Fact or Fiction: African American Confederate Veterans

As an intern this past summer at The National Civil War Museum in Harrisburg, Pennsylvania, I came across many intriguing artifacts. One of the artifacts that stood out to me most was the photo above, which I discovered when the museum’s CEO conducted a behind-the-scenes tour. When I look at this photo, I see, on the surface at least, a group of 13 African American men who are presumably Confederate veterans. Several of these men are dressed up for the occasion. Many are wearing ribbons, one man has a Confederate flag, and another has a trumpet. There are also two white men standing on the right side. Looking at this photo, I was fascinated by the possibility that Africans Americans would fight for the South. [excerpt

A Common Soldier: William H. P. Ivey

When I set out to pick a soldier for my first Killed at Gettysburg project, I did not know what I would find. I chose to research a Confederate soldier named William H. P. Ivey simply because he was born and raised on a farm, like me. As I did my research, I realized that Ivey’s life tells us a lot about the motivations and thoughts of a common southern soldier in the Civil War. Like most Confederate infantrymen, Ivey’s family was of the lower class and they were not slaveholders. Ivey, along with his brother Hinton, enlisted in the 8th Alabama on May 8th, 1861. [excerpt

Cointegrating MiDaS Regressions and a MiDaS Test

This paper introduces cointegrating mixed data sampling (CoMiDaS) regressions, generalizing nonlinear MiDaS regressions in the extant literature. Under a linear mixed-frequency data-generating process, MiDaS regressions provide a parsimoniously parameterized nonlinear alternative when the linear forecasting model is over-parameterized and may be infeasible. In spite of potential correlation of the error term both serially and with the regressors, I find that nonlinear least squares consistently estimates the minimum mean-squared forecast error parameter vector. The exact asymptotic distribution of the difference may be non-standard. I propose a novel testing strategy for nonlinear MiDaS and CoMiDaS regressions against a general but possibly infeasible linear alternative. An empirical application to nowcasting global real economic activity using monthly covariates illustrates the utility of the approach.cointegration, mixed-frequency series, mixed data sampling

Testing the Bounds: Empirical Behavior of Target Zone Fundamentals

Standard target zone exchange rate models are based on nonlinear functions of an unobserved economic fundamental, which is assumed to be bounded, similarly to the target zone exchange rates themselves. A violation of this key assumption is a basic structural reason for model failure. Using a novel estimation and testing strategy, we show it is also a testable assumption. Our empirical results cast serious doubt on its validity in practice, providing a primitive reason for well-documented rejections of the basic model. Model failure from this violation is robust to otherwise ideal circumstances (e.g., perfect credibility).target zone exchange rates, economic fundamental, unscented Kalman filter, rescaled range statistic

Cointegrating Regressions with Messy Regressors: Missingness, Mixed Frequency, and Measurement Error

We consider a cointegrating regression in which the integrated regressors are messy in the sense that they contain data that may be mismeasured, missing, observed at mixed frequencies, or have other irregularities that cause the econometrician to observe them with mildly nonstationary noise. Least squares estimation of the cointegrating vector is consistent. Existing prototypical variancebased estimation techniques, such as canonical cointegrating regression (CCR), are both consistent and asymptotically mixed normal. This result is robust to weakly dependent but possibly nonstationary disturbances.cointegration, canonical cointegrating regression, near-epoch dependence, messy data, missing data, mixed-frequency data, measurement error, interpolation

Conditionally Efficient Estimation of Long-run Relationships Using Mixed-frequency Time Series

I analyze efficient estimation of a cointegrating vector when the regressand is observed at a lower frequency than the regressors. Previous authors have examined the effects of specific temporal aggregation or sampling schemes, finding conventionally efficient techniques to be efficient only when both the regressand and the regressors are average sampled. Using an alternative method for analyzing aggregation under more general weighting schemes, I derive an efficiency bound that is conditional on the type of aggregation used on the regressand and differs from the unconditional bound defined by the infeasible full-information high-frequency data-generating process. I modify a conventional estimator, canonical cointegrating regression (CCR), to accommodate cases in which the aggregation weights are either unknown or known. In the unknown case, the correlation structure of the error term generally confounds identification of the conditionally efficient weights. In the known case, the correlation structure may be utilized to offset the potential information loss from aggregation, resulting in a conditionally efficient estimator. Efficiency is illustrated using a simulation study and an application to estimating a gasoline demand equation.cointegration, canonical cointegrating regression, temporal aggregation, mixed-frequency series, mixed data sampling, price elasticity of gasoline demand

Reduction of Markov Chains using a Value-of-Information-Based Approach

In this paper, we propose an approach to obtain reduced-order models of Markov chains. Our approach is composed of two information-theoretic processes. The first is a means of comparing pairs of stationary chains on different state spaces, which is done via the negative Kullback-Leibler divergence defined on a model joint space. Model reduction is achieved by solving a value-of-information criterion with respect to this divergence. Optimizing the criterion leads to a probabilistic partitioning of the states in the high-order Markov chain. A single free parameter that emerges through the optimization process dictates both the partition uncertainty and the number of state groups. We provide a data-driven means of choosing the `optimal' value of this free parameter, which sidesteps needing to a priori know the number of state groups in an arbitrary chain.Comment: Submitted to Entrop

Partitioning Relational Matrices of Similarities or Dissimilarities using the Value of Information

In this paper, we provide an approach to clustering relational matrices whose entries correspond to either similarities or dissimilarities between objects. Our approach is based on the value of information, a parameterized, information-theoretic criterion that measures the change in costs associated with changes in information. Optimizing the value of information yields a deterministic annealing style of clustering with many benefits. For instance, investigators avoid needing to a priori specify the number of clusters, as the partitions naturally undergo phase changes, during the annealing process, whereby the number of clusters changes in a data-driven fashion. The global-best partition can also often be identified.Comment: Submitted to the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP

An Analysis of the Value of Information when Exploring Stochastic, Discrete Multi-Armed Bandits

In this paper, we propose an information-theoretic exploration strategy for stochastic, discrete multi-armed bandits that achieves optimal regret. Our strategy is based on the value of information criterion. This criterion measures the trade-off between policy information and obtainable rewards. High amounts of policy information are associated with exploration-dominant searches of the space and yield high rewards. Low amounts of policy information favor the exploitation of existing knowledge. Information, in this criterion, is quantified by a parameter that can be varied during search. We demonstrate that a simulated-annealing-like update of this parameter, with a sufficiently fast cooling schedule, leads to an optimal regret that is logarithmic with respect to the number of episodes.Comment: Entrop