Choosing instrumental variables in conditional moment restriction models

Properties of GMM estimators are sensitive to the choice of instrument. Using many instruments leads to high asymptotic asymptotic efficiency but can cause high bias and/or variance in small samples. In this paper we develop and implement asymptotic mean square error (MSE) based criteria for instrument selection in estimation of conditional moment restriction models. The models we consider include various nonlinear simultaneous equations models with unknown heteroskedasticity. We develop moment selection criteria for the familiar two-step optimal GMM estimator (GMM), a bias corrected version, and generalized empirical likelihood estimators (GEL), that include the continuous updating estimator (CUE) as a special case. We also find that the CUE has lower higher-order variance than the bias-corrected GMM estimator, and that the higher-order efficiency of other GEL estimators depends on conditional kurtosis of the moments.     

A DYNAMIC DISCRETE CHOICE MODEL OF REVERSE MORTGAGE BORROWER BEHAVIOR

Using unique data on reverse mortgage borrowers in the Home Equity Conversion Mortgage (HECM) program, we semiparametrically estimate a dynamic discrete choice model of borrower behavior. Our estimator is based on a new identification result we develop for models with multiple terminating actions. We show that the per-period utility functions and discount factor are identified without restrictive, ad hoc identifying restrictions that lead to incorrect counterfactual implications. Our estimates provide insights about factors that influence HECM refinance, default, and termination decisions and allow us to quantify the trade-offs involved for proposed program modifications, such as income and credit requirements.     

Linking the formal strategic planning process,planning flexibility,and innovativeness to firm performance

This study explores the link between financial performance and the formal strategic planning process, planning flexibility, and innovativeness of 448 firms in a multi-industry sample. The results suggest that firms' formal strategic planning processes and planning flexibility are positively associated, and each is positively related to innovativeness. In addition, innovativeness fully mediates the relationships between firm performance and the formal strategic planning process and planning flexibility.     

Modern Likelihood‐Frequentist Inference

We offer an exposition of modern higher order likelihood inference and introduce software to implement this in a quite general setting. The aim is to make more accessible an important development in statistical theory and practice. The software, implemented in an R package, requires only that the user provide code to compute the likelihood function and to specify extra‐likelihood aspects of the model, such as stopping rule or censoring model, through a function generating a dataset under the model. The exposition charts a narrow course through the developments, intending thereby to make these more widely accessible. It includes the likelihood ratio approximation to the distribution of the maximum likelihood estimator, that is the p^? formula, and the transformation of this yielding a second‐order approximation to the distribution of the signed likelihood ratio test statistic, based on a modified signed likelihood ratio statistic r^?. This follows developments of Barndorff‐Nielsen and others. The software utilises the approximation to required Jacobians as developed by Skovgaard, which is included in the exposition. Several examples of using the software are provided.     

Determination of Long‐run and Short‐run Dynamics in EC‐VARMA Models via Canonical Correlations

This article studies a simple, coherent approach for identifying and estimating error‐correcting vector autoregressive moving average (EC‐VARMA) models. Canonical correlation analysis is implemented for both determining the cointegrating rank, using a strongly consistent method, and identifying the short‐run VARMA dynamics, using the scalar component methodology. Finite‐sample performance is evaluated via Monte Carlo simulations and the approach is applied to modelling and forecasting US interest rates. The results reveal that EC‐VARMA models generate significantly more accurate out‐of‐sample forecasts than vector error correction models (VECMs), especially for short horizons. Copyright © 2015 John Wiley & Sons, Ltd.     

Agricultural Policies and Trade Paths in Turkey

In 1959, shortly after the European Economic Community was founded under the 1957 Treaty of Rome, Turkey applied for Associate Membership of the then six‐member common market. By 1963, a path for integrating the economies of Turkey and the eventual European Union had been mapped. As with many trade agreements, agriculture posed difficult political hurdles, which were never fully cleared, even as trade barriers to other sectors were eventually removed and a Customs Union formed. In this paper, we trace the influences the Turkey‐EU economic institutions have had on agricultural policies and the agricultural sector. Using an applied general equilibrium framework we provide estimates of what including agriculture under the Customs Union would mean for the sector and the economy. We also discuss the implications of fully aligning Turkey's agricultural policies with the EU's Common Agricultural Policy, as would be required under full membership.     

Local and global spatial effects in hierarchical models

Hierarchical models have a long history in empirical applications; recognition of the fact that many datasets of interest to applied econometricians are nested; counties within states, pupils within school, regions within countries, etc. Just as many datasets are characterized by nesting, many are also characterized by the presence of spatial dependence or spatial heterogeneity. Significant advances have been made in developing econometric techniques and models to allow applied econometricians to address this spatial dimension to their data. This article fuses these two literatures together and combines a hierarchical model with the two general spatial econometric models.     

Technical efficiency in competing panel data models: a study of Norwegian grain farming

Estimation of technical efficiency is widely used in empirical research using both cross-sectional and panel data. Although several stochastic frontier models for panel data are available, only a few of them are normally applied in empirical research. In this article we chose a broad selection of such models based on different assumptions and specifications of heterogeneity, heteroskedasticity and technical inefficiency. We applied these models to a single dataset from Norwegian grain farmers for the period 2004–2008. We also introduced a new model that disentangles firm effects from persistent (time-invariant) and residual (time-varying) technical inefficiency. We found that efficiency results are quite sensitive to how inefficiency is modeled and interpreted. Consequently, we recommend that future empirical research should pay more attention to modeling and interpreting inefficiency as well as to the assumptions underlying each model when using panel data.