Econometric guidance for developing UrbanSim models. First lessons from the SustainCity project.

In the context of the SustainCity project (www.sustaincity.eu), three European cities (Brussels, Paris and Zurich) will be modelled using the land use microsimulation platform UrbanSim. This platform relies on various models interacting with each other, to predict long-term urban development. The aim of this paper is to provide some econometric insight into this process. A common set of notation and assumptions are first defined, and the more common model structures (linear regression, multinomial logit, nested logit, mixed MNL and latent variable models) are described in a consistent way. Special treatments and approaches that are required due to the specific nature of the data in this type of applications (i.e. involving very large number of alternatives, and often exhibiting endogeneity, correlation, and (pseudo-)panel data properties) will also be discussed. For example, importance sampling, spatial econometrics, Geographically Weighted Regression (GWR) and endogeneity issues will be covered. Applications and specific options of the following models: (i) household location choice model, (ii) jobs location/firmography, (iii) real estate price model, and (iv) land development model, will be demonstrated using examples from the on-going case studies in Brussels, Paris and Zurich. Finally, lessons learnt in relation to the econometric models from these on-going case studies will be summarized.

Three essays in behavioural finance: An examination into non- Bayesian Investment behaviour

Behavioural Finance relaxes the neoclassical assumption that investors consistently apply Bayes Rule when updating their expectations, and identifies the behavioural attributes that affect asset prices. This thesis extends this literature by examining deviations from the Bayesian model that arise due to i) ambiguity aversion, ii) investor sentiment and iii) decision heuristics. Bayesian Updating assumes that investors are able to always estimate a single generating process for expected returns. However, in reality investors analyze noisy information signals that relate to this unknown distribution in a latent way, and it is likely that they are not always able to determine a single probability distribution. Behavioural economists have shown that in such conditions of uncertainty about probabilities people become pessimistic. The first chapter examines whether the pricing of analyst earnings is affected by ambiguity aversion, offering confirmatory evidence. A behavioural literature shows that people in good sentiment make optimistic choices, relative to objective probabilities. The second chapter examines whether investor sentiment affects the performance of the momentum trading strategy, an anomaly related to the pricing of good and bad information. The results indicate that sentiment strongly affects the momentum phenomenon, suggesting that it is triggered from investors’ behavioural biases. It has been suggested that deviations from Bayesian Updating arise due to heuristics triggered by the characteristics of the information used. The last chapter examines the validity of one such important hypothesis proposed by Griffin and Tversky (1992) using rigorous experimental economics techniques. The results confirm this hypothesis, indicating that investors are likely to overreact to salient information signals with low predictive validity

Surrogate Data Analysis and Stochastic Chaotic Modelling: Application to Stock Exchange Returns Series

We investigate for evidence of complex-deterministic dynamics in financial returns time series. By combining the Surrogate Data Analysis inferential framework with the MG-GARCH (Kyrtsou and Terraza, 2003) modelling approach, we examine whether the sequences are characterized by aperiodic and nonlinear deterministic cycles or pure randomness. Our results support the hypothesis of complex nonlinear and non-stochastic dynamics in the data generating processes. According to our approach, markets can be assumed to be highly complex, high-dimensional, open and dissipative dynamical systems that need feedback as well as other kinds of inputs in order to operate. These inputs may come in the guise of noise or news. The inputs may also control the evolution of the system dynamics and the knowledge of their nature may allow us to forecast the future states of the market with greater accuracy. To this extent the MG-GARCH model provides a valuable insight on how a feedback mechanism can operate within the structure of stock returns processes and explain stylized facts.MG-GARCH, Surrogate Data Analysis, Chaos, Complexity

Terrorist attacks, analyst sentiment, and earnings forecasts

We examine whether exogenous and extremely negative events, such as terrorist attacks and mass shootings, influence the sentiment and forecasts of sell-side equity analysts. We find that analysts who are local to these attacks issue forecasts that are relatively more pessimistic than the consensus forecast. This effect is stronger when the analyst is closer to the event and located in a low-crime region. Impacted analysts are also relatively more pessimistic around the one- and two-year anniversaries of the attacks. Collectively, these findings indicate that exposure to extreme negative events affects the behavior of information intermediaries and the information dissemination process in financial markets

Demand simulation for dynamic traffic assignment

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 1997.Includes bibliographical references (p. 114-118).by Constantinos Antoniou.M.S

On-line calibration for dynamic traffic assignment

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2004.Includes bibliographical references (p. 149-153).(cont.) application, the EKF has more desirable properties than the UKF. Furthermore, the Limiting EKF provides accuracy comparable to that of the best algorithm (EKF), but with computational complexity which is order(s) of magnitude lower than the other algorithms.In this thesis, an on-line calibration approach for dynamic traffic assignment (DTA) that jointly estimates demand and supply parameters has been developed. The objective of on-line calibration is to introduce a systematic procedure that will use the available data to steer the model parameters to values closer to the realized ones. The approach is general and flexible and imposes no restrictions on the models, the parameters or the data that it can handle. The on-line calibration approach is formulated as a state-space model, comprising transition and measurement equations. A priori values provide direct measurements of the unknown parameters (such as origin-destination flows, segment capacities and traffic dynamics models' parameters), while surveillance information (for example, link counts, speeds and densities) is incorporated through indirect measurement equations. The state vector is defined in terms of deviations of the parameters and inputs that need to be calibrated from available estimates. Standard Kalman Filter theory does not apply to this formulation, as it is not linear. Therefore, three modified Kalman Filter methodologies are presented: Extended Kalman Filter (EKF), Limiting EKF, and Unscented Kalman Filter (UKF). A case study on a freeway network in Southampton, U.K., is used to demonstrate the feasibility of the approach, to verify the importance of on-line calibration, and to test the candidate algorithms. The empirical results from this application support the hypothesis that simultaneous on-line calibration of demand and supply parameters can improve the traffic estimation and prediction accuracy and show significant benefits (over the base case in which only the origin-destination flows are estimated on-line). In thisby Constantinos Antoniou.Ph.D

Wagner’s law versus Keynesian hypothesis: Evidence from pre-WWII Greece

With data of over a century, 1833-1938, this paper attempts, for the first time, to analyze the causal relationship between income and government spending in the Greek economy for such a long period; that is, to gain some insight into Wagner and Keynesian Hypotheses. The time period of the analysis represents a period of growth, industrialization and modernization of the economy, conditions which are conducive to Wagner’s Law but also to the Keynesian Hypothesis. The empirical analysis resorts to Autoregressive Distributed Lag (ARDL) Cointegration method and tests for the presence of possible structural breaks. The results reveal a positive and statistically significant long run causal effect running from economic performance towards the public size giving support to Wagner’s Law in Greece, whereas for the Keynesian hypothesis some doubts arise for specific time sub-periods

Modelling reservation-based shared autonomous vehicle services: A dynamic user equilibrium approach

Shared Autonomous Vehicles (SAVs) are expected to be used for regular and pre-planned trips. Such trips are suitable for reservation-based services, wherein the customer needs to book for a trip in advance. Systems enabling reservation of trips can allow for better planning of routes and schedules, and if optimally designed, enable higher efficiency. The primary objective of this research is to model the effects of such a system, by formulating and solving the combined Dynamic User Equilibrium and Shared autonomous vehicle Chain Formation (DUESCF) problem. The problem is formulated as a bilevel model based on game theory, involving road users and SAV service operator. Given a situation where conventional private and shared autonomous vehicles co-exist, road users select paths and departure times to maximize a perceived utility (commonly treated as minimizing a disutility) by forming a DUE (fixed point problem), and the SAV service operator tries to maximize the performance by forming appropriate SAV chains (combinatorial problem). The final objective of this bilevel model is a traffic assignment that includes SAV chain formation, such that both road users and SAV service operator obtain optimal solutions by reaching a Nash equilibrium, where no player is better off by unilaterally changing their decisions. A solution approach, based on Iterative Optimization and Assignment (IOA) method, is proposed with path flow and SAV performance changes as convergence criteria. Furthermore, the solution approach is tested for its robustness, and a scenario analysis is carried out to evaluate the impacts of reservation-based SAV services. The results show that a ridesharing SAV system is better compared to a carsharing and a mixed system consisting of both, in terms of total system travel time, congestion levels, total vehicle kilometres travelled and vehicle requirements

Social media and travel behaviour

Social media has emerged as a trend that greatly influences transportation and travel behaviour. This influence is identified both on the way that travellers make decisions concerning transportation related matters and the fact that social media allow tracing back the way that these decisions were made and allow for the collection of data, related to understanding these decisions. This chapter introduces these interdependences between Social Media (SM) and travel behaviour by presenting a collection of use cases and methods. Firstly, an introduction to the existing dominant SM is presented, including current availability of data and a discussion of helpful frameworks that could facilitate transportation related research on the subject. The pertinent literature on the User Generated Content (UGC) generation process and users’ personality characteristics is reviewed, in order to gain understanding on the characteristics of the users, who generate the content. Secondly, the relation of SM to travel behaviour is established by investigating the impact of UGC to the transportation system and vice versa. The capabilities of SM to allow for behavioural interventions is discussed towards the direction of inducing more socially responsible behaviour. Finally, related case studies are presented