FORECASTING INFLATION USING DYNAMIC MODEL AVERAGING*

We forecast quarterly US inflation based on the generalized Phillips curve using econometric methods that incorporate dynamic model averaging. These methods not only allow for coefficients to change over time, but also allow for the entire forecasting model to change over time. We find that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more sophisticated approaches such as those using time varying coefficient models. We also provide evidence on which sets of predictors are relevant for forecasting in each period.     

Assessing the Transmission of Monetary Policy Using Time‐varying Parameter Dynamic Factor Models*

This article extends the current literature which questions the stability of the monetary transmission mechanism, by proposing a factor‐augmented vector autoregressive (VAR) model with time‐varying coefficients and stochastic volatility. The VAR coefficients and error covariances may change gradually in every period or be subject to abrupt breaks. The model is applied to 143 post‐World War II quarterly variables fully describing the US economy. I show that both endogenous and exogenous shocks to the US economy resulted in the high inflation volatility during the 1970s and early 1980s. The time‐varying factor augmented VAR produces impulse responses of inflation which significantly reduce the price puzzle. Impulse responses of other indicators of the economy show that the most notable changes in the transmission of unanticipated monetary policy shocks occurred for gross domestic product, investment, exchange rates and money.     

Cointegration modeling of interrelated factor demands: With an application to labor–import substitution in the European Union

In this paper we present techniques for cointegration modeling of interrelated factor demands. These techniques respect the non-stationary character of the price and quantity data, and permit specification of general, dynamic factor demand models based on error correction forms derived from cointegration. Therefore, we do not have to assume ad hoc dynamic forms. Moreover, we ensure that estimated relations are structural, and not spurious. Cointegrating vectors are estimated subject to all standard economic theory restrictions by using a procedure, which we call dynamic SUR. We show how consistent error correction models can be specified and estimated. In addition, we test the neoclassical restrictions both in the short- and the long run. The new methods are used to shed light on the major problem of the European Union (unemployment) and its relationship with imports. The empirical analysis is conducted for five countries of the European Union with an emphasis to the south: The UK, France, Greece, Italy, and Spain.     

Integrating machine learning and workflow management to support acquisition and adaptation of workflow models

Current workflow management systems (WFMS) offer little aid for the acquisition of workflow models and their adaptation to changing requirements. To support these activities we propose to apply techniques from machine learning, which enable an inductive approach to workflow acquisition and adaptation. We present a machine learning component that combines two different machine learning algorithms: the first induces the structure of sequential workflows and the second is responsible for the induction of transition conditions. The second task can be solved by applying standard decision rule induction algorithms. In this contribution we focus mainly on the algorithms for the first task. For this purpose we describe two algorithms based on the induction of hidden Markov models. The first algorithm is a bottom‐up, specific‐to‐general algorithm and the other applies a top‐down, general‐to‐specific strategy. Both algorithms have been implemented in a research prototype. In six scenarios we evaluate and compare the two algorithms experimentally. The induced workflow models can be imported by the business process management system ADONIS. Copyright © 2000 John Wiley & Sons, Ltd.     

Economies of scale,cost minimization and productivity in telecom markets under economic crisis: evidence from Greece

Telecommunications has entered a new age of development with advanced technology and increased competition with established players. The main focus of telecom operators is to maintain and increase their market shares by creating a loyal customer base and technology advancements. However, it remains difficult for telecom operators to evaluate their performance and competence within a fast-changing environment, especially since the telecom business is complex and involves unknown trade-offs. The current study proposes a series of methods in order to evaluate the performance of telecommunication companies, using as a case study the Hellenic Telecommunications Organization (OTE). OTE’s importance lies on its role as one of the leading companies in Greece affecting the economy. On the methodological side, this paper applies the linear regression analysis to measure and evaluate the performance of OTE. The results show that during the period 2005-2015 there are events which affect the operation of OTE and create conditions for cost optimization which are of great importance and they determine the future evolution of the organization. The results of this study are expected to be utilized as guidelines for the telecom operators as well as for regulators and decision makers in general.     

Mixed Poisson Distributions

Mixed Poisson distributions have been used in a wide range of scientific fields for modeling nonhomogeneous populations. This paper aims at reviewing the existing literature on Poisson mixtures by bringing together a great number of properties, while, at the same time, providing tangential information on general mixtures. A selective presentation of some of the most prominent members of the family of Poisson mixtures is made.     

What drives acquisitions?: Market valuations and bidder performance

Given the recent theoretical development that documents stock market misvaluations’ driven acquisition, this paper examines the relation between market valuations and bidder performance. We focus on hot stock markets and find that bidder reactions to mergers, in both the short- and long-run period, are consistent with the predictions of investors’ sentiment (optimism) after controlling for target type and method of payment. Managers that undertake mergers during bullish periods are rewarded by the generalized upward trend of the market in the short-run. However, this is followed by long-term reversals as the market learns only gradually that many of the mergers undertaken during hot periods were not carefully evaluated and were made under the pressure of ‘urge to merge’ to take advantage of the overall market status of a particular period.     

Acquisitions,Overconfident Managers and Self‐attribution Bias

We examine whether acquisitions by overconfident managers generate superior abnormal returns and whether managerial overconfidence stems from self‐attribution. Self‐attribution bias suggests that overconfidence plays a greater role in higher order acquisition deals predicting lower wealth effects for higher order acquisition deals. Using two alternative measures of overconfidence (1) high order acquisition deals and (2) insider dealings we find evidence supporting the view that average stock returns are related to managerial overconfidence. Overconfident bidders realise lower announcement returns than rational bidders and exhibit poor long‐term performance. Second, we find that managerial overconfidence stems from self‐attribution bias. Specifically, we find that high‐order acquisitions (five or more deals within a three‐year period) are associated with lower wealth effects than low‐order acquisitions (first deals). That is, managers tend to credit the initial success to their own ability and therefore become overconfident and engage in more deals. In our analysis we control for endogeneity of the decision to engage in high‐order acquisitions and find evidence that does not support the self‐selection of excessive acquisitive firms. Our analysis is robust to the influence of merger waves, industry shocks, and macroeconomic conditions.     

Volatility options: Hedging effectiveness,pricing, and model error

Motivated by the growing literature on volatility options and their imminent introduction in major exchanges, this article addresses two issues. First, the question of whether volatility options are superior to standard options in terms of hedging volatility risk is examined. Second, the comparative pricing and hedging performance of various volatility option pricing models in the presence of model error is investigated. Monte Carlo simulations within a stochastic volatility setup are employed to address these questions. Alternative dynamic hedging schemes are compared, and various option‐pricing models are considered. It is found that volatility options are not better hedging instruments than plain‐vanilla options. Furthermore, the most naïve volatility option‐pricing model can be reliably used for pricing and hedging purposes. © 2006 Wiley Periodicals, Inc. Jrl Fut Mark 26:1–31, 2006     

Sources of knowledge acquisition for Chinese software engineers

This research adopts a triangulation strategy based on ethnographic case study and questionnaire survey shedding light on how Chinese software engineers acquire codified and tacit knowledge in their daily development work. Software engineers make effective use of complementary resources within a broad spectrum of choices for seeking advice, learning how to solve technical problems, and transferring knowledge to the local community of practice from far beyond the organizational boundary. The analysis focuses on patterns of advice seeking relations within and across project team boundaries, also highlighting the Internet software technology forums as an important channel for technical information sharing across organizational boundaries. The implications for R&D managers are also discussed with special reference to software development and other knowledge intensive computer related work in China.