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Information cascades in the Brazilia...

Brewer, Brady E.

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Information cascades in the Brazilian farmland market.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Information cascades in the Brazilian farmland market./
作者:	Brewer, Brady E.
面頁冊數:	296 p.
附註:	Source: Dissertation Abstracts International, Volume: 76-11(E), Section: A.
Contained By:	Dissertation Abstracts International76-11A(E).
標題:	Agricultural economics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3708360
ISBN:	9781321835519

Information cascades in the Brazilian farmland market.
Brewer, Brady E.

Information cascades in the Brazilian farmland market. - 296 p.

Source: Dissertation Abstracts International, Volume: 76-11(E), Section: A.

Thesis (Ph.D.)--Kansas State University, 2015.

Farmland values have reached all-time highs and have significantly risen over the last few years. This has caused much debate about whether farmland prices are currently on a bubble and ready to burst, much like the earlier 1980s. Much research has been done on farmland values; however, work done outside of agricultural economics, looking at general asset values, can be incorporated into models of farmland value. Information cascades, or herding, are phenomenon where information in the market is sent between investors and this information is bid into the asset price, thus resulting in boom and bust periods. By using a Vector Autoregression (VAR) model, farmland price dynamics are modeled and analyzed for spatial dependencies from one region to the next. VAR allows for no a priori specification of network typology. This allows for the examination of the existence of information cascades and what form the network takes among spatially located farmland markets. This method is then compared to two other spatial estimation techniques. The first is a Spatial Autoregressive (SAR) model where network typology is imposed prior to estimation. The second is a VAR model where no network is modeled, and only the region's own asset prices can influence future periods. It is found that information cascades exist and network typology is somewhat random.

ISBN: 9781321835519Subjects--Topical Terms:

3172150
Agricultural economics.

Information cascades in the Brazilian farmland market.
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500 $a Source: Dissertation Abstracts International, Volume: 76-11(E), Section: A.
500 $a Advisers: Allen M. Featherstone; Christine Wilson.
502 $a Thesis (Ph.D.)--Kansas State University, 2015.
520 $a Farmland values have reached all-time highs and have significantly risen over the last few years. This has caused much debate about whether farmland prices are currently on a bubble and ready to burst, much like the earlier 1980s. Much research has been done on farmland values; however, work done outside of agricultural economics, looking at general asset values, can be incorporated into models of farmland value. Information cascades, or herding, are phenomenon where information in the market is sent between investors and this information is bid into the asset price, thus resulting in boom and bust periods. By using a Vector Autoregression (VAR) model, farmland price dynamics are modeled and analyzed for spatial dependencies from one region to the next. VAR allows for no a priori specification of network typology. This allows for the examination of the existence of information cascades and what form the network takes among spatially located farmland markets. This method is then compared to two other spatial estimation techniques. The first is a Spatial Autoregressive (SAR) model where network typology is imposed prior to estimation. The second is a VAR model where no network is modeled, and only the region's own asset prices can influence future periods. It is found that information cascades exist and network typology is somewhat random.
520 $a These results caution the current direction of the literature of imposing network or spatial structure. However, due to data requirements, SAR models are easier to estimate since they require less data and if network structure, which the SAR model inherently imposes by the weight matrix, could be determined by an autoregressive process instead of an adjacency rule it could prove to be the most accurate forecasting method.
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