Forecasting near-term trends in the labour market

Harvey Daniell and Andre Moreira

The latest developments in the labour market are often inside to monetary policy decisions. We outline a framework for mapping labour market indicators to near-term employment and pay growth, drawing on established insights from the ‘nowcasting’ literature. The key benefits of our tideway are: the worthiness to map a range of ‘soft’ and ‘hard’ indicators of variegated frequencies to quarterly official data; the empirical determination of how much weight to place on each indicator; and the worthiness to shift those weights flexibly as increasingly data wilt available. This framework beats simple benchmark models in our labour market application.

Understanding the latest developments in the labour market is often key for monetary policy decisions. In May, for example, the Monetary Policy Committee linked remoter tightening in monetary policy to, among other things, the tightness of labour market conditions and the behaviour of wage growth.

However, official data on the labour market are published with a lag. To modernize understanding of current conditions when setting policy, it’s necessary to pericope the signal from increasingly contemporaneous indicators to ‘nowcast’ – ie to predict current and near-term developments in – the labour market.  

What are the key insights of our approach?

1: Nonflexible and soft indicators, of variegated frequencies, can be mapped individually and directly to the target labour market variable.

The growth of high-frequency data over the past decade or so ways we have indicators of the labour market at quarterly, monthly, and plane weekly frequency. These indicators can be mapped individually to a target variable, such as quarterly employment growth, using ‘mixed-data sampling’ (or ‘MIDAS’) techniques. MIDAS techniques are ‘essentially tightly parameterised, reduced form regressions that involve processes sampled at variegated frequencies‘.

These techniques have the wholesomeness of stuff worldly-wise to handle data of variegated frequency, lamister the need to convert variables to the same frequency, such as reducing weekly data to quarterly. This ways we stave any loss of information (or use of spare assumptions) that transforming variables to the same frequency entails.

A remoter goody of our particular tideway is the worthiness to map each indicator individually to the variable of interest, surpassing combining those into an overall model nowcast. In other words, we start by obtaining a variegated nowcast from each indicator, which is often of interest to policymakers, as it allows us to discuss features such as the dispersion wideness individual indicators.

As an example, Chart 1 illustrates the nowcasts for quarterly employment growth from MIDAS regressions using a range of labour market indicators. The grey lines illustrate the individual nowcasts, which track the target variable, in the undecorous line, fairly well, despite the volatility in the latter.

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

(a) Indicator-based nowcasts are based on Bank of England Agents’ scores, the Lloyds Merchantry Barometer, ONS/HMRC PAYE payrolls growth, S&P Global/CIPS Purchasing Managers Index and KPMG/REC Report on Jobs.

2: The steer from variegated indicators can be combined into one overall view, where the weights are informed by the information content of the relevant indicator.

Different indicators often point to variegated nowcasts for the same variable, so it’s vital to know how much weight to place on each indicator. In our approach, we weight the steer from each indicator equal to its relative forecast performance in the recent past – a worldwide tideway in the forecasting literature, which we find moreover works well in this using to the UK labour market. In other words, increasingly well-judged indicators receive a higher weight.

Chart 2 and Chart 3 plot a measure of forecast performance for a range of indicators of quarter-ahead employment and pay growth. They show that ‘soft’ indicators like the monthly employment wastefulness of the S&P Global/CIPS Purchasing Managers Index and the monthly KPMG/REC Report on Jobs perform the weightier at predicting quarterly employment and pay growth, two quarters ahead. Crucially, though, the combined steer from all indicators outperforms relying on any individual indicator alone.

Chart 2: Forecast performance of indicators of employment growth, two-quarters ahead ^(a)

Chart 3: Forecast performance of indicators of pay growth, two-quarters ahead ^(a)

(a) ‘RMSE’ is root midpoint squared error, a standard measure of historical forecast performance. ‘BCC’ is British Chambers of Commerce quarterly economic survey. ‘Vacancies’ are ONS three-month vacancies growth. ‘Lloyds’ is Lloyds Merchantry Barometer. ‘GDP’ is ONS three-month GDP growth. ‘Payrolls’ are ONS/HMRC PAYE payrolls and median pay growth. ‘REC’ is KPMG/REC Report on Jobs. ‘PMI’ is S&P Global/CIPS Purchasing Managers Index. ‘Agents’ are Bank of England Agents’ scores for employment and pay growth. ‘Job-to-job moves’ are ONS quarterly flows data. ‘Cycle’ indicator is sum of CPI inflation and the (inverse) unemployment gap from Bank of England Monetary Policy Reports.

It’s interesting to note that, within the range of individual nowcasts, soft indicators tend to perform much largest than past observations of the ‘hard’ data – ie misogynist monthly observations of the target ONS variable. Fundamentally, this is considering the nonflexible data are volatile, and are therefore not necessarily very informative plane well-nigh their own trajectory. 

3: The weights placed on each indicator transpiration over the data cycle, which is particularly important for the relative weights placed on ‘hard’ versus ‘soft’ indicators of the labour market.

When new data wilt available, the mapping between a given indicator and the target variable can moreover change, sometimes in quite an important way. So it’s crucial to re-specify and re-estimate the underlying relationships each time the information set changes. Acknowledging this, the model is moreover set up in a way that allows it to optimally shift the weight put on ‘hard’ information (such as ONS data) versus ‘soft’ information (such as merchantry surveys) over the data cycle. This optimal weighing takes place in a simple second-stage OLS regression.

Taking a step back, updating the near-term outlook for an expanding information set is an important source of reducing forecast error, not least considering monthly observations of the nonflexible data, which the ONS moreover publishes, are a uncontrived input to the numbering of the quarterly outturn.

Early in the data cycle, when no intra-quarter official data are available, the model tells us to place increasingly weight on the timelier soft indicators such as the monthly merchantry surveys. Once intra-quarter monthly nonflexible data wilt available, however, these uncork to receive a much higher weight.

Under this approach, the full model’s stereotype forecast error progressively declines each time new data wilt misogynist but, unsurprisingly, the resurgence is particularly striking at the juncture where the nonflexible indicators come in to supplement the steer from soft indicators. Chart 4 illustrates this point for a nowcast of employment growth.

Chart 4: Stereotype forecast error for quarterly employment growth declines as increasingly data wilt available

Viewed in this way, the framework can tell us both the marginal impact of new data on a point estimate of the nowcast, as well as the extent to which the new data moreover reduce uncertainty virtually that nowcast.

4: The tideway significantly outperforms simple benchmark models for forecasting the labour market.

The key insights outlined whilom are important sources of reducing forecast error. For example, in a forecast for employment and pay growth two quarters ahead, the combined forecast in which a joint steer is taken from a range of indicators often outperforms relying on any single indicator alone.

Moreover, the combined nowcast from the full model outperforms a simple autoregressive benchmark model – ie a simple model of quarterly employment/pay growth based on lagged employment/pay growth. This is often true at all states of the data cycle, with the reduction in root-mean-squared errors increasing to virtually 65% prior to the release of the outturn.

Indeed, the model’s forecast performance at the quarter-ahead horizon is similar to the one-quarter superiority forecasts from successive Monetary Policy Reports. This reflects the fact that the tideway outlined here formalises many of the existing heuristics that forecasters at the Bank employ, such as placing increasingly weight on softer indicators early in the data cycle, and relying on a wide range of data.

Interestingly, performance at the one-year-ahead horizon of the combined model unquestionably slightly outperforms successive Monetary Policy Reports. This is likely to reflect two factors: data-driven approaches to forecasting may outperform increasingly structural models like those supporting the Monetary Policy Report at the one-year horizon; and Monetary Policy Report forecasts are provisionary forecasts, whose forecast verism is only one requirement of the model among many other requirements.

Harvey Daniell and Andre Moreira work in the Bank’s Current Economic Conditions Division.

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