Information-based estimators for the non-stationary transition probability matrix: an application to the Danish pork industry

Karantininis, Kostas

doi:10.1016/s0304-4076(01)00124-5

Cited by 31 publications

(35 citation statements)

References 21 publications

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“…As expected, the price of cow's milk has a positive impact on the number of farms in the medium and large size classes, especially in the 40–49 cows class. This result is consistent with those reported by Chavas and Magand (), Karantininis (), and Jongeneel et al ().…”

Section: Resultssupporting

confidence: 94%

“…The solution of the minimization problem, subject to the consistency constraint, the normalization and nonnegativity conditions of the probabilities, is obtained using the Lagrange function. The recovered

p_{ij}

are:

{\overset{p}{true}}_{italicij} = \frac{p_{ij}^{0} exp [\sum_{t} \sum_{n} x_{i t} z_{t n} {\overset{λ}{true}}_{n j}]}{\sum_{j} p_{italicij}^{0} prefixexp [\sum_{t} \sum_{n} x_{i t} z_{t n} {\overset{λ}{true}}_{n j}]}

where

{\tilde{p}}_{ij}

and

{\tilde{λ}}_{n j}

denote the estimated probabilities and the Lagrange multipliers, respectively (for derivations, see Golan and Vogel ; Karantininis ). Notice that there is a single TPM recovered with this method, not one matrix for each transition.…”

Section: Generalized Instrumental Variable Cross Entropy (Ivgce) For mentioning

confidence: 99%

“…wherep ij andλ nj denote the estimated probabilities and the Lagrange multipliers, respectively (for derivations, see Golan and Vogel 2000;Karantininis 2002). Notice that there is a single TPM recovered with this method, not one matrix for each transition.…”

Section: Generalized Instrumental Variable Cross Entropy (Ivgce)mentioning

confidence: 99%

“…The specific objectives of this paper are (1) to determine growth patterns and survival trends of French dairy farms between 1988 and 2006, specifically the size distribution of dairy farms; and (2) to identify the main factors of change, focusing in particular on the role of agricultural policies: namely decoupled payments, milk quotas, and YFSPs, in addition to milk yield and milk market price. We apply a nonstationary Markov model (Hallberg ; Zepeda , ), estimated with maximum entropy formalism (Karantininis ) to model the effects of factors influencing the numbers and the sizes of dairy farms. For this purpose, structural change is defined as the change of the number of farms in different size classes.…”

Section: Introductionmentioning

confidence: 99%

“…The specific objectives of this paper are (1) to determine growth patterns and survival trends of French dairy farms between 1988 and 2006, specifically the size distribution of dairy farms; and (2) to identify the main factors of change, focusing in particular on the role of agricultural policies: namely decoupled payments, milk quotas, and YFSPs, in addition to milk yield and milk market price. We apply a nonstationary Markov model (Hallberg 1969;Zepeda 1995aZepeda , 1995b, estimated with maximum entropy formalism (Karantininis 2002) to model the effects of factors influencing the numbers and the sizes 1 The "Département" is one of the large districts into which, France is divided for administrative purposes, and it correspond to the NUTS3 level of the European geographical classification. In France, transfers of dairy quota between "Départements" are not permitted, although in principle up to 20% of the national reserve allocations may be managed at national level (Alliance Environnement 2008).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Agricultural Policies and Structural Change in French Dairy Farms: A Nonstationary Markov Model

Arfa¹,

Daniel²,

Jacquet

et al. 2014

Canadian J Agri Economics

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The aim of this paper is to gain an understanding into how agricultural policies have affected structural change in the French dairy sector. A nonstationary Markov model is estimated using a Generalized Cross Entropy approach. Results show that while the price of cow's milk encourages farm growth, direct payments and quota restriction favor small farms. The price signal is a key factor of structural change. As a result, higher milk prices may accelerate the trend toward the further growth of dairy farms. Settlement policy and technical change seem not to slow this trend.

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Section: Resultssupporting

confidence: 94%

p_{ij}

are:

{\overset{p}{true}}_{italicij} = \frac{p_{ij}^{0} exp [\sum_{t} \sum_{n} x_{i t} z_{t n} {\overset{λ}{true}}_{n j}]}{\sum_{j} p_{italicij}^{0} prefixexp [\sum_{t} \sum_{n} x_{i t} z_{t n} {\overset{λ}{true}}_{n j}]}

where

{\tilde{p}}_{ij}

and

{\tilde{λ}}_{n j}

Section: Generalized Instrumental Variable Cross Entropy (Ivgce) For mentioning

confidence: 99%

Section: Generalized Instrumental Variable Cross Entropy (Ivgce)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The specific objectives of this paper are (1) to determine growth patterns and survival trends of French dairy farms between 1988 and 2006, specifically the size distribution of dairy farms; and (2) to identify the main factors of change, focusing in particular on the role of agricultural policies: namely decoupled payments, milk quotas, and YFSPs, in addition to milk yield and milk market price. We apply a nonstationary Markov model (Hallberg 1969;Zepeda 1995aZepeda , 1995b, estimated with maximum entropy formalism (Karantininis 2002) to model the effects of factors influencing the numbers and the sizes 1 The "Département" is one of the large districts into which, France is divided for administrative purposes, and it correspond to the NUTS3 level of the European geographical classification. In France, transfers of dairy quota between "Départements" are not permitted, although in principle up to 20% of the national reserve allocations may be managed at national level (Alliance Environnement 2008).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Agricultural Policies and Structural Change in French Dairy Farms: A Nonstationary Markov Model

Arfa¹,

Daniel²,

Jacquet

et al. 2014

Canadian J Agri Economics

Self Cite

View full text Add to dashboard Cite

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Scenario for structural development of livestock production in the Baltic littoral countries

Niskanen

Iho

Kalliovirta

2020

Agricultural Systems

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Forecasting the potential of Danish biogas production – Spatial representation of Markov chains

Bojesen

Skov-Petersen

Gylling

2015

Biomass and Bioenergy

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This paper forecasts the spatial distribution of Danish husbandry production from 2009 until 2025. The study builds on a time series data set (1999-2009) for the number of livestock units (including piglets, finishers, sows, dairy cattle and young stock) measured by 1km 2 grid cells for the whole of Denmark. A Markov Chain Model (MCM) was applied to estimate transition probabilities for the future livestock intensity composition, divided into state classes. Neighbouring effects between grid cells were not included. The modelled transition probabilities fit the data very well in all state classes, except for those farms which are in the largest state class. Regional differences in development trends were documented. The strategic objective of the model is to provide data for the spatial assessment of the potential of biogas production which can form the basis for a location analysis for future biogas plants.

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Information-based estimators for the non-stationary transition probability matrix: an application to the Danish pork industry

Cited by 31 publications

References 21 publications

Agricultural Policies and Structural Change in French Dairy Farms: A Nonstationary Markov Model

Agricultural Policies and Structural Change in French Dairy Farms: A Nonstationary Markov Model

Scenario for structural development of livestock production in the Baltic littoral countries

Forecasting the potential of Danish biogas production – Spatial representation of Markov chains

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