Analysis of intercity modal split models. Final report.

Transportation Department, Office of the Assistant Secretary for Policy and International Affairs, Washington, D.C.

Development of an upgrading plan for highway safety simulation models. Volume II: executive summary.

Turner-Fairbank Highway Research Center, McLean, Va.

Development of an upgrading plan for highway safety simulation models. Volume I: final report.

Turner-Fairbank Highway Research Center, McLean, Va.

Dynamic Models under Uncertainty for Large-Scale Enterprise Systems

Thesis (Ph.D.)--University of Washington, 2016-06

Forecasting stock returns using model-selection criteria

This paper examines the economic significance of return predictability in Australian equities. In light of considerable model uncertainty, formal model-selection criteria are used to choose a specification for the predictive model. A portfolio-switching strategy is implemented according to model predictions. Relative to a buy-and-hold market investment, the returns to the portfolio-switching strategy are impressive under several model-selection criteria, even after accounting for transaction costs. However, as these findings are not robust across other model-selection criteria examined, it is difficult to conclude that the degree of return predictability is economically significant.

Leading edge forecasting techniques for exchange rate prediction

This paper describes how modern machine learning techniques can be used in conjunction with statistical methods to forecast short term movements in exchange rates, producing models suitable for use in trading. It compares the results achieved by two different techniques, and shows how they can be used in a complementary fashion. The paper draws on experience of both inter- and intra-day forecasting taken from earlier studies conducted by Logica and Chemical Bank Quantitative Research and Trading (QRT) group's experience in developing trading models.

Model specification and forecasting foreign exchange rates with vector autoregressions

This study examines the forecasting accuracy of alternative vector autoregressive models each in a seven-variable system that comprises in turn of daily, weekly and monthly foreign exchange (FX) spot rates. The vector autoregressions (VARs) are in non-stationary, stationary and error-correction forms and are estimated using OLS. The imposition of Bayesian priors in the OLS estimations also allowed us to obtain another set of results. We find that there is some tendency for the Bayesian estimation method to generate superior forecast measures relatively to the OLS method. This result holds whether or not the data sets contain outliers. Also, the best forecasts under the non-stationary specification outperformed those of the stationary and error-correction specifications, particularly at long forecast horizons, while the best forecasts under the stationary and error-correction specifications are generally similar. The findings for the OLS forecasts are consistent with recent simulation results. The predictive ability of the VARs is very weak.

Variational inference for Student-t MLP models

This paper presents a novel methodology to infer parameters of probabilistic models whose output noise is a Student-t distribution. The method is an extension of earlier work for models that are linear in parameters to nonlinear multi-layer perceptrons (MLPs). We used an EM algorithm combined with variational approximation, the evidence procedure, and an optimisation algorithm. The technique was tested on two regression applications. The first one is a synthetic dataset and the second is gas forward contract prices data from the UK energy market. The results showed that forecasting accuracy is significantly improved by using Student-t noise models.

Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models

This paper presents some forecasting techniques for energy demand and price prediction, one day ahead. These techniques combine wavelet transform (WT) with fixed and adaptive machine learning/time series models (multi-layer perceptron (MLP), radial basis functions, linear regression, or GARCH). To create an adaptive model, we use an extended Kalman filter or particle filter to update the parameters continuously on the test set. The adaptive GARCH model is a new contribution, broadening the applicability of GARCH methods. We empirically compared two approaches of combining the WT with prediction models: multicomponent forecasts and direct forecasts. These techniques are applied to large sets of real data (both stationary and non-stationary) from the UK energy markets, so as to provide comparative results that are statistically stronger than those previously reported. The results showed that the forecasting accuracy is significantly improved by using the WT and adaptive models. The best models on the electricity demand/gas price forecast are the adaptive MLP/GARCH with the multicomponent forecast; their MSEs are 0.02314 and 0.15384 respectively.

Schematic structure and the modulation of propositions in economics forecasting text

Working within the framework of the branch of Linguistics known as discourse analysis, and more specifically within the current approach of genre analysis, this thesis presents an analysis of the English of economic forecasting. The language of economic forecasting is highly specialised and follows certain conventions of structure and style. This research project identifies these characteristics and explains them in terms of their communicative function. The work is based on a corpus of texts published in economic reports and surveys by major corporate bodies. These documents are targeted at an international expert readership familiar with this genre. The data is analysed at two broad levels: firstly, the macro-level of text structure which is described in terms of schema-theory, a currently influential model of analysis, and, secondly, the micro-level of authors' strategies for modulating the predictions which form the key move in the forecasting schema. The thesis aims to contribute to the newly developing field of genre analysis in a number of ways: firstly, by a coverage of a hitherto neglected but intrinsically interesting and important genre (Economic Forecasting); secondly, by testing the applicability of existing models of analysis at the level of schematic structure and proposing a genre-specific model; thirdly by offering insights into the nature of modulation of propositions which is often broadly classified as `hedging' or `modality', and which has been recently described as lq`an area for prolonged fieldwork'. This phenomenon is shown to be a key feature of this particular genre. It is suggested that this thesis, in addition to its contribution to the theory of genre analysis, provides a useful basis for work by teachers of English for Economics, an important area of English for Specific Purposes.

Forecasting the term structure when short-term rates are near zero

This paper compares the experience of forecasting the UK government bond yield curve before and after the dramatic lowering of short-term interest rates from October 2008. Out-of-sample forecasts for 1, 6 and 12 months are generated from each of a dynamic Nelson-Siegel model, autoregressive models for both yields and the principal components extracted from those yields, a slope regression and a random walk model. At short forecasting horizons, there is little difference in the performance of the models both prior to and after 2008. However, for medium- to longer-term horizons, the slope regression provided the best forecasts prior to 2008, while the recent experience of near-zero short interest rates coincides with a period of forecasting superiority for the autoregressive and dynamic Nelson-Siegel models. © 2014 John Wiley & Sons, Ltd.

Experience of Development and Application of Expert Systems for Economic Macroparameters Forecasting and Operation

In this article there are considered problems of forecasting economical macroparameters, and in the first place, index of inflation. Concept of development of synthetical forecasting methods which use directly specified expert information as well as calculation result on the basis of objective economical and mathematical models for forecasting separate “slowly changeable parameters” are offered. This article discusses problems of macroparameters operation on the basis of analysis of received prognostic magnitude.

Short-term wind power forecasting using Gaussian processes

Since wind has an intrinsically complex and stochastic nature, accurate wind power forecasts are necessary for the safety and economics of wind energy utilization. In this paper, we investigate a combination of numeric and probabilistic models: one-day-ahead wind power forecasts were made with Gaussian Processes (GPs) applied to the outputs of a Numerical Weather Prediction (NWP) model. Firstly the wind speed data from NWP was corrected by a GP. Then, as there is always a defined limit on power generated in a wind turbine due the turbine controlling strategy, a Censored GP was used to model the relationship between the corrected wind speed and power output. To validate the proposed approach, two real world datasets were used for model construction and testing. The simulation results were compared with the persistence method and Artificial Neural Networks (ANNs); the proposed model achieves about 11% improvement in forecasting accuracy (Mean Absolute Error) compared to the ANN model on one dataset, and nearly 5% improvement on another.

Kelet-közép-európai devizaárfolyamok előrejelzése határidős árfolyamok segítségével (Forecasting the exchange rates of three Central-Eastern European currencies with forward exchange rates)

Írásunkban azt vizsgáljuk, hogy a hosszú lejáratú határidős árfolyamok stacionaritását feltételező hibakorrekciós modellek, amelyeknek korábbi számítások szerint - a világ devizapiaci forgalmának mintegy 75 százalékát kitevő fejlett ipari országokra alkalmazva - kitűnő a mintán kívüli előrejelző erejük, hogyan képesek három keletközép- európai ország devizaárfolyamát előrejelezni. A három vizsgálat alá vont deviza (cseh, magyar, lengyel) esetében az eredmények relációnként nagyon eltérnek, és összességében kedvezőtlenebbek, mint a fejlett ipari országokra kapott eredmények, amit a nem teljesen rugalmas árfolyamrezsim, a rendelkezésre álló adatsor rövidsége, az eurózóna-csatlakozáshoz kapcsolódó bizonytalanságok, a devizakockázati és a határidős kamatprémium létezése, továbbá a Balassa-Samuelson-hatás együttes befolyásaként tudunk értelmezni. JEL kód: E43, F31, F47. /===/ This paper studies whether models that assume long-maturity forward exchange rates are stationary (which proved in earlier studies to provide superior forecasting ability when applied to exchange rates of major currencies) are capable of forecasting the Euro exchange rates of three Central-East European currencies (the Czech koruna, Hungarian forint and Polish zloty). The results for the three currencies differ from each other and are generally much worse than those obtained earlier for major currencies. These unfavourable results are attributed to the consequences of managed exchange-rate systems, to the short time series available, to uncertainties related to future Euro-zone entry, to the existence of a foreign exchange and term premium, and to the Balassa–Samuelson effect.