Modeling of timber harvesting options using timber prices as a mean reverting process with stochastic trend

2012 ◽  
Vol 42 (1) ◽  
pp. 179-189 ◽  
Author(s):  
Rajendra Prasad Khajuria ◽  
Shashi Kant ◽  
Susanna Laaksonen-Craig
Keyword(s):  
State Space Model ◽  
Optimal Investment ◽  
Timber Harvesting ◽  
Vital Role ◽  
Stochastic Trend ◽  
Space Model ◽  
Price Process ◽  
Long Run ◽  
Mean Reverting Process

Proper characterization of the timber price process plays a vital role in forest management decisions. The process of long-run timber prices and its implications for harvesting decisions are analyzed for a forest in Ontario, Canada. Timber prices are modeled as a mean reverting process with stochastic trend. The Kalman filter is used to estimate the state–space model. The forecasted prices from the model are used in real options analysis to determine the optimal investment time and optimal investment rule. The results provide insight different from that of other specifications used in earlier literature.

Two competing queues with linear costs: the μc-rule is often optimal

1984 ◽  
Vol 16 (1) ◽  
pp. 8-8
Author(s):  
J. S. Baras ◽  
A. J. Dorsey ◽  
A. M. Makowski
Keyword(s):  
State Space ◽  
Random Sequence ◽  
State Space Model ◽  
Sample Path ◽  
Single Server ◽  
Space Model ◽  
Long Run ◽  
Markov Decision ◽  
Cost Parameters ◽  
Discounted Criterion

A state-space model is presented for a queueing system where two classes of customer compete in discrete-time for the service attention of a single server with infinite buffer capacity. The arrivals are modelled by an independent identically distributed random sequence of a general type while the service completions are generated by independent Bernoulli streams; the allocation of service attention is governed by feedback policies which are based on past decisions and buffer content histories. The cost of operation per unit time is a linear function of the queue sizes. Under the model assumptions, a fixed prioritization scheme, known as the μc -rule, is shown to be optimal when the expected long-run average criterion and the expected discounted criterion, over both finite and infinite horizons, are used. This static prioritization of the two classes of customers is done solely on the basis of service and cost parameters. The analysis is based on the dynamic programming methodology for Markov decision processes and takes advantage of the sample-path properties of the adopted state-space model.

scholarly journals Evidências de bolhas especulativas na BOVESPA: uma aplicação do filtro de Kalman

2011 ◽  
Vol 9 (2) ◽  
pp. 257
Author(s):  
Thiago Bergmann De Queiroz ◽  
Otávio Ribeiro De Medeiros ◽  
José Carneiro da Cunha Oliveira Neto
Keyword(s):  
Kalman Filter ◽  
Price Index ◽  
State Space Model ◽  
Present Value ◽  
Present Value Model ◽  
Space Model ◽  
Price Bubbles ◽  
Price Bubble ◽  
Long Run ◽  
Value Model

The existence of bubbles in asset prices is a matter of great importance to governments and investors due to possible serious effects they may have on economies. In the case of shares, the presence of a price bubble can be seen by comparing prices and dividends in the long run. This study aimed to assess the occurrence of price bubbles in the Brazilian stock market, by comparing the IBOVESPA as price index and an index of dividends, built based on the methodology of IBOVESPA. The bubble was considered a unobserved state vector in a state-space model and was estimated using the Kalman filter. The results were compared with the standard present value model and intrinsic bubbles model (Froot e Obstfeld, 1991). Although the model establishes the presence of bubbles, the intrinsic bubbles model (Froot e Obstfeld, 1991) showed similar results with greater accuracy.

Multivariable modeling and control of an activated sludge process

1998 ◽  
Vol 37 (12) ◽  
pp. 149-156 ◽  
Author(s):  
Carl-Fredrik Lindberg
Keyword(s):  
Activated Sludge ◽  
Nitrate Concentration ◽  
State Space Model ◽  
Activated Sludge Process ◽  
Invariant State ◽  
Linear Quadratic ◽  
Modeling And Control ◽  
Space Model ◽  
And Control ◽  
Time Invariant

This paper contains two contributions. First it is shown, in a simulation study using the IAWQ model, that a linear multivariable time-invariant state-space model can be used to predict the ammonium and nitrate concentration in the last aerated zone in a pre-denitrifying activated sludge process. Secondly, using the estimated linear model, a multivariable linear quadratic (LQ) controller is designed and used to control the ammonium and nitrate concentration.

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