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Potential Output: Concepts and Measurement - Department of Labour

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Darren Gibbs 83<br />

p<br />

⎛ Qt<br />

Assuming that labour productivity ⎜ ⎞<br />

p<br />

⎟<br />

⎝ Lt<br />

⎠ follows a simple time pattern,<br />

Christiano rearranges the above equation to express actual labour input as a<br />

function <strong>of</strong> actual output <strong>and</strong> that time pattern, <strong>and</strong> obtains estimates <strong>of</strong> the b s<br />

coefficients. Using these coefficients <strong>and</strong> an independent measure <strong>of</strong> L p t<br />

, 11 the<br />

dem<strong>and</strong> equation can be inverted to yield an estimate <strong>of</strong> the output gap as a<br />

function <strong>of</strong> lagged values <strong>of</strong> the input gap.<br />

A second approach which makes use <strong>of</strong> a capital (or investment) dem<strong>and</strong><br />

function was proposed by Hickman (1964). In this approach the dem<strong>and</strong> for<br />

capital, K * t<br />

, is expressed as:<br />

* * *<br />

t 0 1 t 2 t 3<br />

log K = α + α logQ + α log P + α t<br />

where Q t * <strong>and</strong> P t * represent permanent income <strong>and</strong> relative output prices at time<br />

t. (The model assumes annual data.)<br />

Assuming that the capital stock follows a partial adjustment process, <strong>and</strong> that<br />

both permanent income <strong>and</strong> relative output prices depend linearly on current<br />

<strong>and</strong> lagged values <strong>of</strong> actual output <strong>and</strong> actual prices respectively, it is possible<br />

to estimate Q t * as a function <strong>of</strong> capital <strong>and</strong> output price series.<br />

The relative advantage <strong>of</strong> the inverted dem<strong>and</strong> function approaches discussed<br />

above is that although, computationally, they are slightly more burdensome than<br />

the other approaches discussed so far, they do go some way towards addressing<br />

some <strong>of</strong> the weaknesses in the simpler approaches. They are also simpler to<br />

apply than the next technique we will discuss, which requires us to estimate the<br />

underlying production function. However, as is usually the case, the search for<br />

computational simplicity requires a trade-<strong>of</strong>f to be made with theoretical<br />

desirability. Strong assumptions need to be made about functional forms <strong>and</strong> the<br />

time behaviour <strong>of</strong> various variables.<br />

There are a number <strong>of</strong> difficulties with applying these approaches to New<br />

Zeal<strong>and</strong> data. In the case <strong>of</strong> the labour dem<strong>and</strong> approach, sufficiently disaggregated<br />

data over a long enough time period is either extremely unreliable or<br />

simply unavailable. Without this disaggregation, the approach differs little from<br />

that suggested by Okun; <strong>and</strong> the capital dem<strong>and</strong> approach has all the limitations<br />

that were discussed earlier in the context <strong>of</strong> the output/capital ratio approach.<br />

The production function approach<br />

The final approach that we shall discuss in this section is the production function<br />

approach which, at least on theoretical grounds, is by far the most desirable. The<br />

essence <strong>of</strong> the production function approach is an explicit modelling <strong>of</strong> output<br />

in terms <strong>of</strong> the underlying factor inputs, which involves the specification <strong>and</strong><br />

11<br />

Christiano derives a measure <strong>of</strong> potential hours dem<strong>and</strong> by applying cyclically adjusted<br />

participation <strong>and</strong> unemployment rates to eight age-gender groups in the population.<br />

Hours are converted to efficiency units by valuing them using relative wages. This is <strong>of</strong>ten<br />

known as ‘Perry weighting’, based on Perry (1971).

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