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This article studies the first-order vector error correction (VEC(1)) model when its noise is a linear process of independent and identically distributed (i.i.d.) heavy-tailed random vectors with a tail index α ∈ (0 , 2) . We show that the rate of convergence of the least squares estimator (LSE) related to the long-run parameters is n (sample size) and its limiting distribution is a stochastic integral in terms of two stable random processes, while the LSE related to the short-term parameters is not consistent. We further propose an automated approach via adaptive shrinkage techniques to determine the cointegrating rank in the VEC(1) model. It is demonstrated that the cointegration rank r0 can be consistently selected despite the fact that the LSE related to the short-term parameters is not consistently estimable when the tail index α ∈ (1 , 2) . Simulation studies are carried out to evaluate the performance of the proposed procedure in finite samples. Last, we use our techniques to explore the long-run and short-run behavior of the monthly prices of wheat, corn, and wheat flour in the United States. [ABSTRACT FROM AUTHOR]

Stochastic precipitation generators (SPGs) are often used to produce synthetic precipitation series for water resource management. Typically, an SPG assumes a stationary climate. We present an hourly precipitation generation algorithm for nonstationary conditions informed by the global climate model (GCM) forecasted average monthly temperature (AMT). The physical basis for precipitation formation is considered explicitly in the design of the algorithm using hourly pressure change events (PCE) to define the relationship between hourly precipitation and AMT. The algorithm consists of a multivariable Markov Chain and a moving window driven by time, temperature, and pressure change. We demonstrate the methodology by generating a 100‐year, continuous, synthetic hourly precipitation time series using GCM AMT projections for the Northeast United States. When compared with historical observations, the synthetic results suggest that future precipitation in this region will be more variable, with more frequent mild events and fewer but intensified extremes, especially in warm seasons. The synthetic time series suggests that there will be less precipitation in the summers, while winters will be wetter, consistent with other research on climate change projections for the Northeast United States. This SPG provides physically plausible weather ensembles for water resource studies involving climate change. Key Points: This article introduces a nonparametric stochastic process in generating synthetic precipitations for nonstationary climateThe basis of this algorithm is highly related to the physical mechanism of precipitation formation defined by pressure change events [ABSTRACT FROM AUTHOR]

In this paper, we evaluate the performance of US manufacturing and service operations in the presence of information technology (IT) as measured by technical efficiency, using firm-level data from 133 companies over the period from 1999 to 2009. To gain insight into the phenomenon of the 'IT productivity paradox', or the history of inconsistent findings in the existing literature, we employ a Bayesian stochastic production frontier approach to model the relationship between performance and technical efficiency at the firm, industry and sector levels. Some results are indicative of a slight advantage of the manufacturing sector over the service sector in terms of technical efficiency and a significant positive contribution of IT-investment to firm output. However, other results do suggest the productivity paradox, because of a lack of any definitive association of high IT investment levels with either high- or low-technical efficiency. Indeed, the findings of this study suggest that the origin of some portion of the IT productivity paradox may exist at the industry level, in that the relationship between extreme levels of IT-investment and extreme levels of technical efficiency appear to work differently in sufficiently different industries. [ABSTRACT FROM AUTHOR]

Hydrometeorological flood generating processes (excess rain, short rain, long rain, snowmelt, and rain‐on‐snow) underpin our understanding of flood behavior. Knowledge about flood generating processes improves hydrological models, flood frequency analysis, estimation of climate change impact on floods, etc. Yet, not much is known about how climate and catchment attributes influence the spatial distribution of flood generating processes. This study aims to offer a comprehensive and structured approach to close this knowledge gap. We employ a large sample approach (671 catchments across the contiguous United States) and evaluate how catchment attributes and climate attributes influence the distribution of flood processes. We use two complementary approaches: A statistics‐based approach which compares attribute frequency distributions of different flood processes; and a random forest model in combination with an interpretable machine learning approach (accumulated local effects [ALE]). The ALE method has not been used often in hydrology, and it overcomes a significant obstacle in many statistical methods, the confounding effect of correlated catchment attributes. As expected, we find climate attributes (fraction of snow, aridity, precipitation seasonality, and mean precipitation) to be most influential on flood process distribution. However, the influence of catchment attributes varies both with flood generating process and climate type. We also find flood processes can be predicted for ungauged catchments with relatively high accuracy (R2 between 0.45 and 0.9). The implication of these findings is flood processes should be considered for future climate change impact studies, as the effect of changes in climate on flood characteristics varies between flood processes. Key Points: Flood generating processes are mostly influenced by climate attributesWhich attributes are influential varies between different flood processes and climate regionsMix of flood generating processes can be predicted for ungauged catchments with high accuracy [ABSTRACT FROM AUTHOR]

This paper examines the presence of chaotic dynamics in Real Estate Investment Trusts (REITs) index returns for the United States. Specifically, the paper uses the largest Lyapunov exponent to explores the chaotic behaviors of all, mortgage and equity REITs index returns for the time period running from January 1980 through December 2018. The sample is divided into two (pre-crisis and post-crisis periods) to ascertain the impact of the U.S. subprime mortgage crisis on the dynamic structure of REITs index returns. The ADF, Phillips-Perron and the KPSS are used to determine the time series properties of the REITs index returns. To test for nonlinearity, the paper implemented the BDS test. The results from the unit root tests indicate that the three REITs index returns have zero order of integration. The BDS test results show that the REITs index return series are nonlinear. The results from the largest Lyapunov exponent test for the full and pre-crisis sample periods provide supportive evidence of chaotic dynamics in REITs index returns. However, for the post-crisis period, the study finds that the behavior of the three REITs index returns is governed by stochastic processes as opposed to chaotic dynamics. The implications of the results are discussed. [ABSTRACT FROM AUTHOR]

Existing approaches to testing for the efficiency of a given portfolio make strong parametric assumptions about investor preferences and return distributions. Stochastic dominance-based procedures promise a useful nonparametric alternative. However, these procedures have been limited to considering binary choices. In this paper we take a new approach that considers all diversified portfolios and thereby introduce a new concept of first-order stochastic dominance (FSD) optimality of a given portfolio relative to all possible portfolios. Using our new test, we show that the U.S. stock market portfolio is significantly FSD nonoptimal relative to benchmark portfolios formed on market capitalization and book-to-market equity ratios. Without appealing to parametric assumptions about the return distribution, we conclude that no nonsatiable investor would hold the market portfolio in the face of the attractive premia of small caps and value stocks. [ABSTRACT FROM AUTHOR]

We test the doubly stochastic assumption under which firms' default times are correlated only as implied by the correlation of factors determining their default intensities. Using data on U.S. corporations from 1979 to 2004, this assumption is violated in the presence of contagion or “frailty” (unobservable explanatory variables that are correlated across firms). Our tests do not depend on the time-series properties of default intensities. The data do not support the joint hypothesis of well-specified default intensities and the doubly stochastic assumption. We find some evidence of default clustering exceeding that implied by the doubly stochastic model with the given intensities. [ABSTRACT FROM AUTHOR]

This article theoretically explores the characteristics underpinning quadratic term structure models (QTSMs), which designate the yield on a bond as a quadratic function of underlying state variables. We develop a comprehensive QTSM, which is maximally flexible and thus encompasses the features of several diverse models including the double square-root model of Longstaff (1989), the univariate quadratic model of Beaglehole and Tenney (1992), and the squared-autoregressive-independent-variable nominal term structure (SAINTS) model of Constantinides (1992). We document a complete classification of admissibility and empirical identification for the QTSM, and demonstrate that the QTSM can overcome limitations inherent in affine term structure models (ATSMs). Using the efficient method of moments of Gallant and Tauchen (1996), we test the empirical performance of the model in determining bond prices and compare the performance to the ATSMs. The results of the goodness-of-fit tests suggest that the QTSMs outperform the ATSMs in explaining historical bond price behavior in the United States. [ABSTRACT FROM AUTHOR]

The authors show how multimarket data can be used to make predictions about brand performance in markets for which no or poor data exist. To obtain these predictions, the authors propose a model for market similarity that incorporates the structure of the U.S. retailing industry and the geographic location of markets. The model makes use of the idea that if two markets have the same retailers or are located close to each other, then branded goods in these markets should have similar sales performance (other factors being held constant). In holdout samples, the proposed spatial prediction method improves greatly on naive predictors such as global-market averages, nearest neighbor predictors, or local averages. In addition, the authors show the spatial model gives more plausible estimates of price elasticities. It does so for two reasons. First, the spatial model helps solve an omitted variables problem by allowing for unobserved factors with a cross-market structure. An example of such unobserved factors is the shelf-space allocations made at the retail-chain level. Second, the model deals with uninformative estimates of price elasticities by drawing them toward their local averages. The authors discuss other substantive issues as well as future research. [ABSTRACT FROM PUBLISHER]

This paper presents an empirical microstructure model of bid and ask quotes that features discreteness, random costs of market making, and ARCH volatility effects. Applied to intraday quotes at 15-minute intervals for Alcoa (a randomly chosen Dow stock), the results show that quote exposure costs contain stochastic components that are persistent and large relative to the deterministic intraday "U" components. Analysis of the filtered estimates of the system suggest that bid and ask costs contain common components, and that these costs reflect risk as proxied by ARCH variance forecasts. [ABSTRACT FROM AUTHOR]

A model is considered for the flow of a river when it is high. The model is based on queueing theory. An application to the Delaware River, located in the United States, is presented. It is shown that an M/M/1/c queueing model is realistic when the flow exceeds a certain threshold. Using this model, one can forecast what would happen if the rate at which events occur increases. The results can be extended by considering more general birth-and-death stochastic processes. [ABSTRACT FROM AUTHOR]

The relationship between income inequality and long-run economic growth has gained a growing attention in economic research for over decades. This study employed advanced time series techniques to examine the existence of an inverted U-shaped long-run relationship between income inequality and economic growth, using long-span time series data for the United States between the periods 1917–2012. The concepts of summability, balancedness and co-summability was advanced to analyze nonlinear long-run relations among stochastic processes. The empirical results find no evidence in support of nonlinear long-run (inverted U-shaped) relationship for the US, but findings from a vocal set of economists lends strong support and is the basis for the conclusions drawn by this study. [ABSTRACT FROM AUTHOR]

The article presents an examination into research on the sustainability of the U.S. fiscal policy in light of budget deficits. Criticism and extensions to previous empirical modeling are provided, outlining a stochastic model to integrate dynamic efficiency methods to calculate interest rates and discounting behaviors. A general government budget equation is modeled, utilizing a simple general equilibrium paradigm which then derives transversality conditions and intertemporal budget constraints. Commentary is also offered outlining the model's application to the U.S. system.

The article presents research extensions on studies exploring the cost inefficiencies present in commercial banks of the U.S. in relation to their size, particularly when considering large banks with assets over $1 billion. A brief review of previous research is given and adaptive models are presented to apply estimation theory to large U.S. commercial banks. A flexible stochastic frontier method and translog cost function analysis is derived and econometric analysis is offered, observing an inverse relationship between bank size and efficiency.

The article focuses on speculative bubbles in foreign exchange markets, particularly the bilateral exchange rate of U.S. dollars for the national currencies of Germany, France, and Japan. A mathematical model is presented that outlines the portfolio balance equation. An uncertainty bubble solution was used for this research rather than a risk bubble solution because of how exchange rate bubbles are created and how the uncertainty bubble solutions handles estimations. The article concludes that the uncertainty bubble is a good method because it passed the positive methodology test and other statistical tests.

The objective of this paper is to estimate the relationship between nominal rates of interest and percentage changes in the price level over the period from 1861 through 1970 with several distributed lag estimators. A large number of subperiods will be investigated using monthly, quarterly, and annual observations on prices and interest rates. The evidence indicates that the hypothetical relationship between interest rates and price changes is not a well established empirical relationship as is often claimed in the professional literature. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to examine the stability of the demand-for-money function using the United States annual data for the period 1900-1974. During this period the United States economy has undergone a wide variety of institutional and monetary change. The question whether a stable demand-for-money function existed during such a long period is of great importance,l We have examined this issue using the varying parameter technique recently developed by Cooley and Prescott. This technique gives estimates of the parameter vector under the maintained assumption that the parameters may be subject to "sequential changes" over time. And, therefore, unlike the usual procedures for testing for structural shift does not require any prior knowledge of the point in time when the shifts in the parameters of the equation might have occurred. [ABSTRACT FROM AUTHOR]

The article presents the results of an equilibrium model created to analyze the pricing of U.S. government bonds for the period of 1958-1979. The bond pricing model relies on the assumption that there are only two independent stochastic factors which are two interest rates; the short rate and the consol rate. These determine bond prices and the shape and position of the yield curve at any point of time. The model also considers Arbitrage Pricing theory, the term structure of interest rates, and the relative rates of return on bonds with different coupons and maturities.

Purchased order flow refers to the practice of dealers or trading locales paying brokers for retail order flow. It is alleged that such agreements are used to "cream skim" uninformed liquidity trades, leaving the information-based trades to established markets. We develop a test of this hypothesis, using a model of the stochastic process of trades. We then estimate the model for a sample of stocks known to be used in order purchase agreements that trade on the New York Stock Exchange (NYSE) and the Cincinnati Stock Exchange. Our main empirical result is that there is a significant difference in the information content of orders executed in New York and Cincinnati, and that this difference is consistent with cream-skimming. [ABSTRACT FROM AUTHOR]

For homothetic time and state separable preferences, the coefficient of relative risk aversion (CRRA) is equal to the reciprocal of the elasticity of intertemporal substitution (EIS). This paper shows that when the growth rate of consumption is i.i.d., asset pricing models based upon preferences in which the CHRA and the EIS are no longer linked do not have more explanatory power. Further, in these stochastic environments, estimates of the CHRA in the standard preferences are measures of the true CHRA and not the EIS. These results are fairly accurate descriptions of economies calibrated using United States annual data. [ABSTRACT FROM AUTHOR]

Organizational renewal requires that a firm's top managers make timely adjustments in their mental models following significant changes in the environment. Our initial propositions about the difference between renewal and decline focused on whether similar organizations in similar contexts differ in their ability to recognize significant changes in their environments. Analysis of longitudinal data from a matched pair of U.S. railroads suggested, however, that renewal hinges not so much on noticing new conditions, but on being able to link environmental change to corporate strategy and to modify that linkage over time. In the successful company we studied organizational renewal is a continuous process of first and second order changes in cognitive maps. [ABSTRACT FROM AUTHOR]

The article focuses on debate concerning the Phelps-Friedman "accelerationist" view of the Phillips curve. It states that empirical tests of Phelps-Friedman is suspect because the validity of the tests is predicated on the adequacy of a suspect hypothesis. It suggests that while it is reasonable to believe a sustained, constant inflation would be fully anticipated, this consideration is not helpful for the purposes of empirical estimation as inflation is never as sustained during periods of estimation as the mental experiment claims.

The time series behavior of corporate incomes has generated considerable interest. The early work of Little suggests that successive changes in corporate incomes in the U.K. are independent. Subsequent empirical work suggests that the same conclusion applies to corporate incomes in the U.S. This article applies different methods from those of previous researchers, but the conclusion is unchanged. We conclude that, in general, measured annual accounting incomes for U.S. corporations follow either a submartingale or some very similar process. Because of small sample problems this conclusion is based on mean and median results; no attempt has been made to determine whether specific firms are systematic outliers. The conclusion that corporate incomes are submartingales has important implications for forecasters and researchers in accounting and finance. For example, it implies that attempts to smooth corporate incomes in the manner suggested in the accounting and finance literatures are not successful. It also affects the interpretation of the growth and decline of firms. [ABSTRACT FROM AUTHOR]

Abstract: Labor's share of income is a key variable in economics. It plays a leading role in analysis of (in)equality, globalization, technical change, growth theories, etc. Notwithstanding this broad application, there are many different definitions of the labor share. Understanding and synthesizing those differences is the purpose of this applied survey. Empirical measures may vary reflecting the allocation of income components that cannot be directly ascribed to capital or labor. We examine the alternative assumptions made in the literature in this regard and quantify and motivate the resulting discrepancies. Focusing (mostly) on US data, we show that different measures can have very distinct properties in terms of the observed stochastic trends, shares of short‐, medium‐, and long‐run variation and volatilities, persistence and mean‐reversion properties, and susceptibility to structural breaks. For instance, while “short‐run” properties of the surveyed labor share measures are relatively consistent across all definitions (and countercyclical), their “medium‐” and “long‐run” trends may diverge substantially (and are procyclical). To substantiate our analysis, we document the implications of discrepancies in the empirical labor share definition for growth accounting, analyzing the effect of technology shocks, and for estimating inflation dynamics. [ABSTRACT FROM AUTHOR]

Reliable subnational mortality estimates are essential in the study of health inequalities within a country. One of the difficulties in producing such estimates is the presence of small populations among which the stochastic variation in death counts is relatively high, and thus the underlying mortality levels are unclear. We present a Bayesian hierarchical model to estimate mortality at the subnational level. The model builds on characteristic age patterns in mortality curves, which are constructed using principal components from a set of reference mortality curves. Information on mortality rates are pooled across geographic space and are smoothed over time. Testing of the model shows reasonable estimates and uncertainty levels when it is applied both to simulated data that mimic U.S. counties and to real data for French départements. The model estimates have direct applications to the study of subregional health patterns and disparities. [ABSTRACT FROM AUTHOR]

This paper examines the convergence of per capita carbon dioxide (CO 2 ) emissions for 162 countries across the globe covering all income groups (high-income OECD, high-income non-OECD, middle-income and low-income countries) as categorized by the World Bank. The incidence of stochastic convergence is analysed by making use of recently developed wavelet based unit root tests. The existing studies on this subject make use of conventional unit root tests using time series and/or panel data. A serious drawback of these tests is that they analyse the stochastic behaviour of any series in time domain only whereas the stochastic process may behave differently across different frequencies as well. Thus, wavelet methods are an obvious solution as they consider frequency dimension as well in addition to time dimension when analyzing the stochastic behaviour of any data series and hence these methods encompass the conventional unit root testing methodologies by providing a clear and complete picture of the stochastic process. Our empirical findings, based on latest available time series annual data from 1960 to 2010, lend support in favor of convergence among 38 countries including 18 high income OECD countries, 2 high income non-OECD countries, 13 middle income countries and 5 low income countries while for the rest of 124 countries, the CO 2 emission series is found to be non-stationary suggesting the divergence in these countries. These findings are in contrast with the most of the existing studies, which may be due to the use of wavelet based unit root tests, that are of course better alternatives. Some policy implications evolve from the empirical findings. [ABSTRACT FROM AUTHOR]

This paper structurally investigates the changes in the Fed’s communication strategy since the mid-1990s through the lens of anticipated and unanticipated disturbances to a Taylor rule. The anticipated disturbances are identified using Treasury bond yield data in estimating a dynamic stochastic general equilibrium (DSGE) model with a term structure of interest rates. Our estimation results show that the Fed’s decisions were unanticipated for market participants until 1999, but thereafter a larger portion of its future policy actions tended to be communicated in advance. We also find that the relative contribution of the anticipated monetary policy disturbances to macroeconomic fluctuations became larger after 1999. The bond yield data is indispensable to these results, since it contains crucial information on an expected future path of the federal funds rate. [ABSTRACT FROM AUTHOR]

The propensity to name babies after US presidents is well known. Less well known is the magnitude of the increased propensity to name babies after presidents when those babies are born on the president’s birthday. In this article that propensity is evaluated and, in addition, the increased propensity to name children after various holidays and months is also assessed. The analysis makes use of the Social Security Death Index, which contains records of over 90 million deaths occurring between 1935 and 2014. Tens of thousands of individuals have been named after George Washington and also after some months of the year; smaller estimates of the number of babies named after selected other presidents on their birthday, as well as those named after several different holidays and months, are also provided. [ABSTRACT FROM PUBLISHER]

American U.S. crude oil prices have dropped significantly of late down to a low of less than $30 a barrel in early 2016. At the same time price volatility has increased and crude in storage has reached record amounts in the U.S. America. Low oil prices in particular pose quite a challenge for the survival of U.S. America's tight-oil industry. In this paper we assess the current profitability and future prospects of this industry. The question could be broadly stated as: should producers stop operation immediately or continue in the hope that prices will rise in the medium term? Our assessment is based on a stochastic volatility model with three risk factors, namely the oil spot price, the long-term oil price, and the spot price volatility; we allow for these sources of risk to be correlated and display mean reversion. We then use information from spot and futures West Texas Intermediate (WTI) oil prices to estimate this model. Our aim is to show how the development of the oil price in the future may affect the prospective revenues of firms and hence their operation decisions at present. With the numerical estimates of the model's parameters we can compute the value of an operating tight-oil field over a certain time horizon. Thus, the present value (PV) of the prospective revenues up to ten years from now is $37.07/bbl in the base case. Consequently, provided that the cost of producing a barrel of oil is less than $37.07 production from an operating field would make economic sense. Obviously this is just a point estimate. We further perform a Monte Carlo (MC) simulation to derive the risk profile of this activity and calculate two standard measures of risk, namely the value at risk (VaR) and the expected shortfall (ES) (for a given confidence level). In this sense, the PV of the prospective revenues will fall below $22.22/bbl in the worst 5% of the cases; and the average value across these worst scenarios is $19.77/bbl. Last we undertake two sensitivity analyses with respect to the spot price and the long-term price. The former is shown to have a stronger impact on the field's value than the latter. This bodes well with the usual time profile of tight oil production: intense depletion initially, followed by steep decline thereafter. [ABSTRACT FROM AUTHOR]

Coal has been an important source of energy in the USA for centuries. Coal prices can be quite uncertain and highly volatile, often experiencing large changes. Understanding the data-generating process of coal prices would seem critical, both from a market perspective and from a policy perspective. This study investigates the appropriate stochastic process underlying coal prices. Commonly assumed processes, such as geometric Brownian motion fail to properly account for the arrival of unanticipated information which inflicts rapid changes - or jumps - in energy markets. Such discontinuities can manifest 'fat tails' in the distribution of returns. To investigate the possibility of time-varying volatility, generalized autoregressive conditional heteroscedastic models are also incorporated into the analysis. We find compelling empirical evidence that discontinuities must not be ignored, with US coal prices experiencing jumps every few days. The result has implications for the potential closure of coal-fired plants in response to cheaper alternatives or climate-based regulations. [ABSTRACT FROM AUTHOR]

We use an empirically grounded simulation model to examine how initial smoking prevalence moderates the effectiveness of potential interventions designed to change adolescent smoking behavior. Our model investigates the differences that result when manipulating peer influence and smoker popularity as intervention levers. We demonstrate how a simulation-based approach allows us to estimate outcomes that arise (1) when intervention effects could plausibly alter peer influence and/or smoker popularity effects and (2) across a sample of schools that match the range of initial conditions of smoking prevalence in U.S. schools. We show how these different initial conditions combined with the exact same intervention effects can produce substantially different outcomes-for example, effects that produce smoking declines in some settings can actually increase smoking in others. We explore the form and magnitude of these differences. Our model also provides a template to evaluate the potential effects of alternative intervention scenarios. [ABSTRACT FROM AUTHOR]

This article presents a sequential method to estimate the direct transportation economic impacts (DTEI) related to transportation due to disruptions in highway networks used by trucks and cars. The main input is the Freight Analysis Framework version 3, best public data for truck movements in the United States. The method considers multicommodity flows in an equilibrium framework, associates monetary values to changes in traffic conditions that are specific to each user type, and links truck flows with commodity flows. This approach can consider transportation analysis zones smaller than those presented in the Freight Analysis Framework. A real-world numerical example is presented to estimate the DTEI due to severe floods that occurred in 2008 and disrupted key segments of the highway network in the northwestern Indiana region. [ABSTRACT FROM PUBLISHER]

Hip fracture affects more than 250,000 people in the US and 1.6 million worldwide per year. With an aging population, the development of reliable fracture risk models is therefore of prime importance. Due to the complexity of the hip fracture phenomenon, the use of clinical data only, as it is done traditionally, might not be sufficient to ensure an accurate and robust hip fracture prediction model. In order to increase the predictive ability of the risk model, the authors propose to supplement the clinical data with computational data from finite element models. The fusion of the two types of data is performed using deterministic and stochastic computational data. In the latter case, uncertainties in loading and material properties of the femur are accounted for and propagated through the finite element model. The predictive capability of a support vector machine (SVM) risk model constructed by combining clinical and finite element data was assessed using a Women׳s Health Initiative (WHI) dataset. The dataset includes common factors such as age and BMD as well as geometric factors obtained from DXA imaging. The fusion of computational and clinical data systematically leads to an increase in predictive ability of the SVM risk model as measured by the AUC metric. It is concluded that the largest gains in AUC are obtained by the stochastic approach. This gain decreases as the dimensionality of the problem increases: a 5.3% AUC improvement was achieved for a 9 dimensional problem involving geometric factors and weight while a 1.3% increase was obtained for a 20 dimensional case including geometric and conventional factors. [ABSTRACT FROM AUTHOR]

We estimate versions of the Nelson-Siegel model of the yield curve of US government bonds using a Markov switching latent variable model that allows for discrete changes in the stochastic process followed by the interest rates. Our modeling approach is motivated by evidence suggesting the existence of breaks in the behavior of the US yield curve that depend, for example, on whether the economy is in a recession or a boom, or on the stance of monetary policy. Our model is parsimonious, relatively easy to estimate and flexible enough to match the changing shapes of the yield curve over time. We also derive the discrete time non-arbitrage restrictions for the Markov switching model. We compare the forecasting performance of these models with that of the standard dynamic Nelson and Siegel model and an extension that allows the decay rate parameter to be time varying. We show that some parametrizations of our model with regime shifts outperform the single-regime Nelson and Siegel model and other standard empirical models of the yield curve. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Physical activity measurements derived from self-report surveys are prone to measurement errors. Monitoring devices like accelerometers offer more objective measurements of physical activity, but are impractical for use in large-scale surveys. A model capable of predicting objective measurements of physical activity from self-reports would offer a practical alternative to obtaining measurements directly from monitoring devices. Using data from National Health and Nutrition Examination Survey 2003–2006, we developed and validated models for predicting objective physical activity from self-report variables and other demographic characteristics. The prediction intervals produced by the models were large, suggesting that the ability to predict objective physical activity for individuals from self-reports is limited. [ABSTRACT FROM AUTHOR]

This paper uses a multi-factor pricing model with time-varying risk exposures and premia to examine whether the 2003-2006 period has been characterized, as often claimed by a number of commentators and policymakers, by a substantial mispricing of publicly traded real estate assets (REITs). The estimation approach relies on Bayesian methods to model the latent process followed by risk exposures and idiosynchratic volatility. Our application to monthly, 1979-2009 U.S. data for stock, bond, and REIT returns shows that both market and real consumption growth risks are priced throughout the sample by the cross-section of asset returns. There is weak evidence at best of structural mispricing of REIT valuations during the 2003-2006 sample. [ABSTRACT FROM AUTHOR]

Abstract: The sub-prime crisis in the U.S. reveals the limitation of diversification strategy based on mean–variance analysis. A regime switch and a turning point can be observed using a high moment representation and time-dependent transition probability. Up–down price movements are induced by interactions among agents, which can be described by the birth–death (BD) process. Financial instability is visible by dramatically increasing 3rd to 5th moments one-quarter before and during the crisis. The sudden rising high moments provide effective warning signals of a regime-switch or a coming crisis. The critical condition of a market breakdown can be identified from nonlinear stochastic dynamics. The master equation approach of population dynamics provides a unified theory of a calm and turbulent market. [Copyright &y& Elsevier]

In this article, we use actuarial methods to solve a nonlinear stochastic optimal liquidity risk management problem for subprime originators with deposit inflow rates and marketable securities allocation as controls. The main objective is to minimize liquidity risk in the form of funding and credit crunch risk in an incomplete market. In order to accomplish this, we construct a stochastic model that incorporates originator mortgage and deposit reference processes. Finally, numerical examples that illustrate the main modeling and optimization features of the article are provided. [ABSTRACT FROM PUBLISHER]

The characterization of knock intensity distributions can provide useful insights into the process and help to improve knock control system designs. In this paper, an extensive statistical analysis is performed on knock intensity data recorded under a broad range of operating conditions. First, the critical issue of whether the data exhibit any cycle-to-cycle correlations is investigated, and it is shown that knock intensity closely approximates a cyclically independent random process. The study then focuses on the variation of knock intensity distributions with operating condition, and on the quantification of these distributions using simple scalar measures. The relationship between knock event distributions and knock intensity distributions is also investigated, and it is shown that knock event data are binomially distributed regardless of the underlying knock intensity distribution. This supports ongoing efforts to exploit binomial probability theory in knock event simulation and controller design. [ABSTRACT FROM PUBLISHER]

Frontier methods quantify the energy efficiency of buildings by forming an efficient frontier (best-practice technology) and by comparing all buildings against that frontier. Because energy consumption fluctuates over time, the efficiency scores are stochastic random variables. Existing applications of frontier methods in energy efficiency either treat efficiency scores as deterministic values or estimate their uncertainty by resampling from one set of measurements. Availability of smart meter data (repeated measurements of energy consumption of buildings) enables using actual data to estimate the uncertainty in efficiency scores. Additionally, existing applications assume a linear form for an efficient frontier; i.e., they assume that the best-practice technology scales up and down proportionally with building characteristics. However, previous research shows that buildings are nonlinear systems. This paper proposes a statistical method called stochastic energy efficiency frontier (SEEF) to estimate a bias-corrected efficiency score and its confidence intervals from measured data. The paper proposes an algorithm to specify the functional form of the frontier, identify the probability distribution of the efficiency score of each building using measured data, and rank buildings based on their energy efficiency. To illustrate the power of SEEF, this paper presents the results from applying SEEF on a smart meter data set of 307 residential buildings in the United States. SEEF efficiency scores are used to rank individual buildings based on energy efficiency, to compare subpopulations of buildings, and to identify irregular behavior of buildings across different time-of-use periods. SEEF is an improvement to the energy-intensity method (comparing ): whereas SEEF identifies efficient buildings across the entire spectrum of building sizes, the energy-intensity method showed bias toward smaller buildings. The results of this research are expected to assist researchers and practitioners compare and rank (i.e., benchmark) buildings more robustly and over a wider range of building types and sizes. Eventually, doing so is expected to result in improved resource allocation in energy-efficiency programs. [ABSTRACT FROM AUTHOR]

We estimate efficiency and TFP growth for two measures of congestion and two measures of the monetary value of congestion for the largest 88 contiguous cities in the U.S. over the period 1982-2007. Using stochastic frontier analysis we find that the efficiency scores for congestion and the associated ranking of cities is sensitive to the measure of congestion. In contrast, the efficiency scores and rankings are robust for the two measures of the monetary value of congestion. Most importantly, for the most valid measure of congestion and both measures of the monetary value of congestion, we find that average TFP growth over the study period is characterized by an upward trend. This is an encouraging sign even though in all three cases growth is only zero or slightly less than zero at the end of the study period. We therefore conclude that policies which have been used towards the end of the study period such as providing incentives to carpool and encouraging employers to offer flexi-time and telecommuting arrangements appear to have been effective and should be implemented more widely. [ABSTRACT FROM AUTHOR]

This paper aims at designing optimal gear shift strategies for conventional passenger vehicles equipped with discrete ratio transmissions. In order to study quantitatively an optimal trade-off between the fuel economy and the driveability, the vehicle driveability is addressed in a fuel-optimal gear shift algorithm based on dynamic programming by three methods: method 1, weighted inverse of power reserve; method 2, constant power reserve; method 3, variable power reserve. Furthermore, another method based on stochastic dynamic programming is proposed to derive an optimal gear shift strategy over a number of driving cycles in an average sense, hence taking into account the vehicle driveability. In contrast with the dynamic-programming-based strategy, the obtained gear shift strategy based on stochastic dynamic programming is real time implementable. A comparative analysis of all proposed gear shift methods is given in terms of the improvements in the fuel economy and the driveability. The variable-power-reserve method achieves the highest fuel economy without sacrificing the driveability. [ABSTRACT FROM AUTHOR]

A stochastic production frontier was used to measure the initial (i.e., bi-weekly) economic effects of hurricanes on commercial grouper ( Serranidae) production in the Exclusive Economic Zone of the United States Gulf of Mexico from 2005 to 2009. We estimated the economic effects of productivity losses associated with specific hurricanes on the commercial grouper fleet. We also calculated the economic effects due to productivity losses during an entire hurricane season at the regional level. The empirical model controls for input levels as well as other factors affecting production to isolate the initial economic effect caused by hurricanes from other non-weather-related factors. The empirical results revealed that hurricanes striking the Gulf of Mexico coastline from 2005 to 2009 had a negative effect on the production of the commercial grouper fleet. The results also demonstrated the relative importance of inputs and regulations on fish production. [ABSTRACT FROM AUTHOR]

Recent Environmental Protection Agency (EPA) decisions regarding resistance management in Bt-cropping systems have prompted concern in some experts that dual-gene Bt-corn (Cry1A.105 and Cry2Ab2 toxins) may result in more rapid selection for resistance in Helicoverpa zea (Boddie) than single-gene Bacillus thuringiensis (Bt)-corn (CrylAb toxin). The concern is that Bt-toxin longevity could be significantly reduced with recent adoption of a natural refuge for dual-gene Bt-cotton (Cry1Ac and Cry2Ab2 toxins) and concurrent reduction in dual-gene corn refuge from 50 to 20%. A population genetics framework that simulates complex landscapes was applied to risk assessment. Expert opinions on effectiveness of several transgenic corn and cotton varieties were captured and used to assign probabilities to different scenarios in the assessment. At least 350 replicate simulations with randomly drawn parameters were completed for each of four risk assessments. Resistance evolved within 30 yr in 22.5% of simulations with single-gene corn and cotton with no volunteer corn. When volunteer corn was added to this assessment, risk of resistance evolving within 30 yr declined to 13.8%. When dual-gene Bt-cotton planted with a natural refuge and single-gene corn planted with a 50% structured refuge was simulated, simultaneous resistance to both toxins never occurred within 30 yr, but in 38.5% of simulations, resistance evolved to toxin present in single-gene Bt-corn (CrylAb). When both corn and cotton were simulated as dual-gene products, cotton with a natural refuge and com with a 20% refuge, 3% of simulations evolved resistance to both toxins simultaneously within 30 yr, while 10.4% of simulations evolved resistance to CrylAb/c toxin. [ABSTRACT FROM AUTHOR]

Flight delays have been a growing issue and they have reached an all-time high in recent years, with the airlines' on-time performance at its worst level in 2007 since 1995. A recent report by the Joint Economic Committee of the U.S. Congress chaired by Senator Charles E. Schumer has estimated that the total cost to the U.S. economy because of flight delays was as much as $41 billion in 2007. The goal of this paper is to build stochastic models of airline networks and utilize publicly available data to answer the following policy questions: Which are the bottleneck airports in the U.S. air-travel infrastructure (i.e., airports that cause most delay propagation)? How would increasing airport capacity at these airports alleviate delay propagation? What are the appropriate metrics for measuring the robustness of airline schedules? How could these schedules be made more robust? Which flight in an aircraft rotation is a bottleneck flight (and, hence, deserves managerial attention)? Flight delays are typically attributed to two factors: (i) the randomness in the intrinsic travel time for a scheduled flight (which is the travel time excluding propagated delays), and (ii) the propagation of this randomness through the air-travel network and infrastructure. We model both of these factors that cause travel delays. The contribution of this paper is twofold. First, we develop stochastic models, using empirical data, to analyze the propagation of delays through air-transportation networks. Our stochastic models allow us to develop three important robustness measures for airline networks. Second, our analysis enables us to make policy recommendations regarding managing bottleneck resources in the air-travel infrastructure, which, if addressed, could lead to a significant improvement in air-travel reliability. [ABSTRACT FROM AUTHOR]

The first part of this paper is a brief survey of the approaches to economic inequality based on ideas from statistical physics and kinetic theory. These include the Boltzmann kinetic equation, the time-reversal symmetry, the ergodicity hypothesis, entropy maximization and the Fokker-Planck equation. The origins of the exponential Boltzmann-Gibbs distribution and the Pareto power law are discussed in relation to additive and multiplicative stochastic processes. The second part of the paper analyses income distribution data in the USA for the time period 1983-2018 using a two-class decomposition. We present overwhelming evidence that the lower class (more than 90% of the population) is described by the exponential distribution, whereas the upper class (about 4% of the population in 2018) by the power law. We show that the significant growth of inequality during this time period is due to the sharp increase in the upper-class income share, whereas relative inequality within the lower class remains constant. We speculate that the expansion of the upper-class population and income shares may be due to increasing digitization and non-locality of the economy in the last 40 years. This article is part of the theme issue 'Kinetic exchange models of societies and economies'.

This letter investigates the real convergence of 17 Latin American countries to the US economy for the period 1950 to 2008. We examine the order of integration of real Gross Domestic Product (GDP) per capita differences between the US and each Latin American country. We allow for fractional degrees of differentiation. This approach provides a test for stochastic convergence, which is a necessary condition for real convergence. The results show no evidence of stochastic convergence with respect to the US economy in any of the countries under study. [ABSTRACT FROM AUTHOR]

Ice storms are an important natural disturbance within forest ecosystems of the northeastern United States. Current models suggest that the frequency and severity of ice storms may increase in the coming decades in response to changes in climate. Because of the stochastic nature of ice storms and difficulties in predicting their occurrence, most past investigations of the ecological effects of ice storms across this region have been based on case studies following major storms. Here we report on a novel alternative approach where a glaze ice event was created experimentally under controlled conditions at the Hubbard Brook Experimental Forest, New Hampshire, USA. Water was sprayed over a northern hardwood forest canopy during February 2011, resulting in 7-12 mm radial ice thickness. Although this is below the minimum cutoff for ice storm warnings (13 mm of ice) issued by the US National Weather Service for the northeastern United States, this glaze ice treatment resulted in significant canopy damage, with 142 and 218 g C·m-2 of fine and coarse woody debris (respectively) deposited on the forest floor, a significant increase in leaf-on canopy openness, and increases in qualitative damage assessments following the treatment. This study demonstrates the feasibility of a relatively simple approach to simulating an ice storm and underscores the potency of this type of extreme event in shaping the future structure and function of northern hardwood forest ecosystems. [ABSTRACT FROM AUTHOR]