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 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 variety of ‘smooth’ and ‘onerous’ indicators of various frequencies to quarterly official information; the empirical willpower of how a lot weight to put on every indicator; and the flexibility to shift these weights flexibly as extra information turn into accessible. This framework beats easy benchmark fashions in our labour market software.

Understanding the newest developments within the labour market is usually key for financial coverage selections. In Could, 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.

Nevertheless, official information on the labour market are revealed 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: Arduous and smooth indicators, of various frequencies, could 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’ve got indicators of the labour market at quarterly, month-to-month, and even weekly frequency. These indicators could be mapped individually to a goal variable, comparable to quarterly employment development, utilizing ‘mixed-data sampling’ (or ‘MIDAS’) methods. MIDAS methods are ‘primarily tightly parameterised, lowered type regressions that contain processes sampled at totally 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, comparable to lowering weekly information to quarterly. This implies we keep away from any lack of info (or use of extra assumptions) that remodeling variables to the identical frequency entails.

An extra good thing about our explicit method is the flexibility to map every indicator individually to the variable of curiosity, earlier than combining these into an total mannequin nowcast. In different phrases, we begin by acquiring a unique 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 variety of labour market indicators. The gray traces illustrate the person nowcasts, which monitor the goal variable, within the blue line, pretty effectively, 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 primarily based on Financial institution of England Brokers’ scores, the Lloyds Enterprise Barometer, ONS/HMRC PAYE payrolls development, S&P International/CIPS Buying Managers Index and KPMG/REC Report on Jobs.

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

Totally different indicators typically level to totally different nowcasts for a similar variable, so it’s important to understand how a lot weight to put on every indicator. In our method, we weight the steer from every indicator in response to its relative forecast efficiency within the latest previous – a typical method within the forecasting literature, which we discover additionally works effectively on this software to the UK labour market. In different phrases, extra correct indicators obtain the next weight.

Chart 2 and Chart 3 plot a measure of forecast efficiency for a variety of indicators of quarter-ahead employment and pay development. They present that ‘smooth’ indicators just like the month-to-month employment stability of the S&P International/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 normal 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 International/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 Stories.

It’s attention-grabbing to notice that, throughout the vary of particular person nowcasts, smooth indicators are likely to carry out significantly better than previous observations of the ‘onerous’ information – ie accessible month-to-month observations of the goal ONS variable. Essentially, it’s because the onerous 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 information cycle, which is especially essential for the relative weights positioned on ‘onerous’ versus ‘smooth’ indicators of the labour market.

When new information turn into accessible, the mapping between a given indicator and the goal variable may also change, generally in fairly an essential manner. 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 manner that permits it to optimally shift the burden placed on ‘onerous’ info (comparable to ONS information) versus ‘smooth’ info (comparable to enterprise surveys) over the information 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 onerous 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 put extra weight on the timelier smooth indicators such because the month-to-month enterprise surveys. As soon as intra-quarter month-to-month onerous information turn into accessible, nevertheless, these start to obtain a a lot increased weight.

Underneath this method, the complete mannequin’s common forecast error progressively declines every time new information turn into accessible however, unsurprisingly, the advance is especially placing on the juncture the place the onerous indicators are available in to complement the steer from smooth 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 into accessible

Seen on this manner, the framework can inform us each the marginal influence of recent 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 essential sources of lowering forecast error. For instance, in a forecast for employment and pay development two quarters forward, the mixed forecast through which a collective steer is taken from a variety 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 development primarily based on lagged employment/pay development. That is usually true in any respect states of the information cycle, with the discount in root-mean-squared errors rising to round 65% previous to the discharge of the outturn.

Certainly, the mannequin’s forecast efficiency on the quarter-ahead horizon is just like the one-quarter forward forecasts from successive Financial Coverage Stories. This displays the truth that the method outlined right here formalises most of the current heuristics that forecasters on the Financial institution make use of, comparable to putting 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 Stories. 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.

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