Thursday, June 20, 2024

Forecasting near-term developments within the labour market – Financial institution Underground


Harvey Daniell and Andre Moreira

The newest developments within the labour market are sometimes central to financial coverage selections. We define a framework for mapping labour market indicators to near-term employment and pay development, drawing on established insights from the ‘nowcasting’ literature. The important thing advantages of our method are: the flexibility to map a spread of ‘mushy’ and ‘arduous’ indicators of various frequencies to quarterly official information; the empirical willpower of how a lot weight to position on every indicator; and the flexibility to shift these weights flexibly as extra information turn out to be out there. This framework beats easy benchmark fashions in our labour market utility.

Understanding the newest developments within the labour market is usually key for financial coverage selections. 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 development.

Nonetheless, official information 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 method?

1: Laborious and mushy indicators, of various frequencies, may be mapped individually and on to the goal labour market variable.

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

These methods have the benefit of having the ability to deal with information of various frequency, avoiding the necessity to convert variables to the identical frequency, similar to decreasing weekly information to quarterly. This implies we keep away from any lack of data (or use of further assumptions) that reworking variables to the identical frequency entails.

An extra advantage of our explicit method 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 usually of curiosity to policymakers, because it permits us to debate options such because the dispersion throughout particular person indicators.

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

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

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

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

Totally different indicators usually level to completely different nowcasts for a similar variable, so it’s important to understand how a lot weight to position on every indicator. In our method, we weight the steer from every indicator in response to its relative forecast efficiency within the current previous – a standard method within the forecasting literature, which we discover additionally works properly 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 development. They present that ‘mushy’ 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 one of the best at predicting quarterly employment and pay development, 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 development, two-quarters forward (a)

Chart 3: Forecast efficiency of indicators of pay development, 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 development. ‘Lloyds’ is Lloyds Enterprise Barometer. ‘GDP’ is ONS three-month GDP development. ‘Payrolls’ are ONS/HMRC PAYE payrolls and median pay development. ‘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 development. ‘Job-to-job strikes’ are ONS quarterly flows information. ‘Cycle’ indicator is sum of CPI inflation and the (inverse) unemployment hole from Financial institution of England Financial Coverage Studies.

It’s fascinating to notice that, inside the vary of particular person nowcasts, mushy indicators are inclined to carry out significantly better than previous observations of the ‘arduous’ information – ie out there month-to-month observations of the goal ONS variable. Essentially, it’s because the arduous information 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 vital for the relative weights positioned on ‘arduous’ versus ‘mushy’ indicators of the labour market.

When new information turn out to be out there, the mapping between a given indicator and the goal variable may change, generally in fairly an vital method. So it’s essential to re-specify and re-estimate the underlying relationships every time the knowledge set adjustments. Acknowledging this, the mannequin can also be arrange in a method that permits it to optimally shift the burden placed on ‘arduous’ data (similar to ONS information) versus ‘mushy’ data (similar 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 data set is a crucial supply of decreasing forecast error, not least as a result of month-to-month observations of the arduous information, which the ONS additionally publishes, are a direct enter to the calculation of the quarterly outturn.

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

Below this method, the complete mannequin’s common forecast error progressively declines every time new information turn out to be out there however, unsurprisingly, the advance is especially hanging on the juncture the place the arduous indicators are available in to complement the steer from mushy indicators. Chart 4 illustrates this level for a nowcast of employment development.

Chart 4: Common forecast error for quarterly employment development declines as extra information turn out to be out there

Seen on this method, the framework can inform us each the marginal affect of latest information on a degree estimate of the nowcast, in addition to the extent to which the brand new information additionally scale back uncertainty round that nowcast.

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

The important thing insights outlined above are vital sources of decreasing forecast error. For instance, in a forecast for employment and pay development two quarters forward, the mixed forecast by which a collective steer is taken from a spread of indicators typically 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 development based mostly on lagged employment/pay development. That is typically 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 Studies. This displays the truth that the method outlined right here formalises most of the present heuristics that forecasters on the Financial institution make use of, similar to inserting extra weight on softer indicators early within the information cycle, and counting on a variety of information.

Curiously, efficiency on the one-year-ahead horizon of the mixed mannequin really barely outperforms successive Financial Coverage Studies. That is more likely to replicate two elements: data-driven approaches to forecasting could 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.

If you wish to get in contact, please electronic mail us at bankunderground@bankofengland.co.uk or depart a remark beneath.

Feedback will solely seem as soon as permitted by a moderator, and are solely printed the place a full title is equipped. Financial institution Underground is a weblog for Financial institution of England workers to share views that problem –or help – prevailing coverage orthodoxies

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