Estimating Sequential Multi-Choice Demand : An Application to Pesticides Utilization in France.

Crop Production/Industries, Demand and Price Analysis,

Forecasting with Spatial Panel Data

This paper compares various forecasts using panel data with spatial error correlation. The true data generating process is assumed to be a simple error component regression model with spatial remainder disturbances of the autoregressive or moving average type. The best linear unbiased predictor is compared with other forecasts ignoring spatial correlation, or ignoring heterogeneity due to the individual effects, using Monte Carlo experiments. In addition, we check the performance of these forecasts under misspecification of the spatial error process, various spatial weight matrices, and heterogeneous rather than homogeneous panel data models.forecasting, BLUP, panel data, spatial dependence, heterogeneity

Pesticides Uses in Crop Production: What Can We Learn from French Farmers Practices?

This article focuses on the demand system of French farmers concerning pesticides uses. We estimate the demand elasticities of herbicides, insecticides and fungicides with respect to pesticide expenditure, and considering crop differentiation. Then we compare two indexes that are used in agronomic literature to measure the intensity of pesticides uses. We retain a Linear Approximated Almost Ideal Demand System (LA/AIDS) specification. A Full-Information Maximum Likelihood estimation procedure is used for dealing with the problem of censored dependent variable. We consider two cross-sections observed in 2001 and 2006 covering pesticides uses of three crops. We confirm the previous results of the literature that farmers response to price variation is very low, with higher prices response in 2006 than in 2001. Moreover, we find that conditional herbicides expenditure elasticities are often higher than insecticides expenditure elasticities, but lower than those of fungicides. We find higher own-price elasticities for herbicides and fungicides than for insecticides, which is the less used. Finally, application dose seems statistically better to explain herbicides decision, whereas treatment frequency index appears better for insecticides and fungicides. However, most of elasticities are closed for dose and treatment frequency index.Pesticides, LA/AIDS, Elasticities, Censored System of Equations, Two-Step procedure, Quasi Maximum Likelihood, Full-Information Maximum Likelihood., Agricultural and Food Policy, Crop Production/Industries, Demand and Price Analysis, C30, C31, C34, L11, Q11, Q12,

Homogeneous, heterogeneous or shrinkage estimators? Some empirical evidence from French regional gasoline consumption

This paper contrasts the performance of heterogeneous and shrinkage estimators versus the more traditional homogeneous panel data estimators. The analysis utilizes a panel data set from 21 French regions over the period 1973-1998 and a dynamic demand specification to study the gasoline demand in France. Out-of-sample forecast performance as well as the plausibility of the various estimators are contrasted.Panel data; French gasoline demand; Error components; Heterogeneous estimators; Shrinkage estimators

Estimating and forecasting with a dynamic spatial panel data model

This paper focuses on the estimation and predictive performance of several estimators for the dynamic and autoregressive spatial lag panel data model with spatially correlated disturbances. In the spirit of Arellano and Bond (1991) and Mutl (2006), a dynamic spatial GMM estimator is proposed based on Kapoor, Kelejian and Prucha (2007) for the Spatial AutoRegressive (SAR) error model. The main idea is to mix non-spatial and spatial instruments to obtain consistent estimates of the parameters. Then, a linear predictor of this spatial dynamic model is derived. Using Monte Carlo simulations, we compare the performance of the GMM spatial estimator to that of spatial and non-spatial estimators and illustrate our approach with an application to new economic geography

Forecasting with spatial panel data

This paper compares various forecasts using panel data with spatial error correlation. The true data generating process is assumed to be a simple error component regression model with spatial remainder disturbances of the autoregressive or moving average type. The best linear unbiased predictor is compared with other forecasts ignoring spatial correlation, or ignoring heterogeneity due to the individual effects, using Monte Carlo experiments. In addition, we check the performance of these forecasts under misspecification of the spatial error process, various spatial weight matrices, and heterogeneous rather than homogeneous panel data models

Panel Data Inference under Spatial Dependence

This paper focuses on inference based on the usual panel data estimators of a one-way error component regression model when the true specification is a spatial error component model. Among the estimators considered, are pooled OLS, random and fixed effects, maximum likelihood under normality, etc. The spatial effects capture the cross-section dependence, and the usual panel data estimators ignore this dependence. Two popular forms of spatial autocorrelation are considered, namely, spatial auto-regressive random effects (SAR-RE) and spatial moving average random effects (SMA-RE). We show that when the spatial coefficients are large, test of hypothesis based on the usual panel data estimators that ignore spatial dependence can lead to misleading inference

Reifying Design Patterns as Metalevel Constructs

A design pattern describes a structure of communicating components that solves a commonly occurring design problem. Designing with patterns offers the possibility of raising the abstraction level at which design is performed, with improvements in clarity, understanding, and facility of maintenance of applications. However, in their most common presentation, design patterns are informal pieces of design process, which application is not reflected in the operational system, and the potential advantages of a more principled design are not realized. This work proposes to organize design in such a way that pattern applications remain explicit in the operational systems. A reflective architecture is proposed, where patterns are reified as metalevel constructs

Joint LM Test for Homoskedasticity in a One-Way error Component Model

This paper considers a general heteroskedastic error component model using panel data, and derives a joint LM test for homoskedasticity against the alternative of heteroskedasticity in both error components. It contrasts this joint LM test with marginal LM tests that ignore the heteroskedasticity in one of the error components. Monte Carlo results show that misleading inference can occur when using marginal rather than joint tests when heteroskedasticity is present in both components

Car traffic, habit persistence, cross-sectional dependence, and spatial heterogeneity:New insights using French departmental data

This paper adopts a dynamic general nesting spatial panel data model with common factors to explore the effect of population density, real household income per capita, car fleet per capita, and real price of gasoline on departmental traffic per light vehicle in France over the period 1990–2009. Spatial heterogeneity is modeled by a translog function in the first three explanatory variables, which are dominated by variation in the cross-sectional domain, while the real price of gasoline, which is dominated by variation in the time domain, is treated as an observable common factor. Additional unobservable common factors are controlled for by principal components with heterogenous coefficients, building on previous work of Shi and Lee (2017a), thereby, generalizing the dynamic spatial panel data model with spatial and time period fixed applied in recent studies. It is found that the spatial lag in the dependent variable becomes insignificant due to these extensions. This paper explains the wider implications of this finding for spatial econometric modeling of cross-sectional dependence. In addition, the elasticities of the first three explanatory variables are shown to vary across space and time and to follow a plausible structure. Among other, an important result is that the long run income elasticity of car traffic diminished from 1.0 in 1990 to 0.4 in 2003, and then remained almost constant until the end of our sample period in 2009, i.e., during the peak-car traffic period