CLAR(1) point forecasting under estimation uncertainty

Forecast error is not only caused by the randomness of the data-generating process but also by the uncertainty due to estimated model parameters. We investigate these different sources of forecast error for a popular type of count process, the Poisson first-order integer-valued autoregressive (INAR(1)) process. However, many of our analytical derivations also hold for the more general family of conditional linear AR(1) (CLAR(1)) processes. In addition, results from a simulation study are presented, to verify and complement our asymptotic approximations.     

Stochastic specification and the estimation of share equations

The standard stochastic specification for a system of share equations is obtained by assuming that the shares have a joint normal distribution with means depending upon exogenous variables and a constant covariance matrix. This specification ignores the requirement that shares be between zero and unity by giving positive probability to shares outside this range. An alternative stochastic specification involving the Dirichlet distribution, which automatically limits shares to the unit simplex, is suggested. A comparison of results obtained from the two specifications is made using sampling experiments and data from three different empirical studies. The sampling experiments and empirical applications show that the results are generally quite close, thus providing some justification for the continued use of the normal distribution specification in the estimation of share equations.     

Econometric specification of stochastic discount factor models

We consider the problem of derivative pricing when the stochastic discount factors are exponential-affine functions of underlying state variable. In particular we discuss the conditionally Gaussian framework and introduce semi-parametric pricing methods for models with path dependent drift and volatility. This approach is also applied to more complicated frameworks, such as pricing of a derivative written on an index, when the interest rate is stochastic.     

The firm under uncertainty: real and financial decisions

This paper examines the interplay between the real and financial decisions of the competitive firm under output price uncertainty. The firm faces additional sources of uncertainty that are aggregated into a background risk. We show that the firm always chooses its optimal debt–equity ratio to minimize the weighted average cost of capital, irrespective of the risk attitude of the firm and the incidence of the underlying uncertainty. We further show that the firm’s optimal input mix depends on its optimal debt–equity ratio, thereby rendering the interdependence of the real and financial decisions of the firm. When the background risk is either additive or multiplicative, we provide reasonable restrictions on the firm’s preferences so as to ensure that the firm’s optimal output is adversely affected upon the introduction of the background risk.     

Nonparametric dynamic panel data models: Kernel estimation and specification testing

Motivated by the first-differencing method for linear panel data models, we propose a class of iterative local polynomial estimators for nonparametric dynamic panel data models with or without exogenous regressors. The estimators utilize the additive structure of the first-differenced model—the fact that the two additive components have the same functional form, and the unknown function of interest is implicitly defined as a solution of a Fredholm integral equation of the second kind. We establish the uniform consistency and asymptotic normality of the estimators. We also propose a consistent test for the correct specification of linearity in typical dynamic panel data models based on the _L2$L_2$ distance of our nonparametric estimates and the parametric estimates under the linear restriction. We derive the asymptotic distributions of the test statistic under the null hypothesis and a sequence of Pitman local alternatives, and prove its consistency against global alternatives. Simulations suggest that the proposed estimators and tests perform well for finite samples. We apply our new method to study the relationships among economic growth, the initial economic condition and capital accumulation, and find a significant nonlinear relation between economic growth and the initial economic condition.     

Innovation speed under uncertainty and competition

Innovation speed is widely considered to be a key factor for a firm's ability to maintain competitive advantage. Primarily, empirical evidence has found contradictory interdependencies regarding the role of innovation speed. The prevailing proposition of “the faster the better” has been challenged by results of empirical studies heavily depending on the methodological setup used. In contrast, we propose a model of the complete innovation process to study innovation speed under uncertainty and competition. We find that higher market uncertainty speeds up innovation and encourages firms to innovate incrementally. Strong competition tends to reduce innovation speed and encourages rather radical innovation.     

Residential mobility and job changes under uncertainty

This paper empirically investigates households' residential mobility and job change decisions under uncertainty. We allow households' degree of risk aversion to be a confounding factor in the joint decision of residential mobility and job changes. Using panel data to estimate a random effects multinomial probit model of households' joint decision of residential and job mobility, our empirical results show that risk aversion discourages a household from making any changes. Moreover, when compared to single changes in either job or residential locations, risk aversion is more discouraging for joint changes to more central residential locations and less discouraging for joint changes to more distant residential locations. These effects are statistically significant, albeit small in magnitude. Our empirical results demonstrate the uncertainty does play a role in households' job and residential mobility decisions.     

Supplier incentives and resource constraints under uncertainty

Procurement in environments of cost uncertainty and asymmetric information require special arrangements such as the linear incentive contract. Usually the buyer is motivated to make investments that can relieve temporary supplier resource constraints during the procurement. Special problems arise, however, due to interactions between investments in suppliers and the risk-incentive trade-off achieved by the incentive contract. A cost signaling model is proposed to overcome these problems, where a supplier offers an equity share in the profit from the incentive contract to the buyer in return for a priori investment. The equity share signals the supplier's private cost information, and forms the basis for the buyer's investment decision. Under equilibrium the buyer can expect to recover the entire amount provided to the supplier through his or her share of the profit.     

The interrelated dynamics of unemployment and low‐wage employment

This paper examines the extent of state dependence in unemployment and the role played in this by intervening low‐wage employment. A range of dynamic random and fixed‐effects estimators are compared. Low‐wage employment is found to have almost as large an adverse effect as unemployment on future prospects and the difference in their effects is found to be insignificant. Evidence is presented that low‐wage jobs act as the main conduit for repeat unemployment and considerably increases its probability. Obtaining a higher‐wage job reduces the increased risk of repeat unemployment to insignificance. Copyright © 2007 John Wiley & Sons, Ltd.     

Empirical factor demands and flexible functional forms: a bayesian approach

In this paper we compare classical econometrics, calibration and Bayesian inference in the context of the empirical analysis of factor demands. Our application is based on a popular flexible functional form for the firm's cost function, namely Diewert's Generalized Leontief function, and uses the well-known Berndt and Wood 1947–1971 KLEM data on the US manufacturing sector. We illustrate how the Gibbs sampling methodology can be easily used to calibrate parameter values and elasticities on the basis of previous knowledge from alternative studies on the same data, but with different functional forms. We rely on a system of mixed non-informative diffuse priors for some key parameters and informative tight priors for others. Within the Gibbs sampler, we employ rejection sampling to incorporate parameter restrictions, which are suggested by economic theory but in general rejected by economic data. Our results show that values of those parameters that relate to non-informative priors are almost equal to the standard SUR estimates, whereas differences come out for those parameters to which we have assigned informative priors. Moreover, discrepancies can be appreciated in some crucial parameter estimates obtained with or without rejection sampling.     

Modelling factor demands with SEM and VAR: an empirical comparison

The empirical analysis of the economic interactions between factors of production, output and corresponding prices has received much attention over the last two decades. Most contributions in this area have agreed on the neoclassical principle of a representative optimizing firm and typically use theory-based structural equation models (SEM). A popular alternative to SEM is the vector autoregression (VAR) methodology. The most recent attempts to link the SEM approach with VAR analysis in the area of factor demands concentrate on single-equation models, whereas no effort has been devoted to compare these alternative approaches when a firm is assumed to face a multi-factor technology and to decide simultaneously the optimal quantity for each input. This paper bridges this gap. First, we illustrate how the SEM and the VAR approaches can both represent valid alternatives to model systems of dynamic factor demands. Second, we show how to apply both methodologies to estimate dynamic factor demands derived from a cost-minimizing capital-labour-energy-materials (KLEM) technology with adjustment costs (ADC) on the quasi-fixed capital factor. Third, we explain how to use both models to calculate some widely accepted indicators of the production structure of an economic sector, such as price and quantity elasticities, and alternative measures of ADC. In particular, we propose and discuss some theoretical and empirical justifications of the differences between observed elasticities, measures of ADC, and the assumption of exogeneity of output and/or input prices. Finally, we offer some suggestions for the applied researcher.      

The competitive firm under price uncertainty: the role of information and hedging

We study the impact of transparency in a commodity market on the decision problem of a competitive firm under price uncertainty and hedging opportunities. Market transparency is modeled by means of the informational content of publicly observable signals which are correlated with the random price. We find that the impact of more transparency on labor employment and production depends on the firm’s technology. In particular, more transparency may result in lower average output even though on average more labor has been used in the production process. We also analyze the link between market transparency and the welfare of the firm. We are grateful to two anonymous referees who made extremely useful comments and suggestions.     

Forecast-error-based estimation of forecast uncertainty when the horizon is increased

Past forecast errors are employed frequently in the estimation of the unconditional forecast uncertainty, and several institutions have increased their forecast horizons in recent times. This work addresses the question of how forecast-error-based estimation can be performed if there are very few errors available for the new forecast horizons. It extends the results of Knüppel (2014) in order to relax the condition on the data structure that is required for the SUR estimator to be independent of unknown quantities. It turns out that the SUR estimator of the forecast uncertainty, which estimates the forecast uncertainty for all horizons jointly, tends to deliver large efficiency gains relative to the OLS estimator (i.e., the sample mean of the squared forecast errors for each individual horizon) in the case of increased forecast horizons. The SUR estimator is applied to the forecast errors of the Bank of England, the US Survey of Professional Forecasters, and the FOMC.     

Efficient estimation of forecast uncertainty based on recent forecast errors

Multi-step-ahead forecasts of the forecast uncertainty of an individual forecaster are often based on the horizon-specific sample means of his recent squared forecast errors, where the number of past forecast errors available decreases one-to-one with the forecast horizon. In this paper, the efficiency gains from the joint estimation of forecast uncertainty for all horizons in such samples are investigated. If the forecast uncertainty is estimated by seemingly unrelated regressions, it turns out that the covariance matrix of the squared forecast errors does not have to be estimated, but simply needs to have a certain structure, which is a very useful property in small samples. Considering optimal and non-optimal forecasts, it is found that the efficiency gains can be substantial for longer horizons in small samples. The superior performance of the seemingly-unrelated-regressions approach is confirmed in several empirical applications.     

The estimation of guaranteed fatigue life under random loading

One of the pressing problems of mechanical reliability still requiring a satisfactory solution is that of ensuring the guaranteed fatigue life of a component or structure subject to random dynamic loading. In the past, this problem has generally been solved in technical practice by the choice of a sufficiently large "safety factor" when dimensioning critically stressed parts of a complex structure.
Application of probability and statistical methods now offers the possibility of developing a theory of reliability of mechanical systems, where the risk of failure can be expressed as a probability, taking into account effects of random loading processes, which characterize either the functioning of the system itself or external environmental operating conditions.
In the following paper we describe one method of approach to the solution of this problem. The solution consists of a combination of the W eibull -F reudenthal -G umbel theory of fatigue estimation using ( , N, P ) relations, the P almgren -M iner hypothesis of linear accumulation of damage and the theory of stationary random processes having a given autocorrelation function or spectral density. Several other stochastic models are discussed in [1]. The subject of this paper was chosen in acknowledgement of the fact that H. C. H amaker in his applied theoretical work also dealt with a related problem concerning the breaking strength of glass [2].     

Lender learning and entry under general demand uncertainty

In this paper, we examine the effect of potential entry on learning by a lender when the demand shock has a general distribution. We show that under this type of noise, entry does not lead to any changes in the equilibrium expected signals and therefore, there is no effect on learning by the lender, unlike the case when noise is uniformly distributed. The result holds even when contracts are not observable.     

Decision-making under uncertainty for capital budgeting and hiring

Most business decisions are not merely risky but are subject to uncertainty. Different individuals estimate different measures of merit. It is shown that the traditional procedure of ranking the alternatives by their estimated measure of merit and then choosing the highest ranked alternative frequently gives the wrong answer even where the estimates are unbiased. The argument will be made using a simple decision tree. The effect will be shown to be especially serious where there is competition, direct or indirect, between the alternatives.