Zero-truncated Poisson distribution

In mathematics and in particular mathematical dynamics, discrete time and continuous time are two alternative frameworks within which to model variables that evolve over time.

Discrete time

Discrete time views values of variables as occurring at distinct, separate "points in time", or equivalently as being unchanged throughout each non-zero region of time ("time period"). Thus a variable jumps from one value to another as time moves from time period to the next. This view of time corresponds to a digital clock that gives a fixed reading of 10:37 for a while, and then jumps to a new fixed reading of 10:38, etc. In this framework, each variable of interest is measured once at each time period. The number of measurements between any two time periods is finite. Measurements are typically made at sequential integer values of the variable "time".

Continuous time

In contrast, continuous time views variables as having a particular value for potentially only an infinitesimally short amount of time. Between any two points in time there are an infinite number of other points in time. The variable "time" ranges over the entire real number line, or depending on the context, over some subset of it such as the non-negative reals.

Relevant contexts

Discrete time is often employed when empirical measurements are involved, because normally it is only possible to measure variables sequentially. For example, while economic activity actually occurs continuously, there being no moment when the economy is totally in a pause, it is nevertheless possible to measure economic activity only discretely. Hence published data on, for example, gross domestic product will show a sequence of quarterly values.

When one attempts to empirically explain such variables in terms of other variables and/or their own prior values, one uses time series or regression methods in which variables are indexed with a subscript indicating the time period in which the observation occurred. For example, y_t might refer to the value of income observed in unspecified time period t, y₃ to the value of income observed in the third time period, etc.

Moreover, when a researcher attempts to develop a theory to explain what is observed in discrete time, often the theory itself is expressed in discrete time in order to facilitate the development of a time series or regression model.

On the other hand, it is often more mathematically tractable to construct theoretical models in continuous time, and often in areas such as physics an exact description requires the use of continuous time. In a continuous-time context the value of a variable y at an unspecified point in time is denoted as y(t) or, when the meaning is clear, simply as y.

Types of equations

Discrete time

Discrete time makes use of difference equations, also known as recurrence relations. An example, known as the logistic map or logistic equation, is

$x_{t+1}=r{x}_t(1-x_t),$

in which r is a parameter in the range from 2 to 4 inclusive, and x is a variable in the range from 0 to 1 inclusive whose value in period t nonlinearly affects its value in the next period, t+1. For example, if $r=4$ and $x_1=1/3$, then for t=1 we have $x_2=4(1/3)(2/3)=8/9$, and for t=2 we have $x_3=4(8/9)(1/9)=32/81$.

Another example models the adjustment of a price P in response to non-zero excess demand for a product as

$P_{t+1}=P_t+\delta \cdot f(P_t,...)$

where $\delta$ is the positive speed-of-adjustment parameter which is less than or equal to 1, and where $f$ is the excess demand function.

Continuous time

Continuous time makes use of differential equations. For example, the adjustment of a price P in response to non-zero excess demand for a product can be modeled in continuous time as

$\frac{dP}{dt}=\lambda \cdot f(P,...)$

where the left side is the first derivative of the price with respect to time (that is, the rate of change of the price), $\lambda$ is the speed-of-adjustment parameter which can be any positive finite number, and $f$ is again the excess demand function.

Graphical depiction

The values of a variable measured in discrete time can be plotted as a step function, in which each time period is given a region on the horizontal axis of the same length as every other time period, and the measured variable is plotted as a height that stays constant throughout the region of the time period. In this graphical technique, the graph appears as a sequence of horizontal steps. Alternatively, each time period can be viewed as a detached point in time, usually at an integer value on the horizontal axis, and the measured variable is plotted as a height above that time-axis point. In this technique, the graph appears as a set of dots.

The values of a variable measured in continuous time are plotted as a continuous function, since the domain of time is considered to be the entire real axis or at least some connected portion of it.

See also

• Discrete calculus