Decision Errors, Legal Uncertainty and Welfare: a General Treatment

This paper provides a general treatment of the implications for welfare of legal uncertainty. We distinguish legal uncertainty from decision errors: though the former can be influenced by the latter, the latter are neither necessary nor sufficient for the existence of legal uncertainty. We show that an increase in decision errors will always reduce welfare. However, for any given level of decision errors, information structures involving more legal uncertainty can improve welfare. This holds always, even when there is complete legal uncertainty, when sanctions on socially harmful actions are set at their optimal level. This transforms radically one’s perception about the “costs” of legal uncertainty. We also provide general proofs for two results, previously established under restrictive assumptions. The first is that Effects-Based enforcement procedures may welfare dominate Per Se (or object-based) procedures and will always do so when sanctions are optimally set. The second is that optimal sanctions may well be higher under enforcement procedures involving more legal uncertainty.

Legal Uncertainty, Competition Law Enforcement Procedures and Optimal Penalties

In this paper we make three contributions to the literature on optimal Competition Law enforcement procedures. The first (which is of general interest beyond competition policy) is to clarify the concept of “legal uncertainty”, relating it to ideas in the literature on Law and Economics, but formalising the concept through various information structures which specify the probability that each firm attaches – at the time it takes an action – to the possibility of its being deemed anti-competitive were it to be investigated by a Competition Authority. We show that the existence of Type I and Type II decision errors by competition authorities is neither necessary nor sufficient for the existence of legal uncertainty, and that information structures with legal uncertainty can generate higher welfare than information structures with legal certainty – a result echoing a similar finding obtained in a completely different context and under different assumptions in earlier Law and Economics literature (Kaplow and Shavell, 1992). Our second contribution is to revisit and significantly generalise the analysis in our previous paper, Katsoulacos and Ulph (2009), involving a welfare comparison of Per Se and Effects- Based legal standards. In that analysis we considered just a single information structure under an Effects-Based standard and also penalties were exogenously fixed. Here we allow for (a) different information structures under an Effects-Based standard and (b) endogenous penalties. We obtain two main results: (i) considering all information structures a Per Se standard is never better than an Effects-Based standard; (ii) optimal penalties may be higher when there is legal uncertainty than when there is no legal uncertainty.

On the Sources of Uncertainty in Exchange Rate Predictability

We analyse the role of time-variation in coefficients and other sources of uncertainty in exchange rate forecasting regressions. Our techniques incorporate the notion that the relevant set of predictors and their corresponding weights, change over time. We find that predictive models which allow for sudden rather than smooth, changes in coefficients significantly beat the random walk benchmark in out-of-sample forecasting exercise. Using innovative variance decomposition scheme, we identify uncertainty in coefficients' estimation and uncertainty about the precise degree of coefficients' variability, as the main factors hindering models' forecasting performance. The uncertainty regarding the choice of the predictor is small.

Tractable valuations under uncertainty

I put forward a concise and intuitive formula for the calculation of the valuation for a good in the presence of the expectation that further, related, goods will soon become available. This valuation is tractable in the sense that it does not require the explicit resolution of the consumerís life-time problem.

Firm survival, uncertainty and financial frictions: Is there a financial uncertainty accelerator?

Using a large panel of unquoted UK over the period 2000-09, we examine the impact of firm-specific uncertainty on corporate failures. In this context we also distinguish between firms which are likely to be more or less dependant on bank finance as well as public and non-public companies. Our results document a significant effect of uncertainty on firm survival. This link is found to be more potent during the recent financial crisis compared with tranquil periods. We also uncover significant firm-level heterogeneity since the survival chance of bank-dependent and non-public firms are most affected by changes in uncertainty, especially during the recent global financial crisis.

Global Warming and Fat Tailed-uncertainty: Rethinking the Timing and Intensity of Climate Policy

The possibility of low-probability extreme natural events has reignited the debate over the optimal intensity and timing of climate policy. In this paper, we contribute to the literature by assessing the implications of low-probability extreme events on environmental policy in a continuous-time real options model with “tail risk”. In a nutshell, our results indicate the importance of tail risk and call for foresighted pre-emptive climate policies.

Firm survival, uncertainty and financial frictions: Is there a financial uncertainty accelerator?

Using a large panel of unquoted UK firms over the period 2000-09, we examine the impact of firm-specific uncertainty on corporate failures. In this context we also distinguish between firms which are likely to be more or less dependent on bank finance as well as public and non-public companies. Our results document a significant effect of uncertainty on firm survival. This link is found to be more potent during the recent financial crisis compared with tranquil periods. We also uncover significant firm-level heterogeneity since the survival chances of bank-dependent and non-public firms are most affected by changes in uncertainty, especially during the recent global financial crisis.

Term Structure Dynamics, Macro-Finance Factors and Model Uncertainty

This paper extends the Nelson-Siegel linear factor model by developing a flexible macro-finance framework for modeling and forecasting the term structure of US interest rates. Our approach is robust to parameter uncertainty and structural change, as we consider instabilities in parameters and volatilities, and our model averaging method allows for investors' model uncertainty over time. Our time-varying parameter Nelson-Siegel Dynamic Model Averaging (NS-DMA) predicts yields better than standard benchmarks and successfully captures plausible time-varying term premia in real time. The proposed model has significant in-sample and out-of-sample predictability for excess bond returns, and the predictability is of economic value.

Quantile forecasts of inflation under model uncertainty

Bayesian model averaging (BMA) methods are regularly used to deal with model uncertainty in regression models. This paper shows how to introduce Bayesian model averaging methods in quantile regressions, and allow for different predictors to affect different quantiles of the dependent variable. I show that quantile regression BMA methods can help reduce uncertainty regarding outcomes of future inflation by providing superior predictive densities compared to mean regression models with and without BMA.

Traffic forecasts under uncertainty and capacity constraints

Traffic forecasts provide essential input for the appraisal of transport investment projects. However, according to recent empirical evidence, long-term predictions are subject to high levels of uncertainty. This paper quantifies uncertainty in traffic forecasts for the tolled motorway network in Spain. Uncertainty is quantified in the form of a confidence interval for the traffic forecast that includes both model uncertainty and input uncertainty. We apply a stochastic simulation process based on bootstrapping techniques. Furthermore, the paper proposes a new methodology to account for capacity constraints in long-term traffic forecasts. Specifically, we suggest a dynamic model in which the speed of adjustment is related to the ratio between the actual traffic flow and the maximum capacity of the motorway. This methodology is applied to a specific public policy that consists of suppressing the toll on a certain motorway section before the concession expires.

Evaluating the influence of spatial uncertainty in locality points for species distributional modeling

1. Species distribution modelling is used increasingly in both applied and theoretical research to predict how species are distributed and to understand attributes of species' environmental requirements. In species distribution modelling, various statistical methods are used that combine species occurrence data with environmental spatial data layers to predict the suitability of any site for that species. While the number of data sharing initiatives involving species' occurrences in the scientific community has increased dramatically over the past few years, various data quality and methodological concerns related to using these data for species distribution modelling have not been addressed adequately. 2. We evaluated how uncertainty in georeferences and associated locational error in occurrences influence species distribution modelling using two treatments: (1) a control treatment where models were calibrated with original, accurate data and (2) an error treatment where data were first degraded spatially to simulate locational error. To incorporate error into the coordinates, we moved each coordinate with a random number drawn from the normal distribution with a mean of zero and a standard deviation of 5 km. We evaluated the influence of error on the performance of 10 commonly used distributional modelling techniques applied to 40 species in four distinct geographical regions. 3. Locational error in occurrences reduced model performance in three of these regions; relatively accurate predictions of species distributions were possible for most species, even with degraded occurrences. Two species distribution modelling techniques, boosted regression trees and maximum entropy, were the best performing models in the face of locational errors. The results obtained with boosted regression trees were only slightly degraded by errors in location, and the results obtained with the maximum entropy approach were not affected by such errors. 4. Synthesis and applications. To use the vast array of occurrence data that exists currently for research and management relating to the geographical ranges of species, modellers need to know the influence of locational error on model quality and whether some modelling techniques are particularly robust to error. We show that certain modelling techniques are particularly robust to a moderate level of locational error and that useful predictions of species distributions can be made even when occurrence data include some error.

The Role of model uncertainty and learning in the U.S. postwar policy response to oil prices

This paper studies optimal monetary policy in a framework that explicitly accounts for policymakers' uncertainty about the channels of transmission of oil prices into the economy. More specfically, I examine the robust response to the real price of oil that US monetary authorities would have been recommended to implement in the period 1970 2009; had they used the approach proposed by Cogley and Sargent (2005b) to incorporate model uncertainty and learning into policy decisions. In this context, I investigate the extent to which regulator' changing beliefs over different models of the economy play a role in the policy selection process. The main conclusion of this work is that, in the specific environment under analysis, one of the underlying models dominates the optimal interest rate response to oil prices. This result persists even when alternative assumptions on the model's priors change the pattern of the relative posterior probabilities, and can thus be attributed to the presence of model uncertainty itself.

Quantifying uncertainty: physicians' estimates of infection in critically ill neonates and children.

To determine the diagnostic accuracy of physicians' prior probability estimates of serious infection in critically ill neonates and children, we conducted a prospective cohort study in 2 intensive care units. Using available clinical, laboratory, and radiographic information, 27 physicians provided 2567 probability estimates for 347 patients (follow-up rate, 92%). The median probability estimate of infection increased from 0% (i.e., no antibiotic treatment or diagnostic work-up for sepsis), to 2% on the day preceding initiation of antibiotic therapy, to 20% at initiation of antibiotic treatment (P<.001). At initiation of treatment, predictions discriminated well between episodes subsequently classified as proven infection and episodes ultimately judged unlikely to be infection (area under the curve, 0.88). Physicians also showed a good ability to predict blood culture-positive sepsis (area under the curve, 0.77). Treatment and testing thresholds were derived from the provided predictions and treatment rates. Physicians' prognoses regarding the presence of serious infection were remarkably precise. Studies investigating the value of new tests for diagnosis of sepsis should establish that they add incremental value to physicians' judgment.

Policy evaluation and uncertainty about the effects of oil prices on economic activity

This paper addresses the issue of policy evaluation in a context in which policymakers are uncertain about the effects of oil prices on economic performance. I consider models of the economy inspired by Solow (1980), Blanchard and Gali (2007), Kim and Loungani (1992) and Hamilton (1983, 2005), which incorporate different assumptions on the channels through which oil prices have an impact on economic activity. I first study the characteristics of the model space and I analyze the likelihood of the different specifications. I show that the existence of plausible alternative representations of the economy forces the policymaker to face the problem of model uncertainty. Then, I use the Bayesian approach proposed by Brock, Durlauf and West (2003, 2007) and the minimax approach developed by Hansen and Sargent (2008) to integrate this form of uncertainty into policy evaluation. I find that, in the environment under analysis, the standard Taylor rule is outperformed under a number of criteria by alternative simple rules in which policymakers introduce persistence in the policy instrument and respond to changes in the real price of oil.