We use the New Keynesian Dynamic Stochastic General Equilibrium (DSGE) framework and Vector Autoregressive (VAR) to investigate the usefulness and relevancy of monetary services, augmented to include credit card transaction services. We use the new credit-card-augmented Divisia monetary aggregates in the models to further the existing research on their usefulness and relevancy. In this research, we compare three different monetary aggregates within the New Keynesian framework: (1) the aggregation-theoretic “true” monetary aggregate, (2) the credit-card-augmented Divisia monetary aggregate, and (3) the simple sum monetary aggregate.

We acquire the following primary results. (1) The credit-card-augmented Divisia monetary aggregate tracks the theoretical (true) monetary aggregate, while simple sum does not. Although this result would be expected from the theory in classical economic models, the result is not an immediate implication of the theory in New Keynesian models and therefore needs empirical confirmation. (2) Under the recursive VAR framework, the credit-card-augmented Divisia monetary aggregate serves as a preferable monetary policy indicator compared to the traditional federal funds rate. (3) On theoretical grounds, we find that the separability condition for existence of a monetary aggregator function could fail, if credit card deferred payment services were excluded from the monetary services block, unless all markets are perfect.

Determining accurate capital requirements is a central activity across the life insurance industry. This is computationally challenging and often involves the acceptance of proxy errors that directly impact capital requirements. Within simulation-based capital models, where proxies are being used, capital estimates are approximations that contain both statistical and proxy errors. Here, we show how basic error analysis combined with targeted exact computation can entirely eliminate proxy errors from the capital estimate. Consideration of the possible ordering of losses, combined with knowledge of their error bounds, identifies an important subset of scenarios. When these scenarios are calculated exactly, the resulting capital estimate can be made devoid of proxy errors. Advances in the handling of proxy errors improve the accuracy of capital requirements.

The Age-Period-Cohort-Improvement (APCI) model is a new addition to the canon of mortality forecasting models. It was introduced by Continuous Mortality Investigation as a means of parameterising a deterministic targeting model for forecasting, but this paper shows how it can be implemented as a fully stochastic model. We demonstrate a number of interesting features about the APCI model, including which parameters to smooth and how much better the model fits to the data compared to some other, related models. However, this better fit also sometimes results in higher value-at-risk (VaR)-style capital requirements for insurers, and we explore why this is by looking at the density of the VaR simulations.

We examine whether input–output interactions among industries impact the transmission of monetary policy shocks through the economy. Using vector autoregressive (VAR) methods we find evidence of heterogeneity in the output response to a monetary policy shock in both finished goods industries and intermediate goods industries. While output responses in finished goods industries can be related to heterogeneity in industry characteristics, this relationship is not so obvious for intermediate goods industries. For the intermediate goods industries in our sample, we find new evidence of demand-spillover effects that impact the transmission of monetary policy via input–output linkages.

Nine out of the last ten recessions in the United States have been preceded by an increase in the price of oil as noted by Hamilton [Palgrave Dictionary of Economics]. Given the small share of energy in gross domestic product this phenomenon is difficult to explain using standard models. In this paper, I show that firm entry can be an important transmission and amplifying channel for energy price shocks. The results from the baseline dynamic stochastic general equilibrium (DSGE) model predict a drop in output that is two times the impact in a model without entry. The model also predicts an increase in energy prices would lead to a decline in real wages, investment, consumption, and return on investment. Additionally, using US firm level data, I demonstrate that a rise in energy prices has a negative impact on firm entry as predicted by the DSGE model. This lends further support toward endogenizing firm entry when analyzing the effects of energy price shocks.

In this paper, we first propose a statistical model, called the Coherent Incurred Paid model, to predict future claims, using simultaneously the information contained in incurred and paid claims. This model does not assume log-normality of the levels (or normality of the growth rates) and is semi-parametric since it only specifies the first and the second moments; however, in order to evaluate the impact of the normality assumption, we also propose a benchmark Gaussian version of our model. Correlations between growth rates of incurred and paid claims are allowed and the tail development period is estimated. We also provide methods for computing the Claim Development Results and their Values at Risk in the semi-parametric framework. Moreover, we show how to take into account the updating of the estimation in the computation of the Claim Development Results. An application highlights the practical importance of relaxing the normality assumption and of updating the estimation of the parameters.

This paper examines the effects of various structural shocks in the passive monetary-active fiscal regime in which the fiscal theory of the price level is valid, and compares these effects to those suggested by conventional theory (the active monetary-passive fiscal regime), within a framework of the New Keynesian sticky price model. The results suggest that the effects of structural shocks are substantially different in the passive monetary-active fiscal regime. First, a monetary contraction (an increase in the interest-rate) increases the inflation rate persistently, and increases output with lags. Second, a positive government spending shock leads to a consumption rise in the model that predicts a consumption fall based on conventional theory. Third, in response to aggregate-demand and aggregate-supply shocks, a period of inflation above (or below) the steady-state is followed by a period of inflation below (or above) the steady-state. This inflation reversal is also found in the impulse responses of the estimated VAR models during the 1940's and 1950's, which suggests that the passive monetary-active fiscal regime seems to be actually in place during that period.I thank the editor, an associate editor, and two anonymous referees for constructive suggestions, Christopher Sims for discussion on earlier drafts, Minjung Chae for research assistance, and Clayton Reck for editorial help. All remaining errors are mine.

Existing empirical evidence on the impact of macroeconomic variables on agriculture remains mixed and inconclusive. This paper re-examines the dynamic relationship between monetary policy variables and agricultural prices using alternative vector autoregression (VAR) type model specifications. Directed acyclic graph theory is proposed as an alternative modeling approach to supplement existing modeling methods. Similar to results in other studies, this study's findings show that over the time period analyzed (1975–2000), changes to money supply as a monetary policy tool had little or no impact on agricultural prices. The primary macroeconomic policy instrument that affects agricultural prices is the exchange rate, which is shown to be directly linked to interest rate, a source of monetary policy shock.

The Erlang mixture model has been widely used in modeling insurance losses due to its desirable distributional properties. In this paper, we consider the problem of efficient estimation of the Erlang mixture model. We present a new thresholding penalty function and a corresponding EM algorithm to estimate model parameters and to determine the order of the mixture. Using simulation studies and a real data application, we demonstrate the efficiency of the EM algorithm.

The effects of the U.S. dollar exchange rate versus the Mexican peso are evaluated for four traded nonfarm-produced inputs (fertilizer, chemicals, farm machinery, and feed) in the U.S. Unit root tests suggest that the exchange rate and the four input price ratios support the presence of unit roots with a trend model but the presence unit roots can be rejected in the first difference model. This result is consistent with a fixed price/flex price conceptual framework, with industrial prices more likely to be unresponsive to the exchange rate than farm commodity prices.

Counter-seasonal imports of fresh produce facilitate year-round availability in the U.S. and may impact the seasonal structure of market price relationships. Vector autoregression analysis is used to determine the nature and extent of spatial price relationships among four geographically distinct regions in the U.S. fresh peach wholesale market. We evaluate differences in regional spatial price relationships and find statistical evidence that price relationships among regions are different in periods dominated by regional domestic supplies imports compared with periods when counter-seasonal imports dominate the market.

Although Vector Autoregressive models are commonly used to forecast prices, specification of these models remains an issue. Questions that arise include choice of variables and lag length. This article examines the use of Forecast Error Variance Decompositions to guide the econometrician's model specification. Forecasting performance of Variance Autoregressive models, generated from Forecast Error Variance Decompositions, is analyzed within wholesale chicken markets. Results show that the Forecast Error Variance Decomposition approach has the potential to provide superior model selections to traditional Granger Causality tests.

We undertake a mathematical clarification of the QIS5 proposal for the calculation of the Motor Third Party Liability (MTPL) man-made catastrophe risk capital in terms of two more general models. The QIS5 model assumption implies that the total loss consists of a single catastrophe claim in case it occurs during the next one-year insurance time period. However, the total loss should instead be dynamically modelled by a sequence of claims of varying size that follow a compound Poisson Pareto model, which is our first alternative model. A second possibility also takes into account the effect of investments, whose financial return process follows a Black-Scholes-Merton model. If one excludes limits of coverage, then asymptotically as the total loss increases without limits the first model is equivalent to the model assumption obtained from the QIS5 assumption by replacing a single catastrophe claim by the total loss. In other words, the QIS5 simple model is justified as limiting asymptotic approximation to the classical compound Poisson Pareto model. Conversely, an asymptotic approximation to the VaR economic capital from this model identifies with a modified QIS5 CAT formula. The inclusion of limits of coverage is also analyzed. In this situation we obtain new simple closed-form implementations of the economic capital formulas.

Actuaries are often faced with the task of estimating tails of loss distributions from just a few observations. Thus estimates of tail probabilities (reinsurance prices) and percentiles (solvency capital requirements) are typically subject to substantial parameter uncertainty. We study the bias and MSE of estimators of tail probabilities and percentiles, with focus on 1-parameter exponential families. Using asymptotic arguments it is shown that tail estimates are subject to significant positive bias. Moreover, the use of bootstrap predictive distributions, which has been proposed in the actuarial literature as a way of addressing parameter uncertainty, is seen to double the estimation bias. A bias corrected estimator is thus proposed. It is then shown that the MSE of the MLE, the parametric bootstrap and the bias corrected estimators only differ in terms of order O(n−2), which provides decision-makers with some flexibility as to which estimator to use. The accuracy of asymptotic methods, even for small samples, is demonstrated exactly for the exponential and related distributions, while other 1-parameter distributions are considered in a simulation study. We argue that the presence of positive bias may be desirable in solvency capital calculations, though not necessarily in pricing problems.

It is known that the partial stop-loss contract is an optimal reinsurance form under the VaR risk measure. Assuming that market premiums are set according to the expected value principle with varying loading factors, the optimal reinsurance parameters of this contract are obtained under three alternative single and joint party reinsurance criteria: (i) strong minimum of the total retained loss VaR measure; (ii) weak minimum of the total retained loss VaR measure and maximum of the reinsurer's expected profit; (iii) weak minimum of the total retained loss VaR measure and minimum of the total variance risk measure. New conditions for financing in the mean simultaneously the cedent's and the reinsurer's required VaR economic capital are revealed for situations of pure risk transfer (classical reinsurance) or risk and profit transfer (design of internal reinsurance or reinsurance captive owned by the captive of a corporate firm).

When analyzing catastrophic risk, traditional measures for evaluating risk, such as the probable maximum loss (PML), value at risk (VaR), tail-VaR, and others, can become practically impossible to obtain analytically in certain types of insurance, such as earthquake, and certain types of reinsurance arrangements, specially non-proportional with reinstatements. Given the available information, it can be very difficult for an insurer to measure its risk exposure. The transfer of risk in this type of insurance is usually done through reinsurance schemes combining diverse types of contracts that can greatly reduce the extreme tail of the cedant's loss distribution. This effect can be assessed mathematically. The PML is dei ned in terms of a very extreme quantile. Also, under standard operating conditions, insurers use several “layers” of non proportional reinsurance that may or may not be combined with some type of proportional reinsurance. The resulting reinsurance structures will then be very complicated to analyze and to evaluate their mitigation or transfer effects analytically, so it may be necessary to use alternative approaches, such as Monte Carlo simulation methods. This is what we do in this paper in order to measure the effect of a complex reinsurance treaty on the risk profile of an insurance company. We compute the pure risk premium, PML as well as a host of results: impact on the insured portfolio, risk transfer effect of reinsurance programs, proportion of times reinsurance is exhausted, percentage of years it was necessary to use the contractual reinstatements, etc. Since the estimators of quantiles are known to be biased, we explore the alternative of using an Extreme Value approach to complement the analysis.

Business cycle models with sticky prices and endogenous firm entry make novel predictions on the transmission of shocks through the extensive margin of investment. I test some of these predictions using a VAR with model-based sign restrictions. I find a positive and significant response of firm entry to expansionary shocks to productivity, aggregate spending, monetary policy, and entry costs. The estimated response to a monetary expansion does not support the monetary policy transmission mechanism proposed by the model. Insofar as firm startups require labor services, wage stickiness is needed to make the signs of the model responses consistent with the estimated ones. The shapes of the empirical responses suggest that congestion effects in entry make it harder for new firms to survive when the number of startups rises.

Censuses of population fragments of the Endangered, endemic Hermann's tortoise Testudo hermanni hermanni were carried out during 2001–2005 in mainland France to reassess their status and provide guidelines for conservation management. Comparisons with previous censuses suggest a decline in abundance since 1992, although the total area of distribution remains unchanged. If conservation and management decisions only consider sites where there is a high encounter rate of tortoises, 19 sites would be protected. If indicators of effective reproduction (presence of both sexes, juveniles and subadults) are taken into account, 31 sites would deserve attention, of which only 10 have high encounter rates. Discarding sites with low encounter rates omits c. 70% of sites where the species appears to be reproducing. I provide a new map that includes demographic parameters, and thus describe 16 reproductive fragments plus four where reproduction is suspected. In the long-term the major conservation goal should be to protect these fragments and facilitate their reconnection. The global population of Hermann's tortoise could thus be largely contained in eight large fragments, enhancing the persistence of this species in mainland France.

The U.S. economy has experienced a reduction in volatility since the mid-1980s. In this paper we investigate the changes in the response of the economy to an oil price shock and the role of the systematic monetary policy response in accounting for changes in the response of output, prices, inventories, sales, and the overall decline in volatility. Our results suggest a smaller and more short-lived response of most macro variables during the Volcker-Greenspan period. It also appears that whereas the systematic monetary policy response dampened fluctuations in economic activity during the 1970s, it has had virtually no effect after the “Great Moderation.”