Panel Data Estimation Techniques and Farm‐level Data Models

Platoni, Silvia; Sckokai, Paolo; Moro, Daniele

doi:10.1093/ajae/aas072

Cited by 14 publications

(8 citation statements)

References 27 publications

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“…The estimation codes are of course rather sophisticated and must be prepared by an expert econometrician, since the estimation techniques for unbalanced panel data, where censoring (i.e. farms not producing a given output) plays an important role, are continuously evolving (see Platoni, Sckokai and Moro, 2012). However, given the homogeneity of the FADN database, once the econometric code has been prepared, it can easily be estimated for other EU countries, potentially to all EU-27.…”

Section: Models Addressing Risk and Uncertainty In Decision Makingmentioning

confidence: 99%

Farm-household investment behaviour and the CAP decoupling: Methodological issues in assessing policy impacts

Viaggi

Raggi

Paloma

2011

Journal of Policy Modeling

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Section: Models Addressing Risk and Uncertainty In Decision Makingmentioning

confidence: 99%

Farm-household investment behaviour and the CAP decoupling: Methodological issues in assessing policy impacts

Viaggi

Raggi

Paloma

2011

Journal of Policy Modeling

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“…These crop acreage choice models display one kink and, potentially, a jump at the crop return level where farmers are indifferent between planting the considered crop or not. Being based on extensions of generalized Tobit models, recent CR-MEMC models (e.g., Moro, 2006, 2009;Lacroix and Thomas, 2011;Bateman and Fezzi, 2011;Platoni et al, 2012) implicitly account for production regime costs.…”

Section: Corners Kinks and Jumps In Acreage Choice Modelsmentioning

confidence: 99%

“…A few recent MEMC models explicitly account for null crop acreages (e.g., Moro, 2006, 2009;Lacroix and Thomas, 2011;Bateman and Fezzi, 2011;Platoni et al, 2012). 2 These models are designed as censored regression (CR) systems and are estimated following two-step approaches inspired by that initially proposed by Shonkwiler and Yen (1999).…”

Section: Introductionmentioning

confidence: 99%

Modeling Corners, Kinks, and Jumps in Crop Acreage Choices: Impacts of the EU Support to Protein Crops

Koutchade¹,

Carpentier²,

Femenia³

2020

American J Agri Economics

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Null crop acreages raise pervasive issues when modelling acreage choices with farm data. We revisit these issues and emphasize that null acreage choices arise not only due to binding nonnegativity constraints but also due to crop production fixed costs. Based on this micro-economic background, we present a micro-econometric multi-crop model that consistently handles null acreages and accounts for crop production fixed costs. This multivariate endogenous regime switching model allows for specific crop acreage patterns, such as multiple kinks and jumps in crop acreage responses to economic incentives, that are due to changes in produced crop sets. Currently available micro-econometric multi-crop models, which handle null acreages based on a censored regression approach, cannot represent these patterns. We illustrate the empirical tractability of our modelling framework by estimating a random parameter version of the proposed endogenous regime switching micro-econometric multi-crop model with a panel dataset of French farmers. Our estimation and simulation results support our theoretical analysis, the effects of crop fixed costs and crop set choices on crop acreage choices in particular. More generally, these results suggest that the micro-econometric multicrop model presented in this article can significantly improves empirical analyses of crop supply based on farm data.

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“…Since panel data are generally characterized by high number of cross-sections (regions) and a limited number of years for which the data is available, they do not easily lend themselves to the application of standard econometric techniques (Autant-Bernard 2012; Platoni et al 2012). Econometric techniques adapted for panel data analysis are nowadays quite easily implemented in standard statistical packages.…”

Section: Objective and Scopementioning

confidence: 99%

Forecasting Transportation Infrastructure Impacts of Renewable Energy Industry Using Neural Networks

Gopalakrishnan

Gkritza

2014

Technological and Economic Development of Economy

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Iowa is a state rich in renewable energy resources, especially biomass. The successful development of renewable energy industry in Iowa is concomitant with increase in freight traffic and is likely to have significant impacts on transportation infrastructure condition and increased maintenance expenses for the state and local governments. The primary goal of this paper is to investigate the feasibility of employing the Neural Networks (NN) methodology to forecast the impacts of Iowa's biofuels and wind power industries on Iowa's secondary and local road condition and maintenance-related costs in a panel data framework. The data for this study were obtained from a number of sources and for a total of 24 counties in clusters in Northern, Western, and Southern Iowa over a period of ten years. Back-Propagation NN (BPNN) using a Quasi-Newton secondorder training algorithm was chosen for this study owing to its very fast convergence properties. Since the size of the training set is relatively small, ensembles of well-trained NNs were formed to achieve significant improvements in generalization performance. The developed NN forecasting models could identify the presence of biofuel plants and wind farms as well as large-truck traffic as the most sensitive inputs influencing pavement condition and granular and blading maintenance costs. Pavement deterioration resulting from traffic loads was found to be associated with the presence of both biofuel plants and wind farms. The developed NN forecasting models can be useful in identifying and properly evaluating future transportation infrastructure impacts resulting from the renewable energy industry development and thus help Iowa maintain its competitive edge in the rapidly developing bioeconomy. keywords: neural networks, renewable energy, biofuel plants, wind farms, forecasting, panel data, infrastructure impacts.

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Panel Data Estimation Techniques and Farm‐level Data Models

Cited by 14 publications

References 27 publications

Farm-household investment behaviour and the CAP decoupling: Methodological issues in assessing policy impacts

Farm-household investment behaviour and the CAP decoupling: Methodological issues in assessing policy impacts

Modeling Corners, Kinks, and Jumps in Crop Acreage Choices: Impacts of the EU Support to Protein Crops

Forecasting Transportation Infrastructure Impacts of Renewable Energy Industry Using Neural Networks

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