Stochastic difference equation?

In summary, the conversation is about a question regarding a specific step in solving a long equation with variables and constants. The solution involves extracting a variable and using a "stochastic difference equation" and a shift operator, L. The key also mentions the use of the differential operator D_x.
  • #1
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Homework Statement




This is a question about one single step of a solution of a long equation.

http://www.geocities.com/link_herooftime/math.jpg

where P, U and V are variables. a, b, c, d are constants and t is the time, which are measured in discrete periods.

The question is how to go from equation 1 to equation 3, and how the L appears.

Attempted solution


I have solved it until equation 2, and I see that the solution requires the extraction of P out of the bracket on the left hand side. Problem becomes how to do it, since the P minus one period doesn't equal P of current period. Nevertheless the key somehow does this. Does this require some other method than simple algebra?

The key mentions it is a "stochastic difference equation".
 
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  • #2
Just looking at it, L appears to be a shift operator, that is [itex]Lp_t=p_{t-1}[/itex]. Note that [itex]Lp_t[/itex] is not multiplication, but rather the L operator is applied to [itex]t_p[/itex]. Another example of this kind of notation involves the differential operator [itex]D_x[/itex], in particular [itex]D_xf(x)[/itex] is the derivative of f(x) w.r.t. x.
 

1. What is a stochastic difference equation?

A stochastic difference equation is a mathematical model that describes the evolution of a system over time, taking into account random or unpredictable factors. It is similar to a difference equation, but with the addition of a stochastic or probabilistic term.

2. What is the purpose of a stochastic difference equation?

The purpose of a stochastic difference equation is to model and analyze complex systems that involve random elements. It allows for the prediction of future behavior and the study of how different factors affect the system over time.

3. What are the key components of a stochastic difference equation?

The key components of a stochastic difference equation include an initial value, a deterministic term that represents the behavior of the system, and a stochastic term that accounts for random or unpredictable factors. It also includes a time variable and a lag operator to represent the difference between current and past values.

4. How is a stochastic difference equation solved?

There are various methods for solving a stochastic difference equation, including numerical methods and analytical methods. Numerical methods involve using computational tools to simulate the behavior of the system, while analytical methods involve finding an explicit solution to the equation.

5. What are some real-world applications of stochastic difference equations?

Stochastic difference equations have a wide range of applications in fields such as economics, finance, biology, and engineering. They can be used to model stock prices, population dynamics, chemical reactions, and more. They are particularly useful for studying complex systems that involve random or unpredictable elements.

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