Doubly Robust Smoothing of Dynamical Processes via Outlier Sparsity Constraints

Coping with outliers contaminating dynamical processes is of major importance in various applications because mismatches from nominal models are not uncommon in practice. In this context, the present paper develops novel fixed-lag and fixed-interval smoothing algorithms that are robust to outliers simultaneously present in the measurements {\it and} in the state dynamics. Outliers are handled through auxiliary unknown variables that are jointly estimated along with the state based on the least-squares criterion that is regularized with the $\ell_1$-norm of the outliers in order to effect sparsity control. The resultant iterative estimators rely on coordinate descent and the alternating direction method of multipliers, are expressed in closed form per iteration, and are provably convergent. Additional attractive features of the novel doubly robust smoother include: i) ability to handle both types of outliers; ii) universality to unknown nominal noise and outlier distributions; iii) flexibility to encompass maximum a posteriori optimal estimators with reliable performance under nominal conditions; and iv) improved performance relative to competing alternatives at comparable complexity, as corroborated via simulated tests.Comment: Submitted to IEEE Trans. on Signal Processin

Spatial processes in environmental economics: empirics and theory

Economic activities are fundamentally influenced by their location in space, which determines the physical and natural environment in which they take place. Likewise, location defines the social context of economic activity prescribing the particular laws, regulations and social norms to which it should conform. Moreover, spatial location defines proximity, which shapes the costs of accessing factor inputs, product markets and other economic and social institutions. In fact, spatial location mediates most forms of interaction, intended and unintended, that may arise from communication and connections between economic agents. These spatial processes have important implications for estimation, policy evaluation and prediction in models of economic activity. This thesis is comprised of two parts. Part I presents a broad range of issues that arise in estimation due to space and frames these as general spatial omitted variables. I explore the use of semi-parametric estimators to identify the parameters of interest in this general model and derive identification conditions for fixed and local adaptive spatial smoothing estimators. The properties of these estimators are contrasted to OLS and spatial econometric estimators. Part II addresses issues in policy evaluation and prediction. I derive an equilibrium sorting model with endogenous tenure choice that can be used to evaluate the general equilibrium welfare effects of policies that affect local environmental quality. Using a series of simulations, motivated by a real world policy application, I contrast the welfare changes derived under this model to a conventional static approach. By allowing for rental and purchase markets the model I develop provides a far richer characterisation of the complex adjustments that propagate through the property market following policy changes and the contrary impact such policies can have upon renters and owners. The usefulness of the model for applied policy analysis is demonstrated through two applications: The Polegate Bypass and Mortgage Interest Deduction reform

Temporal Aggregation and Structural Inference in Macroeconomics

This paper examines the quantitative importance of temporal aggregation bias in distorting parameter estimates and hypothesis tests. Our strategy is to consider two empirical examples in which temporal aggregation bias has the potential to account for results which are widely viewed as being anomalous from the perspective of particular economic models. Our first example investigates the possibility that temporal aggregation bias can lead to spurious Granger causality relationships. The quantitative importance of this possibility is examined in the context of Granger causal relations between the growth rates of money and various measures of aggregate output. Our second example investigates the possibility that temporal aggregation bias can account for the slow speeds of adjustment typically obtained with stock adjustment models. The quantitative importance of this possibility is examined in the context of a particular class of continuous and discrete time equilibriurn models of inventories and sales. The different models are compared on the basis of the behavioral implications of the estimated values of the structural parameters which we obtain and their overall statistical performance. The empirical results from both examples provide support for the view that temporal aggregation bias can be quantitatively important in the sense of Significantly distorting inference.

Automatic Matching of Bullet Land Impressions

In 2009, the National Academy of Sciences published a report questioning the scientific validity of many forensic methods including firearm examination. Firearm examination is a forensic tool used to help the court determine whether two bullets were fired from the same gun barrel. During the firing process, rifling, manufacturing defects, and impurities in the barrel create striation marks on the bullet. Identifying these striation markings in an attempt to match two bullets is one of the primary goals of firearm examination. We propose an automated framework for the analysis of the 3D surface measurements of bullet land impressions which transcribes the individual characteristics into a set of features that quantify their similarities. This makes identification of matches easier and allows for a quantification of both matches and matchability of barrels. The automatic matching routine we propose manages to (a) correctly identify land impressions (the surface between two bullet groove impressions) with too much damage to be suitable for comparison, and (b) correctly identify all 10,384 land-to-land matches of the James Hamby study.Comment: 27 pages, 20 figure

Labor-Market Performance and Macroeconomic Policy: Time-Varying NAIRU in the Czech Republic (in English)

During the second half of the 1990s, the Czech economy experienced a sharp increase in the unemployment rate. The authors attempt to determine whether this was caused by structural changes, worsening labor-market performance, or by the changing business-cycle position. This has direct implications for both monetary and fiscal policy. The authors use NAIRU (non-accelerating inflation rate of unemployment) estimates using time-varying NAIRU. The estimates indicate that the NAIRU increased between 1996 and 2002 by approximately 1.5 percent. Estimated increases in the NAIRU can be associated with the worsening of labor-market efficiency.forward-looking expectations, maximum-likelihood methods, non-accelerating inflation rate of unemployment, time-varying NAIRU

Towards Handling Uncertainty in Prognostic Scenarios: Advanced Learning from the Past

Das Forschungsprogramm „Earth System Sciences (ESS)“, ein Programm des Bundesministeriums für Wissenschaft, Forschung und Wirtschaft (BMWFW), durchgeführt von der ÖAW, hat die Erforschung des Systems Erde zum Ziel. Im Rahmen von Ausschreibungen werden wissenschaftliche Forschungsprojekte gefördert, die dem neusten Stand der Wissenschaft entsprechen. Das Programm ESS sieht es als seine Aufgabe, Lücken in der österreichischen Förderungslandschaft zu schließen. Dies bezieht sich etwa auf interdisziplinäre Projekte, Projekte zur Langzeitforschung sowie auf Projekte, die auf derzeit noch gering beforschte Bereiche fokussiert sind und denen wissenschaftlich

Time dependent wind fields

Two tasks were performed: (1) determination of the accuracy of Seasat scatterometer, altimeter, and scanning multichannel microwave radiometer measurements of wind speed; and (2) application of Seasat altimeter measurements of sea level to study the spatial and temporal variability of geostrophic flow in the Antarctic Circumpolar Current. The results of the first task have identified systematic errors in wind speeds estimated by all three satellite sensors. However, in all cases the errors are correctable and corrected wind speeds agree between the three sensors to better than 1 ms sup -1 in 96-day 2 deg. latitude by 6 deg. longitude averages. The second task has resulted in development of a new technique for using altimeter sea level measurements to study the temporal variability of large scale sea level variations. Application of the technique to the Antarctic Circumpolar Current yielded new information about the ocean circulation in this region of the ocean that is poorly sampled by conventional ship-based measurements

A Methodology for Robust Multiproxy Paleoclimate Reconstructions and Modeling of Temperature Conditional Quantiles

Great strides have been made in the field of reconstructing past temperatures based on models relating temperature to temperature-sensitive paleoclimate proxies. One of the goals of such reconstructions is to assess if current climate is anomalous in a millennial context. These regression based approaches model the conditional mean of the temperature distribution as a function of paleoclimate proxies (or vice versa). Some of the recent focus in the area has considered methods which help reduce the uncertainty inherent in such statistical paleoclimate reconstructions, with the ultimate goal of improving the confidence that can be attached to such endeavors. A second important scientific focus in the subject area is the area of forward models for proxies, the goal of which is to understand the way paleoclimate proxies are driven by temperature and other environmental variables. In this paper we introduce novel statistical methodology for (1) quantile regression with autoregressive residual structure, (2) estimation of corresponding model parameters, (3) development of a rigorous framework for specifying uncertainty estimates of quantities of interest, yielding (4) statistical byproducts that address the two scientific foci discussed above. Our statistical methodology demonstrably produces a more robust reconstruction than is possible by using conditional-mean-fitting methods. Our reconstruction shares some of the common features of past reconstructions, but also gains useful insights. More importantly, we are able to demonstrate a significantly smaller uncertainty than that from previous regression methods. In addition, the quantile regression component allows us to model, in a more complete and flexible way than least squares, the conditional distribution of temperature given proxies. This relationship can be used to inform forward models relating how proxies are driven by temperature