Modeling Conditional Yield Densities

Given the increasing interest in agricultural risk, many have sought improved methods to characterize conditional crop-yield densities. While most have postulated the Beta as a flexible alternative to the Normal, others have chosen nonparametric methods. Unfortunately, yield data tends not to be sufficiently abundant to invalidate many reasonable parametric models. This is problematic because conclusions from economic analyses, which require estimated conditional yield densities, tend not to be invariant to the modeling assumption. We propose a semiparametric estimator that, because of its theoretical properties and our simulation results, enables one to empirically proceed with a higher degree of confidence.     

On estimating the mean of the selected normal population under the LINEX loss function

Following Parsian and Farsipour (1999), we consider the problem of estimating the mean of the selected normal population, from two normal populations with unknown means and common known variance, under the LINEX loss function. Some admissibility results for a subclass of equivariant estimators are derived and a sufficient condition for the inadmissibility of an arbitrary equivariant estimator is provided. As a consequence, several of the estimators proposed by Parsian and Farsipour (1999) are shown to be inadmissible and better estimators are obtained. Received January 2001/Revised May 2002     

Nonparametric efficiency analysis: A multivariate conditional quantile approach

This paper focuses on nonparametric efficiency analysis based on robust estimation of partial frontiers in a complete multivariate setup (multiple inputs and multiple outputs). It introduces α-quantile efficiency scores. A nonparametric estimator is proposed achieving strong consistency and asymptotic normality. Then if α increases to one as a function of the sample size we recover the properties of the FDH estimator. But our estimator is more robust to the perturbations in data, since it attains a finite gross-error sensitivity. Environmental variables can be introduced to evaluate efficiencies and a consistent estimator is proposed. Numerical examples illustrate the usefulness of the approach.     

The Impact of Non-Normality and Estimation Methods in SEM on Satisfaction Research in Marketing

This paper discusses consequences of violating the normal distribution assumption imbedded in Structural Equation Modeling (SEM). Based on real data from a large sample customer satisfaction survey we follow the procedures as suggested in leading textbooks. We document consequences of this practice and discuss its impact on decision making in marketing.     

Pseudo empirical likelihood method in the presence of missing data

In this paper, we propose an estimator for the population mean when some observations on the study and auxiliary variables are missing from the sample. The proposed estimator is valid for any unequal probability sampling design, and is based upon the pseudo empirical likelihood method. The proposed estimator is compared with other estimators in a simulation study.     

Restricted maximum likelihood estimation under Eisenhart model Ill

For a balanced two-way mixed model, the maximum likelihood (ML) and restricted ML (REML) estimators of the variance components were obtained and compared under the non-negativity requirements of the variance components by L ee and K apadia (1984). In this note, for a mixed (random blocks) incomplete block model, explicit forms for the REML estimators of variance components are obtained. They are always non-negative and have smaller mean squared error (MSE) than the analysis of variance (AOV) estimators. The asymptotic sampling variances of the maximum likelihood (ML) estimators and the REML estimators are compared and the balanced incomplete block design (BIBD) is considered as a special case. The ML estimators are shown to have smaller asymptotic variances than the REML estimators, but a numerical result in the randomized complete block design (RCBD) demonstrated that the performances of the REML and ML estimators are not much different in the MSE sense.     

Export Sophistication: A Dynamic Panel Data Approach

In this article, we analyze export sophistication based on a large panel dataset (2001–2015; 101 countries) and using various estimation algorithms. Using Monte Carlo simulations, we evaluate the bias properties of estimators and show that GMM-type estimators outperform instrumental-variable and fixed-effects estimators. Based on our analysis we document that GDP per capita and the size of the economy exhibit significant and positive effects on export sophistication; weak institutional quality exhibits negative effect. We also show that export sophistication is path-dependent and stable even during a major economic crisis, which is especially important for emerging and developing economies.     

On estimating long-run effects in models with lagged dependent variables

A common procedure in economics is to estimate long-run effects from models with lagged dependent variables. For example, macro panel studies frequently are concerned with estimating the long-run impacts of fiscal policy, international aid, or foreign investment.Our analysis points out the hazards of this practice. We use Monte Carlo experiments to demonstrate that estimating long-run impacts from dynamic models produces unreliable results.Biases can be substantial, sample ranges very wide, and hypothesis tests can be rendered useless in realistic data environments. There are three reasons for this poor performance. First, OLS estimates of the coefficient of a lagged dependent variable are downwardly biased in finite samples. Second, small biases in the estimate of the lagged, dependent variable coefficient are magnified in the calculation of long-run effects. And third, and perhaps most importantly, the statistical distribution associated with estimates of the LRP is complicated, heavy-tailed, and difficult to use for hypothesis testing. While many of the underlying problems have been long-known in the literature, the continued widespread use of the associated empirical procedures suggests that researchers are unaware of the extent and severity of the estimation problems. This study aims to illustrate their practical importance for applied research.     

Impact of contracts in high yielding varieties seed production on profits and yield: The case of Nepal

Nepal’s population continues to grow, but the agricultural sector’s performance remains almost stagnant. This has led to a decline in the per capita availability of food. Increasing agricultural productivity is the key to agricultural growth, and one strategy for increasing agricultural productivity is to use improved seeds. This study investigates the impact of contract farming (CF) in high yielding varieties (HYV) of paddy seed production on costs, yield, and profits of smallholder farms in Nepal. Using farm-level data and a non-parametric propensity score matching estimator, the study finds a significant positive impact of contract HYV seed farming on revenues, profits, and yield, and a significant negative impact on total costs of production. Additionally, very small farms (⩽0.43 ha) with CF in HYV paddy seeds tend to gain the most when it comes to yield per hectare. Our estimates reveal that the average smallholder household in Nepal engaged in CF with input conditions receives higher profits. However, farmers engaged in CF with output conditions tend to have higher yields but smaller profits. Finally, farmers engaged in CF in HYV paddy seeds with both input and output conditions have the highest yield gains and significantly higher profits.     

Some Variants of Constrained Estimation in Finite Population Sampling

This is a review article that unifies several important examples using constrained optimisation techniques. The basic tools are three simple mathematical optimisation results subject to certain constraints. Applications include calibration, benchmarking in small area estimation and imputation. A final illustration is constrained optimisation under a general divergence loss.