Forecasting unemployment across countries: The ins and outs

Barnichon, Régis; Garda, Paula

doi:10.1016/j.euroecorev.2015.10.006

Cited by 21 publications

(11 citation statements)

References 19 publications

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“…For the UK, evidence of nonlinearities is found by Peel and Speight (2000), Milas and Rothman (2008) and Johnes (1999), and Gil-Alana ( 2001) finds evidence of long memory. Barnichon and Garda (2016) apply a flow approach to unemployment forecasting and find improvements, as does Smith (2011). Evidence of nonlinearity needs to be interpreted cautiously, because location shifts can generate apparent persistence which may be approximated by nonlinear and 'regime-switching' models, generating spurious nonlinearity due to unmodelled breaks.…”

Section: Forecasting Uk Unemployment Over the Covid-19 Pandemicmentioning

confidence: 99%

The Value of Robust Statistical Forecasts in the Covid-19 Pandemic

Castle

Doornik

Hendry

2021

NIER

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The Covid-19 pandemic has put forecasting under the spotlight, pitting epidemiological models against extrapolative time-series devices. We have been producing real-time short-term forecasts of confirmed cases and deaths using robust statistical models since 20 March 2020. The forecasts are adaptive to abrupt structural change, a major feature of the pandemic data due to data measurement errors, definitional and testing changes, policy interventions, technological advances and rapidly changing trends. The pandemic has also led to abrupt structural change in macroeconomic outcomes. Using the same methods, we forecast aggregate UK unemployment over the pandemic. The forecasts rapidly adapt to the employment policies implemented when the UK entered the first lockdown. The difference between our statistical and theory based forecasts provides a measure of the effect of furlough policies on stabilising unemployment, establishing useful scenarios had furlough policies not been implemented.

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Section: Forecasting Uk Unemployment Over the Covid-19 Pandemicmentioning

confidence: 99%

The Value of Robust Statistical Forecasts in the Covid-19 Pandemic

Castle

Doornik

Hendry

2021

NIER

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“…Our model can exploit the information available at different frequencies to produce reliable forecasts and to derive a narrative based on the key structural shocks to them. Furthermore, we include labour market flows in the empirical model, first to enrich the shock identification strategy, and second to check whether these variables play a role in the forecasting of euro area labour market variables, as shown by Barnichon and Nekarda (2012) and Barnichon and Garda (2016) for the US economy.…”

Section: A Model For the Euro Area Labour Marketmentioning

confidence: 99%

“…First, these variables are used to refine the shock identification and to enrich the set of shocks originating in the labour market. Second, we assess whether they play a role in the forecasting of labour market variables, as some papers in the literature show (see Barnichon and Nekarda (2012) and Barnichon and Garda (2016)). In contrast to the results in this literature, we find no evidence that the inclusion of job market flows in the model produces more accurate forecasts.…”

Section: Introductionmentioning

confidence: 99%

A Mixed Frequency BVAR for the Euro Area Labour Market

2021

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We introduce a Bayesian Mixed-Frequency VAR model for the aggregate euro area labour market that features a structural identification via sign restrictions. The purpose of this paper is twofold: we aim at (i) providing reliable and timely forecasts of key labour market variables and (ii) enhancing the economic interpretation of the main movements in the labour market. We find satisfactory results in terms of forecasting, especially when looking at quarterly variables, such as employment growth and the job finding rate. Furthermore, we look into the shocks that drove the labour market and macroeconomic dynamics from 2002 to early 2020, with a first insight also on the COVID-19 recession. While domestic and foreign demand shocks were the main drivers during the Global Financial Crisis, aggregate supply conditions and labour supply factors reflecting the degree of lockdown-related restrictions have been important drivers of key labour market variables during the pandemic.

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“…Finally, Frazis et al (2005) describe the procedure the BLS uses to rake the flows data. Taking a very different approach, Barnichon and Nekarda (2012) and Barnichon and Garda (2016) forecast inflows and outflows which can be used to predict net unemployment. Whereas these improvements help, in the end they are approximations and each yields different values.…”

Section: Introductionmentioning

confidence: 99%

Estimating labor force joiners and leavers using a heterogeneity augmented two-tier stochastic frontier

Das

Polachek

2017

Journal of Econometrics

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Estimating Labor Force Joiners and Leavers Using a Heterogeneity Augmented Two-Tier Stochastic Frontier jANuAry 2017Any opinions expressed in this paper are those of the author(s) and not those of IZA. Research published in this series may include views on policy, but IZA takes no institutional policy positions. The IZA research network is committed to the IZA Guiding Principles of Research Integrity. The IZA Institute of Labor Economics is an independent economic research institute that conducts research in labor economics and offers evidence-based policy advice on labor market issues. Supported by the Deutsche Post Foundation, IZA runs the world's largest network of economists, whose research aims to provide answers to the global labor market challenges of our time. Our key objective is to build bridges between academic research, policymakers and society. IZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author. This leads to a complex nonlinear likelihood function requiring identification through a two-step estimation procedure, which we estimate using Current Population Survey (CPS) data. By transforming the basic equation linking labor force participation to the working age population, this paper devises a new method which can be used to identify labor market joiners and leavers. The method's advantage is its parsimonious data requirements, especially alleviating the need for survey based longitudinal data. JEL Classification:C23, C51, J21

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Forecasting unemployment across countries: The ins and outs

Cited by 21 publications

References 19 publications

The Value of Robust Statistical Forecasts in the Covid-19 Pandemic

The Value of Robust Statistical Forecasts in the Covid-19 Pandemic

A Mixed Frequency BVAR for the Euro Area Labour Market

Estimating labor force joiners and leavers using a heterogeneity augmented two-tier stochastic frontier

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