arXiv subjects

Tobias Hartl

Publications and source records attributed to Tobias Hartl.

8 recordsLinked to original sources

arXiv2026

A Two-Step Chemical Vapor Deposition Process for the Synthesis of an Ir(111)/Borophene/2D-Hexagonal Boron Nitride Heterostructure by Intrinsic Segregation

We report on a two-step ultrahigh vacuum chemical vapor deposition synthesis of a vertical Ir(111)/borophene/hexagonal boron nitride heterostructure, using borazine as a single-source precursor. The process takes advantage of the finite solubility of boron in Ir: low precursor pressure at high temperature first establishes a boron reservoir in the near-surface region of the substrate, whereas subsequent growth at higher precursor pressure promotes the formation of a closed hexagonal boron nitride monolayer. During cooldown, the reduced boron solubility drives segregation to the surface, resulting in the formation of a borophene monolayer beneath the hexagonal boron nitride overlayer. The heterostructure, with micron sized grains, homogeneously covers the entire Ir substrate. The study is performed by complementary spot profile analysis low-energy electron diffraction, low-energy electron microscopy, and scanning tunneling microscopy measurements. This intrinsic segregation-assisted growth concept provides a promising route toward scalable synthesis of high-quality, vertical heterostructures of two-dimensional materials.

cond-mat.mtrl-sci↗

arXiv2023

Growth of aligned and twisted hexagonal boron nitride on Ir(110)

The growth of monolayer hexagonal boron nitride (h-BN) on Ir(110) through low-pressure chemical vapor deposition is investigated using low energy electron diffraction and scanning tunneling microscopy. We find that the growth of aligned single hexagonal boron nitride on Ir(110) requires a growth temperature of 1500 K, whereas lower growth temperatures result in coexistence of aligned h-BN with twisted h-BN The presence of the h-BN overlayer suppresses the formation of the nano-faceted ridge pattern known from clean Ir(110). Instead, we observe the formation of a (1 $\times$ n) reconstruction, with n such that the missing rows are in registry with the h-BN/Ir(110) moiré pattern. Our moiré analysis showcases a precise methodology for determining both the moiré periodicity and the h-BN lattice parameter on an fcc(110) surface.

cond-mat.mtrl-sci↗

arXiv2021

Monitoring the pandemic: A fractional filter for the COVID-19 contact rate

This paper aims to provide reliable estimates for the COVID-19 contact rate of a Susceptible-Infected-Recovered (SIR) model. From observable data on confirmed, recovered, and deceased cases, a noisy measurement for the contact rate can be constructed. To filter out measurement errors and seasonality, a novel unobserved components (UC) model is set up. It specifies the log contact rate as a latent, fractionally integrated process of unknown integration order. The fractional specification reflects key characteristics of aggregate social behavior such as strong persistence and gradual adjustments to new information. A computationally simple modification of the Kalman filter is introduced and is termed the fractional filter. It allows to estimate UC models with richer long-run dynamics, and provides a closed-form expression for the prediction error of UC models. Based on the latter, a conditional-sum-of-squares (CSS) estimator for the model parameters is set up that is shown to be consistent and asymptotically normally distributed. The resulting contact rate estimates for several countries are well in line with the chronology of the pandemic, and allow to identify different contact regimes generated by policy interventions. As the fractional filter is shown to provide precise contact rate estimates at the end of the sample, it bears great potential for monitoring the pandemic in real time.

econ.EM↗

arXiv2020

Fractional trends and cycles in macroeconomic time series

We develop a generalization of correlated trend-cycle decompositions that avoids prior assumptions about the long-run dynamic characteristics by modelling the permanent component as a fractionally integrated process and incorporating a fractional lag operator into the autoregressive polynomial of the cyclical component. The model allows for an endogenous estimation of the integration order jointly with the other model parameters and, therefore, no prior specification tests with respect to persistence are required. We relate the model to the Beveridge-Nelson decomposition and derive a modified Kalman filter estimator for the fractional components. Identification, consistency, and asymptotic normality of the maximum likelihood estimator are shown. For US macroeconomic data we demonstrate that, unlike $I(1)$ correlated unobserved components models, the new model estimates a smooth trend together with a cycle hitting all NBER recessions. While $I(1)$ unobserved components models yield an upward-biased signal-to-noise ratio whenever the integration order of the data-generating mechanism is greater than one, the fractionally integrated model attributes less variation to the long-run shocks due to the fractional trend specification and a higher variation to the cycle shocks due to the fractional lag operator, leading to more persistent cycles and smooth trend estimates that reflect macroeconomic common sense.

econ.EM↗

arXiv2020

Fractional trends in unobserved components models

We develop a generalization of unobserved components models that allows for a wide range of long-run dynamics by modelling the permanent component as a fractionally integrated process. The model does not require stationarity and can be cast in state space form. In a multivariate setup, fractional trends may yield a cointegrated system. We derive the Kalman filter estimator for the common fractionally integrated component and establish consistency and asymptotic (mixed) normality of the maximum likelihood estimator. We apply the model to extract a common long-run component of three US inflation measures, where we show that the $I(1)$ assumption is likely to be violated for the common trend.

econ.EM↗

arXiv2020

Approximate State Space Modelling of Unobserved Fractional Components

We propose convenient inferential methods for potentially nonstationary multivariate unobserved components models with fractional integration and cointegration. Based on finite-order ARMA approximations in the state space representation, maximum likelihood estimation can make use of the EM algorithm and related techniques. The approximation outperforms the frequently used autoregressive or moving average truncation, both in terms of computational costs and with respect to approximation quality. Monte Carlo simulations reveal good estimation properties of the proposed methods for processes of different complexity and dimension.

econ.EM↗

arXiv2020

Macroeconomic Forecasting with Fractional Factor Models

We combine high-dimensional factor models with fractional integration methods and derive models where nonstationary, potentially cointegrated data of different persistence is modelled as a function of common fractionally integrated factors. A two-stage estimator, that combines principal components and the Kalman filter, is proposed. The forecast performance is studied for a high-dimensional US macroeconomic data set, where we find that benefits from the fractional factor models can be substantial, as they outperform univariate autoregressions, principal components, and the factor-augmented error-correction model.

econ.EM↗

arXiv2019

Multivariate Fractional Components Analysis

We propose a setup for fractionally cointegrated time series which is formulated in terms of latent integrated and short-memory components. It accommodates nonstationary processes with different fractional orders and cointegration of different strengths and is applicable in high-dimensional settings. In an application to realized covariance matrices, we find that orthogonal short- and long-memory components provide a reasonable fit and competitive out-of-sample performance compared to several competing methods.

econ.EM↗