Path integral duality and Planck scale corrections to the primordial spectrum in exponential inflation

The enormous red-shifting of the modes during the inflationary epoch suggests that physics at the Planck scale may modify the standard, nearly, scale-invariant, primordial, density perturbation spectrum. Under the principle of path-integral duality, the space-time behaves as though it has a minimal length $L_{_{\rm P}}$ (which we shall assume to be of the order of the Planck length), a feature that is expected to arise when the quantum gravitational effects on the matter fields have been taken into account. Using the method of path integral duality, in this work, we evaluate the Planck scale corrections to the spectrum of density perturbations in the case of exponential inflation. We find that the amplitude of the corrections is of the order of $({\cal H}/M_{_{\rm P}})$, where ${\cal H}$ and $M_{_{\rm P}}$ denote the inflationary and the Planck energy scales, respectively. We also find that the corrections turn out to be completely independent of scale. We briefly discuss the implications of our result, and also comment on how it compares with an earlier result.Comment: 12 pages, 1 figure, RevTex4 forma

Do Imports and Exports Adjust Nonlinearly? Evidence from 100 Countries

A country is said to live within its international budget constraint if its exports and imports are cointegrated. Previous studies that tried to verify the cointegration between exports and imports used linear models and supported the theory in almost 50% of countries. In this paper, when we use the nonlinear ARDL approach and asymmetry cointegration method, we support the long-run link between imports and exports in 94 out of 100 countries in our sample. This study is not only the most comprehensive study in the literature, but it is also the first to show that, indeed, trade flows adjust in a nonlinear fashion

The Value of Information for Populations in Varying Environments

The notion of information pervades informal descriptions of biological systems, but formal treatments face the problem of defining a quantitative measure of information rooted in a concept of fitness, which is itself an elusive notion. Here, we present a model of population dynamics where this problem is amenable to a mathematical analysis. In the limit where any information about future environmental variations is common to the members of the population, our model is equivalent to known models of financial investment. In this case, the population can be interpreted as a portfolio of financial assets and previous analyses have shown that a key quantity of Shannon's communication theory, the mutual information, sets a fundamental limit on the value of information. We show that this bound can be violated when accounting for features that are irrelevant in finance but inherent to biological systems, such as the stochasticity present at the individual level. This leads us to generalize the measures of uncertainty and information usually encountered in information theory

Maximum Likelihood and Gaussian Estimation of Continuous Time Models in Finance

Published in Handbook of financial time series, 2008, https://doi.org/10.1007/978-3-540-71297-8_22</p

Maximum likelihood and Gaussian estimation of continuous time models in finance

Ministry of Education, Singapore under its Academic Research Funding Tier

Gaussian Estimation of Mixed-Order Continuous-Time Dynamic Models with Unobservable Stochastic Trends from Mixed Stock and Flow Data

This paper develops an algorithm for the exact Gaussian estimation of a mixed-order continuous-time dynamic model, with unobservable stochastic trends, from a sample of mixed stock and flow data. Its application yields exact maximum likelihood estimates when the innovations are Brownian motion and either the model is closed or the exogenous variables are polynomials in time of degree not exceeding two, and it can be expected to yield very good estimates under much more general circumstances. The paper includes detailed formulae for the implementation of the algorithm, when the model comprises a mixture of first- and second-order differential equations and both the endogenous and exogenous variables are a mixture of stocks and flows.