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Nearest neighbor imputation has a long tradition for handling item nonresponse in survey sampling. In this article, we study the asymptotic properties of the nearest neighbor imputation estimator for general population parameters, including population means, proportions and quantiles. For variance estimation, we propose novel replication variance estimation, which is asymptotically valid and straightforward to implement. The main idea is to construct replicates of the estimator directly based on its asymptotically linear terms, instead of individual records of variables. The simulation results show that nearest neighbor imputation and the proposed variance estimation provide valid inferences for general population parameters.

It is shown in the literature that the Arellano–Bond type generalized method of moments (GMM) of dynamic panel models is asymptotically biased (e.g., Hsiao & Zhang, 2015; Hsiao & Zhou, 2017). To correct the asymptotical bias of Arellano–Bond GMM, the authors suggest to use the jackknife instrumental variables estimation (JIVE) and also show that the JIVE of Arellano–Bond GMM is indeed asymptotically unbiased. Monte Carlo studies are conducted to compare the performance of the JIVE as well as Arellano–Bond GMM for linear dynamic panels. The authors demonstrate that the reliability of statistical inference depends critically on whether an estimator is asymptotically unbiased or not.

This chapter shows how a weighted average of a forward and reverse Jackknife IV estimator (JIVE) yields estimators that are robust against heteroscedasticity and many instruments. These estimators, called HFUL (Heteroscedasticity robust Fuller) and HLIM (Heteroskedasticity robust limited information maximum likelihood (LIML)) were introduced by Hausman, Newey, Woutersen, Chao, and Swanson (2012), but without derivation. Combining consistent estimators is a theme that is associated with Jerry Hausman and, therefore, we present this derivation in this volume. Additionally, and in order to further understand and interpret HFUL and HLIM in the context of jackknife type variance ratio estimators, we show that a new variant of HLIM, under specific grouped data settings with dummy instruments, simplifies to the Bekker and van der Ploeg (2005) MM (method of moments) estimator.

China’s economy is transforming at a brisk pace. A partially dismantled command economy and introduction of competition have fueled consumer demand for a greater selection of innovative new products in the retail market. The challenge for retail buyers is to adjust their procurement processes to respond to consumer needs in an efficient and effective manner. This study examines factors influencing buyer‐supplier relationships in a transition economy. We present a model to explain the factors driving retail buyer dependence on suppliers. We find that retailer evaluation of supplier credibility mediates the relationship between retailer perceptions of a supplier ability to add value to its business and the ability to achieve its desired goals. In part, this is due to the supplier’s market orientation. Interestingly, guanxi ties have no impact on the retailer perceptions of the supplier credibility, but have a positive affect on retailer dependence on its supplier partners.

This chapter analyzes the properties of an alternative least-squares based estimator for linear panel data models with general predetermined regressors. This approach uses backward means of regressors to approximate individual specific fixed effects (FE). The author analyzes sufficient conditions for this estimator to be asymptotically efficient, and argue that, in comparison with the FE estimator, the use of backward means leads to a non-trivial bias-variance tradeoff. The author complements theoretical analysis with an extensive Monte Carlo study, where the author finds that some of the currently available results for restricted AR(1) model cannot be easily generalized, and should be extrapolated with caution.

The purpose of this paper is to identify the factors associated with farm financial success for those farms known to produce for local supply chains. The analysis considers alternative measures of farm financial performance and considers the role of the local foods supply chain in the choice to market locally.

The paper uses a two-stage Heckman approach which addresses the possibility of sample selection bias. In the first stage, the choice model to engage in direct marketing is estimated. In the second stage, the authors estimate a model of the financial performance of those in the sample that direct marketed which includes an IMR term calculated from the parameters of the first stage equation. The analysis uses national farm-level data from the Agricultural and Resource Management Survey of the US Department of Agriculture and combines data from 2009 to 2012 to overcome the constraint of small samples.

Indicators of the development of a local foods supply were positively related to the choice to engage in direct marketing. Factors affecting farm financial performance varied significantly between a short-term and a long-term measure. The results emphasize the importance of considering multiple outcome measures, developing local supply chains and provide implications about beginning farms.

If a local foods system is going to thrive, the farms that market the agricultural products in the local food system must attain a certain level of profitability. The value of the analysis is an improved understanding of the financial performance of farms producing for a small, but growing segment of the food supply chain.

The purpose of this paper is to use stochastic frontier analysis (SFA) to estimate the efficiency of public investments and their impact on economic growth in the USA using panel data. Results of the study show highly significant and positive relationships between gross state product (GSP) and expenditures on education, transportation, health, welfare, and public safety (police and fire), and negative but significant relationships between output and employment in health care and public safety services. Inefficiencies in the study are measured using per capita tax revenue and time. Tax revenue has a very minimal positive and significant effect on efficiency, while time inversely relates to efficiency.

The present study uses SFA to investigate the efficiency of government expenditures in five service sectors – education, transportation, health, welfare, and public safety (police and fire), using recent data and economic trends. The study hypothesizes that changes in the current levels of expenditures in the public sector have a significant impact on the aggregate economy, as measured by GSP. The study uses GSP as the dependent (output) variable, and government expenditure on the five service sectors as the independent (input) variables.

Analysis of efficiency for individual states for all 21 years produced interesting results. Overall, the technical efficiency of the public sector was quite high. The average TE score across all years and all states was 0.878. This suggests that public sector operates at a relatively high efficiency level.

The current SFA model followed Battese and Coelli approach of estimating efficiency of public sectors in each state of the USA. It allowed estimation of policy impact on the overall efficiency. It was applied to macroeconomic panel data.

Technology may produce disruptive changes and market turbulence in any industry. Organizational inertia becomes relevant as a factor that adversely affects organizational transformation; this study aims to examine how to overcome it and its consequences to firms.

The model estimation with seemingly unrelated regression and two-stage least square. The authors build a data set of years 2015–2019 from the Lima Stock Exchange firms to test the hypotheses.

In this research, using the evolutionary-ecological theory of Hannan and Freeman, the study shows the consequences of organizational inertia on marketing intensity and subsequently on firms' financial results.

This study presents an inter-functional model that links organizational behavior, marketing and finance functions, through the marketing value chain to overcome organizational inertia and create firm value.

La tecnología puede producir cambios disruptivos y turbulencias en el mercado en cualquier industria. La inercia organizacional cobra relevancia como factor que incide negativamente en la transformación organizacional; esta investigación examina cómo superarlo y sus consecuencias para las empresas.

La estimación del modelo es con regresión aparentemente no relacionada (SUR) y mínimos cuadrados de dos etapas (2SLS). Para probar las hipótesis, los autores construyen un conjunto de datos de los años 2015–2019 de firmas de la bolsa de valores de Lima.

En esta investigación, utilizando la teoría ecológica evolutiva de Hannan y Freeman, el estudio muestra las consecuencias de la inercia organizacional en la intensidad del marketing y, posteriormente, en los resultados financieros de las empresas.

Este estudio presenta un modelo interfuncional que vincula el comportamiento organizacional, las funciones de marketing y finanzas, a través de la cadena de valor del marketing para superar la inercia organizacional y crear valor para la empresa.