diff --git a/source/rst/scalar_dynam.rst b/source/rst/scalar_dynam.rst index bd38ada6..833035d7 100644 --- a/source/rst/scalar_dynam.rst +++ b/source/rst/scalar_dynam.rst @@ -15,10 +15,10 @@ Overview In this lecture we give a quick introduction to discrete time dynamics in one dimension. -This means that the state of the system is described by a single variable. +In one-dimensional models, the state of the system is described by a single variable. -Although most interesting dynamic models need two or more state variables, the -one dimensional setting is a good place to learn the foundations and build +Although most interesting dynamic models have two or more state variables, the +one-dimensional setting is a good place to learn the foundations of dynamics and build intuition. Let's start with some standard imports: @@ -54,12 +54,10 @@ In the definition, * time homogeneity means that :math:`g` is the same at each time :math:`t` -* first order means dependence on only one lag (:math:`x_{t+1} = g(x_t, - x_{t-1})$` is a second order difference equation and so on). +* first order means dependence on only one lag (i.e., earlier states such as :math:`x_{t-1}` do not enter into :eq:`sdsod`). -If :math:`x_0 \in S` is given, then :eq:`sdsod` recursively defines a sequence -given by +If :math:`x_0 \in S` is given, then :eq:`sdsod` recursively defines the sequence .. math:: :label: sdstraj @@ -71,14 +69,13 @@ given by This sequence is called the **trajectory** of :math:`x_0` under :math:`g`. -If we set :math:`g^n := n` compositions of :math:`g` with itself, then we can -write the trajectory more simply as :math:`x_t = g^t(x_0)` for :math:`t \geq 0`. +If we define :math:`g^n` to be :math:`n` compositions of :math:`g` with itself, then we can write the trajectory more simply as :math:`x_t = g^t(x_0)` for :math:`t \geq 0`. Example: A Linear Model ----------------------- -One easy to work with example is the **linear difference equation** +One simple example is the **linear difference equation** .. math:: x_{t+1} = a x_t + b, \qquad S = \mathbb R