positively associated with high performance. However, based on the fsQCA, three configurations are identified in which OCM is associated with high performance. The most prevalent configuration combined supportive and developmental practices with I‐deals and individual responsibility for career management. We conclude with a discussion of the implications of our findings, and address the utility of adopting a configurational approach in career research. © 2016 Wiley Periodicals, Inc.     

Contextual analyses with QCA-methods

Contextual analyses are essential in comparative research, as they investigate the importance of contextual conditions for causal relationships. During the last decades, an increasing number of comparative studies have also focused on how contextual conditions affect causal relationships. At the same time, new comparative methods have been developed based on set-theoretical logics. Two of the most prominent methods are csQCA and fsQCA, which are used in comparative studies with increasing frequency. However, the conventional design for contextual analysis is still based on quantitative methods and the use of interaction-factors. This article discusses why the use of interaction-factors is not suitable together with QCA-methods. Instead of the conventional design, the article presents an alternative design for contextual analyses with QCA-methods grounded on subgroup-design. Based on one recently-developed methodology comparative multilevel analysis (CMA), some guidelines for performing contextual analyses with two set-theoretical methods (csQCA and fsQCA) are presented. As illustrated with examples, the combination of CMA and QCA provides opportunities to use QCA for contextual analysis.     

Does the interaction between the knowledge management process and sustainable development practices boost corporate green innovation?

Green innovations are being deployed in manufacturing industries to promote organisational sustainability by embracing sustainable development practices (SDPs). However, little is known about how corporate green innovation (CGI) is influenced by the knowledge management process (KMP). To fill this gap, we have developed a multidimensional framework based on the resource-based view (RBV) theory that provides a foundation for sculpturing the process by which KMP was observed to capture and sustain CGI through SDPs. Data were collected from 393 respondents of large- and medium-sized manufacturing corporations in Pakistan and analysed using partial least squares structural equation modelling (SEM) and fuzzy set qualitative comparative analysis (fsQCA). This study provides several key findings. First, KMP dimensions (acquisition, dissemination and application) significantly improve the SDPs' dimensions (environment, economic and social). Second, SDP dimensions play a significant role in achieving CGI. Third, the implementation of SDPs partially mediates the relationship between the KMP and CGI. Furthermore, the fsQCA results signify the robustness of all integrated constructs. Our results demonstrate that investing in and adopting the latest technologies and sustainable practices are not only valuable for long-term success but the soft concerns such as managing organisational knowledge are also vital in the current knowledge-based economy. Finally, in light of our findings, theoretical and managerial implications, with propositions for future studies, have been provided at the end of the paper.     

Causal complexity for passengers’ intentions to re-ride

This study explores the relationship among service range, ride convenience, motivation to ride, satisfaction with facilities, service satisfaction, and intention to re-ride. The conveyance of interest is the Puyuma Express because its transport service last year carries out the overall electrified era in eastern Taiwan and its vast passenger capacity breaks the record over the past 128 years. The very high passenger capacity includes passengers who are on their first ride but, to a much greater extent, passengers who are re-riding. This fact motivates this research. This work involves a carefully designed questionnaire, and uses both web-based and paper-based questionnaire surveys to collect data. The number of valid samples thus obtained was 228. Multiple regression analysis (MRA) and fuzzy set qualitative comparative analysis (fsQCA) are utilized to test proposed research framework. The two methods yield completely different results. MRA reveals that satisfaction with facilities, service satisfaction, ride convenience, and service range are together responsible for the strong intentions of passengers to re-ride. FsQCA identifies seven complex antecedent paths that are responsible for strong intentions to re-ride. The latter explains outcome more comprehensively than the former. The motivation to ride is an important condition in fsQCA and should not be eliminated as MRA suggests. This empirical study elucidates the causal complexity for passengers’ intentions to re-ride, and contributes to theory and practice in the domains of both transportation marketing and consumer behavior.     

Co-alignment of supplier quality management practices and cognitive maps – A neo-configurational perspective

Much of the previous research on the ‘purchasing practice–performance link’ rests on the notion of “net effects,” which assumes that specific practices independently of each other impact outcomes. This study challenges this core tenet by adopting a neo-configurational perspective, exploring how different combinations of factors (called “configurations”) affect outcomes. Another limitation of extant studies on the ‘purchasing practice–performance link’ is the narrow focus on practices; more recent behavioral supply management research finds the behavior of managers to be critically influenced by cognitive maps—the lenses through which managers perceive, simplify, and interpret the world. Focusing on supplier quality as a core aspect of the broader ‘purchasing practice–performance link’, this study explores how configurations of different supplier quality management (SQM) practices and SQM-related cognitive maps help firms to manage supplier quality. It uses fuzzy-set qualitative comparative analysis (fsQCA) to identify SQM configurations and a cognitive-linguistic approach for the computerized text analysis of purchasing managers’ cognitive maps in the context of Western companies sourcing from Chinese suppliers. The configurational fsQCA analysis identifies four different configurations associated with overcoming barriers to SQM in emerging markets. SQM-related cognitive maps prove to be a critical component in these four configurations.     

Introducing the Bank of Canada staff economic projections database

We present a new, publicly available database of real-time data and forecasts from the Bank of Canada's staff economic projections, which will be updated on an annual basis. We describe the data construct, its variables, coverage, and frequency. We then provide a forecast evaluation for gross domestic product (GDP) growth, consumer price index (CPI) inflation and the policy rate since 1982: We compare the staff's forecasts with those from commonly used time series models estimated with the real-time data, and with forecasts from other professional forecasters, and provide standard bias tests. Finally, we study changes in predictability of the Canadian economy following the announcement of the inflation-targeting regime in 1991. Our data set is unprecedented outside the USA, and our evidence is particularly interesting, as it covers over 30 years of staff forecasts, two severe recessions, and different monetary policy regimes.     

Selecting explanatory factors of voting decisions by means of fsQCA and ANN

This investigation applies fuzzy-set qualitative comparative analysis (fsQCA) and an artificial neural networks method (ANN) with the aim of addressing the determinants of votes regarding managerial proposals presented in corporate meetings. The data refer to companies in the United States banking industry and they cover the period from 2003 to 2013. The results show that the variables that contribute to explain the voting support have changed over time. Thus, during the 2003–2006 sub-period the number of funds voting appears as the most clearly outstanding variable. On the contrary, in the 2007–2009 sub-period there is a heterogeneous set of explanatory features that includes the total volume of assets, the leverage ratio and the return on assets ratio, among others, as the most remarkable factors. Finally, in the 2010–2013 sub-period, there are no specific features or combinations that contribute to voting support, indicating that the explanatory factors are yet to be consolidated after the financial downturn.     

Integrating qualitative and quantitative data: index creation using fuzzy-set QCA

This paper demonstrates the use of fuzzy-set Qualitative Comparative Analysis (fsQCA) for index creation. It uses the example of economic, including development, policy towards Latin America in Germany, Spain and the United Kingdom as an example of how fsQCA can be deployed in order to integrate qualitative and numerical evidence into a coherent, comparable framework. Moreover, it shows the usefulness of an index based on fsQCA in the comparison of cases that have strong national idiosyncrasies, making them difficult to compare. FsQCA therefore increases the researcher’s ability to combine different types of data from various backgrounds, making it a useful indexing tool for small to medium-N research in the social sciences.     

基于ARMA模型的上海市人均GDP时间序列分析与预测

本文分析了上海市人均GDP水平时间序列(1978年至2008年),在将数据平稳化的基础上建立自回归移动平均模型(ARMA模型),从中找出上海市人均GDP序列变化的规律,并在此基础上对未来几年的指标数值进行了预测。     

The asymmetry of judgemental confidence intervals in time series forecasting

In forecasting a time series, one may be asked to communicate the likely distribution of the future actual value, often expressed as a confidence interval. Whilst the accuracy (calibration) of these intervals has dominated most studies to date, this paper is concerned with other possible characteristics of the intervals. It reports a study in which the prevalence and determinants of the symmetry of judgemental confidence intervals in time series forecasting was examined. Most prior work has assumed that this interval is symmetrically placed around the forecast. However, this study shows that people generally estimate asymmetric confidence intervals where the forecast is not the midpoint of the estimated interval. Many of these intervals are grossly asymmetric. Results indicate that the placement of the forecast in relation to the last actual value of a time series is a major determinant of the direction and size of the asymmetry.     

Time Series Concepts for Conditional Distributions*

The paper asks the question – as time series analysis moves from consideration of conditional mean values and variances to unconditional distributions, do some of the familiar concepts devised for the first two moments continue to be helpful in the more general area? Most seem to generalize fairly easy, such as the concepts of breaks, seasonality, trends and regime switching. Forecasting is more difficult, as forecasts become distributions, as do forecast errors. Persistence can be defined and also common factors by using the idea of a copula. Aggregation is more difficult but causality and controllability can be defined. The study of the time series of quantiles becomes more relevant.     

广西水电设计院第五栋宿舍楼的变形观测与预报

文章研究了利用时间序列分析方法ARMA（p，q）模型建立变形监测分析及预报的方法，并对时间序列分析方法的应用进行初步探讨。同时，在理论研究的基础上，对具体变形点进行时间序列分析，运用3种模型进行变形观测数据的解算预测。     

Forecasting levels of log variables in vector autoregressions

Sometimes forecasts of the original variable are of interest, even though a variable appears in logarithms (logs) in a system of time series. In that case, converting the forecast for the log of the variable to a naïve forecast of the original variable by simply applying the exponential transformation is not theoretically optimal. A simple expression for the optimal forecast under normality assumptions is derived. However, despite its theoretical advantages, the optimal forecast is shown to be inferior to the naïve forecast if specification and estimation uncertainty are taken into account. Hence, in practice, using the exponential of the log forecast is preferable to using the optimal forecast.     

Forecasting GDP over the Business Cycle in a Multi‐Frequency and Data‐Rich Environment

This paper merges two specifications recently developed in the forecasting literature: the MS‐MIDAS model (Guérin and Marcellino, 2013) and the factor‐MIDAS model (Marcellino and Schumacher, 2010). The MS‐factor MIDAS model that we introduce incorporates the information provided by a large data set consisting of mixed frequency variables and captures regime‐switching behaviours. Monte Carlo simulations show that this specification tracks the dynamics of the process and predicts the regime switches successfully, both in‐sample and out‐of‐sample. We apply this model to US data from 1959 to 2010 and properly detect recessions by exploiting the link between GDP growth and higher frequency financial variables.     

对经济增长的时间序列分析

时间序列分析在经济运用中作用十分明显。利用1980～2003年国内生产总值的相关资料,运用时间序列分析,应用SAS软件对经济增长时间序列进行模型识别、拟合、估计和预测,预测结果较为满意。而改革开放以来,投资在经济增长中的作用越来越明显,在对经济增长序列进行时间序列分析的同时,也结合回归分析建立经济增长和投资的回归-时间序列组合模型来进行分析。     

Improving forecast stability using deep learning

In this paper, we define forecast (in)stability in terms of the variability in forecasts for a specific time period caused by updating the forecast for this time period when new observations become available, i.e., as time passes. We propose an extension to the state-of-the-art N-BEATS deep learning architecture for the univariate time series point forecasting problem. The extension allows us to optimize forecasts from both a traditional forecast accuracy perspective as well as a forecast stability perspective. We show that the proposed extension results in forecasts that are more stable without leading to a deterioration in forecast accuracy for the M3 and M4 data sets. Moreover, our experimental study shows that it is possible to improve both forecast accuracy and stability compared to the original N-BEATS architecture, indicating that including a forecast instability component in the loss function can be used as regularization mechanism.     

A resource orchestration view of supply chain traceability and transparency bundles for competitive advantage

The literature has mostly examined supply chain (SC) traceability and SC transparency separately, ignoring the mutually constitutive relationship of these two related constructs. We draw on the resource orchestration theory and the causal complexity perspective to conceptualize and validate SC traceability and SC transparency as interrelated organizational capabilities that may mutually enhance or compensate each other for competitive advantage. We constructed an original sample from two sources to empirically test this conceptualization using fuzzy set qualitative comparative analysis (fsQCA). Our empirical results reveal that the ability of firms to leverage SC traceability for a high financial performance is contingent upon creating a transparency perception of SC with a wide range of stakeholders. Our results also identified the firm size and its international presence, as having a significant bearing on the ability of firms to leverage SC traceability and SC transparency capabilities for a competitive advantage.