Forecasting near-term trends in the labour market

Harvey Daniell and Andre Moreira

The most recent developments within the labour market are sometimes central to financial coverage choices. We define a framework for mapping labour market indicators to near-term employment and pay progress, drawing on established insights from the ‘nowcasting’ literature. The important thing advantages of our strategy are: the flexibility to map a spread of ‘gentle’ and ‘laborious’ indicators of various frequencies to quarterly official knowledge; the empirical willpower of how a lot weight to position on every indicator; and the flexibility to shift these weights flexibly as extra knowledge develop into out there. This framework beats easy benchmark fashions in our labour market utility.

Understanding the newest developments within the labour market is commonly key for financial coverage choices. In Might, for instance, the Financial Coverage Committee linked additional tightening in financial coverage to, amongst different issues, the tightness of labour market situations and the behaviour of wage progress.

Nonetheless, official knowledge on the labour market are printed with a lag. To enhance understanding of present situations when setting coverage, it’s essential to extract the sign from extra contemporaneous indicators to ‘nowcast’ – ie to foretell present and near-term developments in – the labour market.  

What are the important thing insights of our strategy?

1: Onerous and gentle indicators, of various frequencies, could be mapped individually and on to the goal labour market variable.

The expansion of high-frequency knowledge over the previous decade or so means now we have indicators of the labour market at quarterly, month-to-month, and even weekly frequency. These indicators could be mapped individually to a goal variable, akin to quarterly employment progress, utilizing ‘mixed-data sampling’ (or ‘MIDAS’) strategies. MIDAS strategies are ‘basically tightly parameterised, lowered type regressions that contain processes sampled at completely different frequencies‘.

These strategies have the benefit of having the ability to deal with knowledge of various frequency, avoiding the necessity to convert variables to the identical frequency, akin to lowering weekly knowledge to quarterly. This implies we keep away from any lack of info (or use of extra assumptions) that reworking variables to the identical frequency entails.

An additional good thing about our explicit strategy is the flexibility to map every indicator individually to the variable of curiosity, earlier than combining these into an general mannequin nowcast. In different phrases, we begin by acquiring a special nowcast from every indicator, which is commonly of curiosity to policymakers, because it permits us to debate options such because the dispersion throughout particular person indicators.

For example, Chart 1 illustrates the nowcasts for quarterly employment progress from MIDAS regressions utilizing a spread of labour market indicators. The gray strains illustrate the person nowcasts, which monitor the goal variable, within the blue line, pretty nicely, regardless of the volatility within the latter.

Chart 1: Indicator-based nowcasts for quarter on quarter employment progress (per cent) (a)

(a) Indicator-based nowcasts are primarily based on Financial institution of England Brokers’ scores, the Lloyds Enterprise Barometer, ONS/HMRC PAYE payrolls progress, S&P World/CIPS Buying Managers Index and KPMG/REC Report on Jobs.

2: The steer from completely different indicators could be mixed into one general view, the place the weights are knowledgeable by the data content material of the related indicator.

Completely different indicators typically level to completely different nowcasts for a similar variable, so it’s important to know the way a lot weight to position on every indicator. In our strategy, we weight the steer from every indicator based on its relative forecast efficiency within the current previous – a typical strategy within the forecasting literature, which we discover additionally works nicely on this utility to the UK labour market. In different phrases, extra correct indicators obtain a better weight.

Chart 2 and Chart 3 plot a measure of forecast efficiency for a spread of indicators of quarter-ahead employment and pay progress. They present that ‘gentle’ indicators just like the month-to-month employment stability of the S&P World/CIPS Buying Managers Index and the month-to-month KPMG/REC Report on Jobs carry out the most effective at predicting quarterly employment and pay progress, two quarters forward. Crucially, although, the mixed steer from all indicators outperforms counting on any particular person indicator alone.

Chart 2: Forecast efficiency of indicators of employment progress, two-quarters forward (a)

Chart 3: Forecast efficiency of indicators of pay progress, two-quarters forward (a)

(a) ‘RMSE’ is root imply squared error, a regular measure of historic forecast efficiency. ‘BCC’ is British Chambers of Commerce quarterly financial survey. ‘Vacancies’ are ONS three-month vacancies progress. ‘Lloyds’ is Lloyds Enterprise Barometer. ‘GDP’ is ONS three-month GDP progress. ‘Payrolls’ are ONS/HMRC PAYE payrolls and median pay progress. ‘REC’ is KPMG/REC Report on Jobs. ‘PMI’ is S&P World/CIPS Buying Managers Index. ‘Brokers’ are Financial institution of England Brokers’ scores for employment and pay progress. ‘Job-to-job strikes’ are ONS quarterly flows knowledge. ‘Cycle’ indicator is sum of CPI inflation and the (inverse) unemployment hole from Financial institution of England Financial Coverage Reviews.

It’s attention-grabbing to notice that, throughout the vary of particular person nowcasts, gentle indicators are inclined to carry out significantly better than previous observations of the ‘laborious’ knowledge – ie out there month-to-month observations of the goal ONS variable. Basically, it’s because the laborious knowledge are risky, and are subsequently not essentially very informative even about their very own trajectory. 

3: The weights positioned on every indicator change over the info cycle, which is especially necessary for the relative weights positioned on ‘laborious’ versus ‘gentle’ indicators of the labour market.

When new knowledge develop into out there, the mapping between a given indicator and the goal variable may change, typically in fairly an necessary approach. So it’s essential to re-specify and re-estimate the underlying relationships every time the data set adjustments. Acknowledging this, the mannequin can also be arrange in a approach that enables it to optimally shift the load placed on ‘laborious’ info (akin to ONS knowledge) versus ‘gentle’ info (akin to enterprise surveys) over the info cycle. This optimum weighing takes place in a easy second-stage OLS regression.

Taking a step again, updating the near-term outlook for an increasing info set is a crucial supply of lowering forecast error, not least as a result of month-to-month observations of the laborious knowledge, which the ONS additionally publishes, are a direct enter to the calculation of the quarterly outturn.

Early within the knowledge cycle, when no intra-quarter official knowledge can be found, the mannequin tells us to position extra weight on the timelier gentle indicators such because the month-to-month enterprise surveys. As soon as intra-quarter month-to-month laborious knowledge develop into out there, nonetheless, these start to obtain a a lot larger weight.

Below this strategy, the complete mannequin’s common forecast error progressively declines every time new knowledge develop into out there however, unsurprisingly, the advance is especially hanging on the juncture the place the laborious indicators are available in to complement the steer from gentle indicators. Chart 4 illustrates this level for a nowcast of employment progress.

Chart 4: Common forecast error for quarterly employment progress declines as extra knowledge develop into out there

Seen on this approach, the framework can inform us each the marginal impression of latest knowledge on a degree estimate of the nowcast, in addition to the extent to which the brand new knowledge additionally cut back uncertainty round that nowcast.

4: The strategy considerably outperforms easy benchmark fashions for forecasting the labour market.

The important thing insights outlined above are necessary sources of lowering forecast error. For instance, in a forecast for employment and pay progress two quarters forward, the mixed forecast during which a collective steer is taken from a spread of indicators usually outperforms counting on any single indicator alone.

Furthermore, the mixed nowcast from the complete mannequin outperforms a easy autoregressive benchmark mannequin – ie a easy mannequin of quarterly employment/pay progress primarily based on lagged employment/pay progress. That is usually true in any respect states of the info cycle, with the discount in root-mean-squared errors growing to round 65% previous to the discharge of the outturn.

Certainly, the mannequin’s forecast efficiency on the quarter-ahead horizon is much like the one-quarter forward forecasts from successive Financial Coverage Reviews. This displays the truth that the strategy outlined right here formalises lots of the current heuristics that forecasters on the Financial institution make use of, akin to putting extra weight on softer indicators early within the knowledge cycle, and counting on a variety of knowledge.

Curiously, efficiency on the one-year-ahead horizon of the mixed mannequin really barely outperforms successive Financial Coverage Reviews. That is more likely to replicate two elements: data-driven approaches to forecasting might outperform extra structural fashions like these supporting the Financial Coverage Report on the one-year horizon; and Financial Coverage Report forecasts are conditional forecasts, whose forecast accuracy is just one requirement of the mannequin amongst many different necessities.

Harvey Daniell and Andre Moreira work within the Financial institution’s Present Financial Circumstances Division.

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