COEFFICIENT CHANGE,PRICE EFFECTS,AND IMPLICIT ELASTICITIES: ESTIMATING MICROECONOMIC DETERMINANTS OVER TWO TIME PERIODS

This paper presents and estimates an input–output model in which input coefficient changes are functions of changing prices. The model produces results that mirror the characteristics of input demand functions based on the model of cost minimization subject to producing a desired level of output. It does not rely on the specification of a functional form for input coefficients, and it does not require the use of assumptions regarding the elasticity of substitution. Instead, it allows the actual price and coefficient changes that occur between periods to identify the implicit elasticities and own- and cross-price derivatives. Using this model, it is shown how accurate measures of price effects, including the full array of own and cross-elasticities of demand, can be estimated for models comprising up to 15 sectors given data for only two time periods.     

Structural Analysis of Electricity Demand and Supply Interactions*

We specify a structural asymmetric vector error‐correction model to identify and estimate the demand and supply functions in hourly day‐ahead wholesale electricity markets. In doing so, we provide, inter alia, new insights into a well‐established but unresolved issue concerning the extent of the demand elasticity to price in these markets. We show that whilst demand appears to be inelastic in the short‐run, the quantity traded on the market is significantly influenced by the price level and responds to previous disequilibria in the supply curve through an asymmetric error‐correction mechanism, reacting to a positive disequilibrium but not to a negative one.     

Scarcity, regulation and endogenous technical progress

This paper studies to which extent a firm using a scarce resource input and facing environmental regulation can still manage to have a sustainable growth of output and profits. The firm has a vintage capital technology with two complementary factors, capital and a resource input subject to quota, the latter being increasingly scarce through an exogenously rising price. The firm can scrap obsolete capital and invest in adoptive and/or innovative R&D resource-saving activities. Within this realistic framework, we first characterize long-term growth regimes driven by scarcity (induced-innovation) vs. long-term growth regimes driven by quota regulation (Porter-like innovation). More importantly, we study the interaction between scarcity and quota regulation. In particular, we show that there exists a threshold level for the growth rate of the resource price above which the Porter mechanism is killed while the scarcity-induced growth regime may emerge. Symmetrically, we also find that there must exist a threshold value for the environmental quota under which the growth regime induced by scarcity vanishes while the Porter-like growth regime may survive.     

On implied volatility for options—Some reasons to smile and more to correct

We analyze the properties of the implied volatility, the commonly used volatility estimator by direct option price inversion. It is found that the implied volatility is subject to a systematic bias in the presence of pricing errors, which makes it inconsistent to the underlying volatility. We propose an estimator of the underlying volatility by first estimating nonparametrically the option price function, followed by inverting the nonparametrically estimated price. It is shown that the approach removes the adverse impacts of the pricing errors and produces a consistent volatility estimator for a wide range of option price models. We demonstrate the effectiveness of the proposed approach by numerical simulation and empirical analysis on S&P 500 option data.     

Quasi-experimental evidence on the effect of aircraft noise on apartment rents

Inferring the implicit price of an environmental good hinges on ceteris paribus conditions that are often hard to justify. This paper uses an unexpected change in flight regulations as source of exogenous variation and identifies aircraft noise effects from price adjustments in the market for rental apartments. Controlling for spatial and apartment heterogeneity, we find that aircraft noise reduces apartment rents by about 0.5% per decibel. Our results indicate (i) that noise discounts are overestimated in cross-sectional studies because aircraft noise tends to be negatively correlated with omitted neighborhood and housing amenities and (ii) that noise effects are unlikely to be constant over the entire noise range.     

Consumption and labor supply

We present a new econometric model of aggregate demand and labor supply for the United States. We also analyze the allocation full wealth among time periods for households distinguished by a variety of demographic characteristics. The model is estimated using micro-level data from the Consumer Expenditure Surveys supplemented with price information obtained from the Consumer Price Index. An important feature of our approach is that aggregate demands and labor supply can be represented in closed form while accounting for the substantial heterogeneity in behavior that is found in household-level data. As a result, we are able to explain the patterns of aggregate demand and labor supply in the data despite using a parametrically parsimonious specification.     

Gasoline prices,transport costs,and the U.S. business cycles

The effects of gasoline prices on the U.S. business cycles are investigated. In order to distinguish between gasoline supply and gasoline demand shocks, the price of gasoline is endogenously determined through a transportation sector that uses gasoline as an input of production. The model is estimated for the U.S. economy using five macroeconomic time series, including data on transport costs and gasoline prices. The results show that although standard shocks in the literature (e.g., technology shocks, monetary policy shocks) have significant effects on the U.S. business cycles in the long run, gasoline supply and demand shocks play an important role in the short run.     

Quantile Autoregressive Distributed Lag Model with an Application to House Price Returns*

This article studies quantile regression in an autoregressive dynamic framework with exogenous stationary covariates. We demonstrate the potential of the quantile autoregressive distributed lag model with an application to house price returns in the United Kingdom. The results show that house price returns present a heterogeneous autoregressive behaviour across the quantiles. Real GDP growth and interest rates also have an asymmetric impact on house prices variations.     

A Bayesian mixed logit–probit model for multinomial choice

In this paper, we introduce a new flexible mixed model for multinomial discrete choice where the key individual- and alternative-specific parameters of interest are allowed to follow an assumption-free nonparametric density specification, while other alternative-specific coefficients are assumed to be drawn from a multivariate Normal distribution, which eliminates the independence of irrelevant alternatives assumption at the individual level. A hierarchical specification of our model allows us to break down a complex data structure into a set of submodels with the desired features that are naturally assembled in the original system. We estimate the model, using a Bayesian Markov Chain Monte Carlo technique with a multivariate Dirichlet Process (DP) prior on the coefficients with nonparametrically estimated density. We employ a “latent class” sampling algorithm, which is applicable to a general class of models, including non-conjugate DP base priors. The model is applied to supermarket choices of a panel of Houston households whose shopping behavior was observed over a 24-month period in years 2004–2005. We estimate the nonparametric density of two key variables of interest: the price of a basket of goods based on scanner data, and driving distance to the supermarket based on their respective locations. Our semi-parametric approach allows us to identify a complex multi-modal preference distribution, which distinguishes between inframarginal consumers and consumers who strongly value either lower prices or shopping convenience.     

Inferring Policy Objectives from Economic Outcomes*

Estimated policy rules are reduced‐form equations that are silent on many important policy questions. However, a structural understanding of monetary policy can be obtained by estimating a policymaker's objective function. The paper derives conditions under which the parameters in a policymaker's policy objective function can be identified and estimated. We apply these conditions to a New Keynesian sticky‐price model of the US economy. The results show that the implicit inflation target and the relative weight placed on interest rate smoothing both declined when Paul Volcker was appointed Federal Reserve chairman.     

Limited information Bayesian analysis of a simultaneous equation with an autocorrelated error term and its application to the U.S. gasoline market

Using Markov Chain Monte Carlo algorithms within the limited information Bayesian framework, we estimate the parameters of the structural equation of interest and test weak exogeneity in a simultaneous equation model with white noise as well as autocorrelated error terms. A numerical example and an estimation of the supply and demand equations of the U.S. gasoline market show that if we ignore autocorrelation we obtain unreasonable posterior distributions of the parameters of interest. Also we find that the hypothesis of the asymmetric effect of the changes in oil price on the changes in gasoline price is rejected. Oil inventory has a significant negative effect on the gasoline price.     

Spatial Price Adjustment with and without Trade*

We investigate the possibility that price transmission between spatially distinct markets might vary during periods with and without physical trade flows. We test for differences between trade and non‐trade regimes by using generalized reduced rank regression (GRRR) techniques suggested by Hansen (2003) . We apply these techniques to semi‐weekly price and trade flow data for tomato markets in Zimbabwe and find that intermarket price adjustment occurs in both trade and non‐trade periods. Indeed, the adjustments are generally larger and more rapid in periods without physical trade flows. This finding underscores the importance of information flow for market performance.     

Unintended consequences of child care regulations

The effects of regulations governing staff-child ratio, group size, and staff qualifications in child care centers are estimated, using data on a sample of centers. The data contain measures of staff characteristics and wages, price of the service, and the developmental quality of the child care provided. Regulations vary across states, but may be endogenous to these outcomes. Estimates with state fixed effects are feasible because regulations vary within states by age group of children and job title of staff. Estimates with state fixed effects show that tougher regulations have some impact on input use, but have little or no impact on price and quality. The most striking finding is that tougher regulations reduce staff wages, suggesting that the incidence of child care regulations is on employees of day care centers.     

Global identification of risk preferences with revealed preference data

The concept of parameter identification (for a given specification) is differentiated from global identification (which specification is right). First-order conditions for production under risk are shown to admit many alternative specification pairs representing risk preferences and either perceived price risk, production risk, or the deterministic production structure. Imposing an arbitrary specification on any of the latter three determines which risk preference specification fits a given dataset, undermining global identification even when parameter identification is suggested by typical statistics. This lack of identification is not relaxed by increasing the number of observations. Critical implications for estimation of mean-variance specifications are derived.     

Production location and random input price

The purpose of this paper is to develop systematically the theory of plant location for a competitive firm facing random input price. It will be shown that the impact of input price uncertainty on the firm's optimum location depend crucially upon (i) the firm's attitude toward risk, (ii) the characteristics of the production function, (iii) the structure of transport costs on inputs and output, and (iv) the type of input usages. Moreover, and more importantly, some conclusions obtained by prior studies on location theory in a certainty world can also be shown to be special cases of our more general results, but some are not justifiable in a world with random input price.     

Prices and factor demand in an endogenized input–output model

This paper combines factor demand functions (for intermediate input and labour) and price equations (derived from a Generalized Leontief cost function) with the traditional input–output price model. The cost functions determine factor demands for materials and labour as well as output prices at given input prices. At the second level of aggregation, the intermediate demand as a single input is split proportional to the elements in the column of the technical coefficients matrix. The emphasis in this endogenization of technical coefficients is on two features. First, the repercussion of output on input prices, and, second, the link between the econometric model for the supply side and the input–output demand model.     

Econometric analysis of jump-driven stochastic volatility models

This paper introduces and studies the econometric properties of a general new class of models, which I refer to as jump-driven stochastic volatility models, in which the volatility is a moving average of past jumps. I focus attention on two particular semiparametric classes of jump-driven stochastic volatility models. In the first, the price has a continuous component with time-varying volatility and time-homogeneous jumps. The second jump-driven stochastic volatility model analyzed here has only jumps in the price, which have time-varying size. In the empirical application I model the memory of the stochastic variance with a CARMA(2,1) kernel and set the jumps in the variance to be proportional to the squared price jumps. The estimation, which is based on matching moments of certain realized power variation statistics calculated from high-frequency foreign exchange data, shows that the jump-driven stochastic volatility model containing continuous component in the price performs best. It outperforms a standard two-factor affine jump–diffusion model, but also the pure-jump jump-driven stochastic volatility model for the particular jump specification.     

Incentive pay and rent in oligopoly

Our purpose is to examine strategic delegation in nonlinear Cournot oligopoly. The findings generalize earlier results and show that managerial contracts reward sales under the condition of a fixed input price. Alternatively, under a variable input price, owners might punish sales even when goods are strategic substitutes. We conclude that optimal strategic motivation depends critically on the input price. For example, motivation that supports positive owner profit under a fixed input price nullifies owner-profit if an upstream monopolist with convex costs sets the input price. In a vertical relationship between a duopoly and an upstream monopolist, strategic delegation punishes sales.     

Behavioural heterogeneity and shift-contagion: Evidence from the Asian crisis

In this paper, we propose an empirical model based on the heterogeneous agents literature. Price changes are induced by fundamental, technical, and international factors. The model is estimated for Hong Kong and Thailand surrounding the Asian crisis. We find that the three sources are relevant and that their relative price impact fluctuates conditional on price impact in the previous period. Results imply that the crisis is triggered in Thailand due to an increased focus on the fundamental price, followed by an increase in chartism and finally aggravated by a focus on foreign developments. Furthermore, the crisis deepens in Hong Kong because of increased attention for foreign markets.     

A Bayesian approach to imposing curvature on distance functions

The estimated parameters of output distance functions frequently violate the monotonicity, quasi-convexity and convexity constraints implied by economic theory, leading to estimated elasticities and shadow prices that are incorrectly signed, and ultimately to perverse conclusions concerning the effects of input and output changes on productivity growth and relative efficiency levels. We show how a Bayesian approach can be used to impose these constraints on the parameters of a translog output distance function. Implementing the approach involves the use of a Gibbs sampler with data augmentation. A Metropolis–Hastings algorithm is also used within the Gibbs to simulate observations from truncated pdfs. Our methods are developed for the case where panel data is available and technical inefficiency effects are assumed to be time-invariant. Two models—a fixed effects model and a random effects model—are developed and applied to panel data on 17 European railways. We observe significant changes in estimated elasticities and shadow price ratios when regularity restrictions are imposed.