(Under)Mining local residential property values: A semiparametric spatial quantile autoregression

Rock mining operations, including limestone and gravel production, have considerable adverse effects on residential quality of life due to elevated noise and dust levels resulting from dynamite blasting and increased truck traffic. This paper provides the first estimates of the effects of rock mining—an environmental disamenity—on local residential property values. We focus on the relationship between a house's price and its distance from a nearby rock mine. Our analysis studies Delaware County, Ohio, which, given its unique features, provides a natural environment for the valuation of property‐value‐suppressing effects of rock mines on nearby houses. We improve upon the conventional approach to evaluating adverse effects of environmental disamenities based on hedonic house price functions. Specifically, in the pursuit of robust estimates, we develop a novel (semiparametric) partially linear spatial quantile autoregressive model which accommodates unspecified nonlinearities, distributional heterogeneity, as well as spatial dependence in the data. We derive the consistency and normality limit results for our estimator as well as propose a consistent model specification test. We find statistically and economically significant property‐value‐suppressing effects of being located near an operational rock mine which gradually decline to insignificant near‐zero values at roughly a 10‐mile distance. Our estimates suggest that, all else equal, a house located a mile closer to a rock mine is priced, on average, at about 2.3–5.1% discount, with more expensive properties being subject to larger markdowns.     

A rationale for planning: Implications from the economic theory of the firm

This paper undertakes the development of a foundation for a theory of why firms plan. The theory of the firm literature provides a useful starting-point and provides insight into the factors which have an effect on the planning function of the firm. Those factors include transactions costs, uncertainty and asset-specificity. The fact that some firms should not plan even in the face of uncertainty and transactions costs emphasizes the importance of the relative magnitudes of various costs and demonstrates the relevance of standard economic analysis for the study and practice of business planning. Examples illustrate the role of planning for human, financial and physical resources.     

Effects of US interest rates and news on the daily interest rates of a highly indebted emerging economy: evidence from Turkey

It has been widely demonstrated that asset prices react sensitively to macroeconomic news releases both in the industrialized countries and emerging markets. However, there are contradicting results on the effects of changes in interest rates of industrialized countries on asset prices of emerging markets. In heavily indebted economies, in addition to these factors, political news and announcements from international institutions that may increase or decrease concerns about debt sustainability can affect asset prices as well. This potential notwithstanding, there has been relatively limited empirical work on the effects of such variables. The objective of this study is to quantify the impact of all of these factors on interest rates of a highly indebted emerging economy. Using daily post-crisis data of the Turkish economy we show that both good and bad political news, International Monetary Fund announcements, and European Union related news significantly affected secondary market government securities yields, whereas volatility of yields was affected mainly by bad news releases. Changes in US Treasury bond rates and ‘appetite’ for risk of foreign investors did not affect government securities yields in the period analysed.     

A generalized panel data switching regression model

This paper considers a generalized panel data model of polychotomous and/or sequential switching which can also accommodate the dependence between unobserved effects and covariates in the model. We showcase our model using an empirical illustration in which we estimate scope economies for the publicly owned electric utilities in the US during the period from 2001 to 2003.     

THE ROLE OF CAPITAL ADEQUACY REGULATION IN THE HEDGING DECISIONS OF FINANCIAL INTERMEDIARIES

This paper identifies the effect of capital adequacy requirements, which have been ignored to date in the hedging literature, on the forward hedging decisions of financial intermediaries. Using a more general framework than has been used in the literature on intermediary behavior in forward markets, cases are developed where capital and forward contracting are substitutes as well as where increasing the capital requirement increases the volume of desired forward contracting. The model shows that the most important factors in determining the equilibrium rate and the equilibrium position of intermediaries are the statistical association between the level of the forward rate and the spread between interest rates, the level of the capital-to-assets ratio, and the degree of risk aversion of intermediaries and other participants in the forward market. To characterize whether the intermediary's optimal forward position is long or short, one must have knowledge of at least the sign of the association between the level and spread for the particular intermediary, the intermediary's capital position, and whether the forward market equilibrium corresponds to a positive or negative premium. The model also demonstrates that a full hedge of assets is always sub-optimal, and a universally applicable expression for the optimal hedge ratio when hedging is costless is derived.     

A Cost System Approach to the Stochastic Directional Technology Distance Function with Undesirable Outputs: The Case of us Banks in 2001–2010

This paper offers a methodology to address the endogeneity of inputs in the directional technology distance function (DTDF)‐based formulation of banking technology which explicitly accommodates the presence of undesirable nonperforming loans—an inherent characteristic of the bank's production due to its exposure to credit risk. Specifically, we model nonperforming loans as an undesirable output in the bank's production process. Since the stochastic DTDF describing banking technology is likely to suffer from the endogeneity of inputs, we propose addressing this problem by considering a system consisting of the DTDF and the first‐order conditions from the bank's cost minimization problem. The first‐order conditions also allow us to identify the ‘cost‐optimal’ directional vector for the banking DTDF, thus eliminating the uncertainty associated with an ad hoc choice of the direction. We apply our cost system approach to the data on large US commercial banks for the 2001–2010 period, which we estimate via Bayesian Markov chain Monte Carlo methods subject to theoretical regularity conditions. We document dramatic distortions in banks' efficiency, productivity growth and scale elasticity estimates when the endogeneity of inputs is assumed away and/or the DTDF is fitted in an arbitrary direction. Copyright © 2015 John Wiley & Sons, Ltd.     

Estimation of firm-level productivity in the presence of exports: Evidence from China's manufacturing

Motivated by the long-standing interest of economists in understanding the nexus between firm productivity and export behavior, this paper develops a novel structural framework for control-function-based nonparametric identification of the gross production function and latent firm productivity in the presence of endogenous export opportunities that is robust to recent unidentification critiques of proxy estimators. We provide a workable identification strategy, whereby the firm's degree of export orientation provides the needed (excluded) relevant independent exogenous variation in endogenous freely varying inputs, thus allowing us to identify the production function. We estimate our fully nonparametric instrumental variable model using the Landweber–Fridman regularization with the unknown functions approximated via artificial neural network sieves with a sigmoid activation function, which are known for their superior performance relative to other popular sieve approximators, including the polynomial series favored in the literature. Using our methodology, we obtain robust productivity estimates for manufacturing firms from 28 industries in China during the 1999–2006 period to take a close look at China's exporter productivity puzzle, whereby exporters are found to exhibit lower productivity levels than nonexports.