Application of the Generalized Method of Moments for Estimating Continuous-Time Models of U.S. Short-Term Interest Rates

We show by Monte Carlo simulations that the jackknife estimation of QUENOUILLE (1956) provides substantial bias reduction for the estimation of short-term interest rate models applied in CHAN ET AL. (1992) - hereafter CKLS (1992). We find that an alternative estimation based on NOWMAN (1997) does not sufficiently solve the problem of time aggregation. We provide empirical distributions for parameter tests depending on the elasticity of conditional variance. Using three-month U.S. Treasury bill yields and the Federal fund rates, we demonstrate that the estimation results can depend on both the sampling frequency and the proxy that is used for interest rates.Elasticity of conditional variance, generalized method of moments, jackknife estimation, stochastic differential equations, short-term interest rate.

Magyar Gyógypedagógia 17 (1929) 05-06

A Magyar Gyógypedagógiai Társaság folyóirata 17. évfolyam, 5-6. szám, Budapest, 1929. Havi folyóirat a fogyatékosok (siketnémák, vakok, szellemileg gyengék, beszédhibások, idegesek, epileptikusok és nyomorékok) ügyeinek tárgyalására. 1939-től beolvadt a Magyar gyógypedagógiai tanárok közlönyébe

Pörkölt kávé nedvesség- és olajtartalmának közeli infravörös reflexiós (NIR) spektroszkópia meghatározása INFRAPID 31 készülékkel

Das Gerät INFRAPID 31 erwies sich nach den Befunden der Verfasser auch zur Bestimmung des Feuchtigkeitsgehaltes und des Ölgehaltes vom gerösteten Kaffee fähig. Die Kalibrierung des Geräts wurde auf Grund des durch zwei unterschiedlichen Methoden bestimmten Feuchtigkeitsgehaltes und des durch Exraktion bestimmten Ölgehaltes durchgeführt. Der Korrelationskoeffizient war bei den bestimmten Feuchtigkeitsgehalt 0,982 bzw. bei den mittels Extraktion mit Petroläther bestimmten Ölgehalt 0,971. Die Regressionsgleichungen wurden durch Messung von solchen Mustern kontrolliert, die zur Kalibrierung nicht benützt wurden. Die Genauigkeit der Messungen wurde durch die Länge der zwischen den analytischen und die instrumentalen Bestimmungen vergangenen Zeit beeinflusst (der Feuchtigkeitsgehalt des gerösteten Kaffees verändert sich während seiner Lagerung). The instrument INFRAPID 31 proved to be suitable — according to the measurements of the authors - also for the determination of the moisture content and oil content of roasted coffee. The calibration of the instrument was carried out on the basis of the moisture content determined by two different methods and of the oil content determined by extraction. The coefficient of correlation was at the determined moisture content 0,982 whereas at the oil content determined by extraction with petroleum ether 0,971. The regression equations were checked by measurement of samples which has not been used for calibration. The accuracy of the measurements was affected by the length of the time passed between the analytic and the instrumental determinations (the moisture content of roasted coffee changes during its storage)

Situated planning for execution under temporal constraints

One of the original motivations for domain-independent planning was to generate plans that would then be executed in the environment. However, most existing planners ignore the passage of time during planning. While this can work well when absolute time does not play a role, this approach can lead to plans failing when there are external timing constraints, such as deadlines. In this paper, we describe a new approach for time-sensitive temporal planning. Our planner is aware of the fact that plan execution will start only once planning finishes, and incorporates this information into its decision making, in order to focus the search on branches that are more likely to lead to plans that will be feasible when the planner finishes

Real-time Planning For Robots

Autonomous robotic systems are becoming widespread in the form of self-driving cars, drones, and even as consumer appliances. These systems all have a planner that makes them autonomous. The planner defines the way these robots evaluate and select among the possible actions that are available to them. This dissertations is about a specific type of planning called on-line real-time planning that is especially applicable to autonomous robots. The central thesis of this work is that real-time heuristic search can be a viable planning method for complex state spaces. Planning for autonomous agents requires novel methods that are not direct adaptations of off-line planning methods but designed specifically for the task to provide fast and reliable execution while keeping the agent safe to guarantee that the goal will be reached. While there are many approaches to planning for embodied agents, my work pursues a class of on-line planning methods called real-time heuristic search that provide real-time bounds on action selection time. This dissertation makes three major contributions. Traditional real-time search algorithms are not guaranteed to recognize a subgraph from which the goal is not reachable before entering it, thus they are inherently incomplete in domains with dead-ends when a time bound is imposed on the planner. The first contribution of my dissertation addresses this issue by introducing a real-time search method with completeness guarantees in domains with dead-ends. Real-time planning in the presence of local minima is particularly challenging due to the bounded rationality of real-time decision making. The second contribution of my dissertations is to introduce novel methods to mitigate this deficiency of real-time search. Real-time search methods should be designed specifically to optimize the metric of interest: goal achievement time. Having more time to think leads to more informed and better quality decisions that can improve the overall goal achievement time. The third contribution of my dissertation is the introduction of a real-time metareasoning technique that considers actions that do not lead to the best discovered node, according to the commonly used f metric, in order to provide more time to the agent to plan the upcoming iteration. Together these contributions support that: real-time heuristic search is a viable planning method for complex state spaces