Non-linear impact of exchange rate changes on U.S. industrial production

This paper contributes to the literature on the nexus between production and exchange rate in the United States (U.S.) by considering non-linear adjustments of exchange rate effects on industrial production in several sectors of the U.S. economy. We employ a Non-linear Autoregressive Distributed Lags (NARDL) model which is built upon the Solow model. We show that there exists a non-linear relationship between these two variables in some of the MMIGs. We document short-run non-linear effects of exchange rate on production of non-energy materials, durable manufacturing, consumer goods and business equipment. The short-run effects last into the long-run for all the sectors. While exchange rate changes have short-run linear effects on production of electricity in the U.S., there are no effects of exchange rate movements on the production of mining, and energy materials. Moreover, the paper finds misspecification error of the model for the case of durable manufacturing. The existence of non-linearities considering import content of exports, support our hypothesis and conclusions. Further, the factors that influence demand provide justifications for our results.

Assessing symmetric price transmission by using threshold cointegration in Iranian egg market

Purpose Eggs bear an essential role in Iranian diet, primarily for their protein content. The egg production strictly depends on the price of inputs, that is corn used for poultry feeding. The upsurge in corn prices in recent years gave rise to both consumers’ and producers’ dissatisfaction, increasing production cost and the egg price in the final market. The purpose of this paper is to investigate the price-transmission dynamics between corn and retail egg prices in Iran. Design/methodology/approach Individual commodity price series generally contain stochastic trends and they are non-stationary. Standard unit root and cointegration tests will be conducted in order to determine whether price series are stationary and whether they are cointegrated, respectively. The existence of cointegration between the two-price series depends on the nature of autoregressive process. If there is an asymmetric convergence between two variables, then Engle and Granger’s (1987) test can have a misspecification error and the result cannot indicate nature of variables. Threshold or asymmetric convergence test should be used, which can detect the asymmetric behavior of variables and threshold effects on series. Findings Results showed that, in the long run, owing to price transmission, any price shocks on corn price will be transmitted to the egg price. Practical implications Policy makers should implement input and output price policies to support producer and consumer in the retail market to increase consumer and producer welfare, and they should also control intermediaries in this market. Originality/value This research dealing with price transmission has been concerned only with applying time-series modeling techniques to price data. The main focus of this approach has been to characterize vertical price relationships by the extent, speed and nature of the adjustments through the supply chain to market shocks generated at different levels in the marketing process. Thus, it complements the marketing margin models, which are mainly concerned with testing for market imperfections and calculating the price transmission. Besides these points, particular importance has been given in this research to the question of symmetry of price adjustments.

Robust Design for Profit Maximization With Aversion to Downside Risk From Parametric Uncertainty in Consumer Choice Models

In new product design, risk averse firms must consider downside risk in addition to expected profitability, since some designs are associated with greater market uncertainty than others. We propose an approach to robust optimal product design for profit maximization by introducing an α-profit metric to manage expected profitability vs. downside risk due to uncertainty in market share predictions. Our goal is to maximize profit at a firm-specified level of risk tolerance. Specifically, we find the design that maximizes the α-profit: the value that the firm has a (1 − α) chance of exceeding, given the distribution of possible outcomes. The parameter α ∈ (0,1) is set by the firm to reflect sensitivity to downside risk (or upside gain), and parametric study of α reveals the sensitivity of optimal design choices to firm risk preference. We account here only for uncertainty of choice model parameter estimates due to finite data sampling when the choice model is assumed to be correctly specified (no misspecification error). We apply the delta method to estimate the mapping from uncertainty in discrete choice model parameters to uncertainty of profit outcomes and identify the estimated α-profit as a closed-form function of decision variables for the multinomial logit model. An example demonstrates implementation of the method to find the optimal design characteristics of a dial-readout scale using conjoint data.

The Optimal Size of German Child Care Centers and the Impact of Regulation: Estimating the Cost Function of a Regulated Multi-Product Firm

SummaryUsing a unique data set on German child care centers, we estimate a long-run multi-product cost function for child care provision in Germanywhile taking into account legal minimal labor input restrictions. For a representative child care center we find economies of scale, a U-shaped average cost curve, and indications of diseconomies of scope. The legally stipulated minimum child-to-staff ratio is manifested in a positive Lagrange multiplier, showing that modeling legal restrictions is necessary to avoid misspecification error. Our study provides a useful tool for policymakers in estimating the effects of future demographic change on child care costs.

Robust Design for Profit Maximization Under Uncertainty of Consumer Choice Model Parameters Using the Delta Method

In new product design, risk averse firms must consider downside risk in addition to expected profitability, since some designs are associated with greater market uncertainty than others. We propose an approach to robust optimal product design for profit maximization by introducing an α-profit metric to manage expected profitability vs. downside risk due to uncertainty in market share predictions. Our goal is to maximize profit at a firm-specified level of risk tolerance. Specifically, we find the design that maximizes the α-profit: the value that the firm has a (1−α) chance of exceeding, given the distribution of possible outcomes. The parameter α∈[0,1] is set by the firm to reflect sensitivity to downside risk (or upside gain), and parametric study of α reveals the sensitivity of optimal design choices to firm risk preference. We account here only for uncertainty of choice model parameter estimates due to finite data sampling when the choice model is assumed to be correctly specified (no misspecification error). We apply the delta method to estimate the mapping from uncertainty in discrete choice model parameters to uncertainty of profit outcomes and identify the estimated α-profit as a closed form function of design decision variables. This process is described for the multinomial logit model, and a case study demonstrates implementation of the method to find the optimal design characteristics of a midsize consumer automobile.