UK regional nowcasting using a mixed frequency vector autoregressive model with entropic tilting

Output growth data for the UK regions are available at only annual frequency and are released with significant delay. Regional policy makers would benefit from more frequent and timely data. We develop a stacked, mixed frequency vector auto‐regression to provide, each quarter, nowcasts of annual output growth for the UK regions. The information that we use to update our regional nowcasts includes output growth data for the UK as a whole, as these aggregate data are released in a more timely and frequent (quarterly) fashion than the regional disaggregates which they comprise. We show how entropic tilting methods can be adapted to exploit the restriction that UK output growth is a weighted average of regional growth. In our realtime nowcasting application we find that the stacked mixed frequency vector‐autoregressive model, with entropic tilting, provides an effective means of nowcasting the regional disaggregates exploiting known information on the aggregate

The Appropriateness Of Parental Involvement In The Job Search Process

This paper explores millennial job seekers and their parental involvement in the job search process. Preliminary work on a scale to measure the “appropriateness” of certain job search behaviors is reported. Ten parental job search behaviors are identified.  The appropriateness constructs of “mentoring” and “meddling” are developed and empirically tested. Results indicate that both meddling and mentoring are valid and initially useful constructs in examining the suitability of parental involvement in the job search process. The possible impact of parental involvement in the job search process is then discussed along with possible managerial responses

Nowcasting using mixed frequency methods : an application to the Scottish economy

The delays in the release of key economic variables mean that policymakers do not know their current values. Quickly produced, high frequency, indicators are essential in understanding economic performance in a timely fashion. Thus, there is a need for nowcasts, which are estimates of the current values of such variables (e.g. GDP, employment, etc.). This paper nowcasts economic growth in Scotland. Nowcasting the Scottish economy is complicated because the government statistical agency treats Scotland as a region within the UK. This raises issues of data timeliness and availability. For instance, key nowcast predictors such as industrial production are unavailable at the sub-national level. Accordingly, we use data on some non-traditional variables and investigate whether UK aggregates, and indicators for neighbouring regions of the UK, can help nowcast Scottish GDP growth. Similar considerations hold for other regions in other countries. Thus, we show that these models and methods can be successfully adapted for use in a regional setting, and so produce timely macroeconomic indicators for other regional economies

Reconciled estimates and nowcasts of regional output in the UK

There is renewed interest in levelling up the regions of the UK. The combination of social and political discontent, and the sluggishness of key UK macroeconomic indicators like productivity growth, has led to increased interest in understanding the regional economies of the UK. In turn, this has led to more investment in economic statistics. Specifically, the Office for National Statistics (ONS) recently started to produce quarterly regional GDP data for the nine English regions and Wales that date back to 2012Q1. This complements existing real GVA data for the regions available from the ONS on an annual basis back to 1998; with the devolved administrations of Scotland and Northern Ireland producing their own quarterly output measures. In this paper we reconcile these two data sources along with UK quarterly output data that date back to 1970. This enables us to produce both more timely real terms estimates of quarterly economic growth in the regions of the UK and a new reconciled historical time-series of quarterly regional real output data from 1970. We explore a number of features of interest of these new data. This includes producing a new quarterly regional productivity series and commenting on the evolution of regional productivity growth in the UK

Turnover in radio sales staffs: a census of Kansas commercial radio stations

Call number: LD2668 .T4 1986 M44Master of ArtsJournalism and Mass Communication

Production of pectic enzymes by Verticillium albo-atrum

The main objectives of this study were to determine the effect of certain environmental conditions on the quantity of poly-galacturonase (PG) produced by V. albo-atrum, and to partially purify and characterize the PGs produced by this fungus in culture and in infected tomato plants. V. albo-atrum produced twenty times as much PG when sodium polypectate was the carbon source as when grown on pectin. Optimum PG production occurred 6-8 days after inoculation of the medium. Less pectinmethyesterase (PME) was produced when V. albo-atrum was grown on polypectate as the carbon source than when grown on pectin. PME production was observed early in the growth of the fungus. No PME activity could be detected from culture filtrates after eight days when either pectin or sodium polypectate was the carbon source. Three elution schedules were used to purify PGs from a DEAE-cellulose column. Step-wise elution produced residual peaks of PG activity, and gradient elution gave poor separation of the PGs. Flushing the DEAE-cellulose column with distilled water and NaC1 gave the best separation of the two endo-PGs. PME was associated with all fractions containing PG activity. Assays of PG activity in fractions separated by column chromatography suggested that V. albo-atrum produced at least two endo-PGs in culture. One endo-PG preferentially hydrolyzed sodium polypectate to large fragments; however, the fragments were eventually hydrolyzed to monogalacturonic acid. The second endo-PG preferentially hydrolyzed small fragments from the substrate. Fractions containing the endo-PG that preferentially hydrolyzed the substrate to large fragments gave ratios of viscosity reduction to reducing group liberation at least five times as high as those obtained for fractions containing the endo-PG that preferentially hydrolyzed the substrate to small fragments. Culture filtrates from cultures incubated at room temperature but not on a shaker produced less PG than cultures grown on a shaker at room temperature. When culture filtrates of the still cultures were eluted from the DEAE-cellulose column, fractions from the first PG peak eluted from the column hydrolyzed trigalacturonic acid slowly to monogalacturonic acid. Cultures grown at 30°C produced only trace amounts of PG activity. Growth of the fungus at 30°C was similar to cultures grown at lower temperatures. However, the cultures grown at lower temperatures produced relatively high PG titers. The crude enzyme preparation of V. albo-atrum from infected tomato plants yielded pectic fragments as well as monogalacturonic acid. This indicated the presence of an endo-PG that hydrolyzed small fragments from the substrate. Column chromatography of this enzyme on DEAE-cellulose using a step-wise elution yielded a PG fraction that preferentially hydrolyzed the substrate into small fragments similar to fractions obtained by a step-wise elution of culture filtrates. Evidence was obtained that V. albo-atrum produced trace amounts of pectintranseliminase in culture and in infected plants. Additional studies will be required to understand the significance of this enzyme in the Verticillium wilt syndrome

UK regional nowcasting using a mixed frequency vector autoregressive model

Data on Gross Value Added (GVA) are currently only available at the annual frequency for the UK regions and are released with significant delay. Regional policymakers would benefit from more frequent and timely data. The goal of this paper is to provide these. We use a mixed frequency Vector Autoregression (VAR) to provide, each quarter, nowcasts (i.e. forecasts of current GVA which is as yet unknown due to release delays) of annual GVA growth for the UK regions. The information we use to update our regional nowcasts comes from GVA growth for the UK as a whole as this is released in a more timely and frequent (quarterly) fashion. To improve our nowcasts we use entropic tilting methods to exploit the restriction that UK GVA growth is a weighted average of GVA growth for the UK regions. In this paper, we develop the econometric methodology and test it in the context of a real time nowcasting exercise

Nowcasting 'True' Monthly US GDP During the Pandemic

Expenditure side and income side GDP are both measured at the quarterly frequency in the US and contain measurement error. They are noisy proxies of 'true' GDP. Several econometric methods exist for producing estimates of true GDP which reconcile these noisy estimates. Recently, the authors of this paper developed a mixed frequency reconciliation model which produces monthly estimates of true GDP. In the present paper, we investigate whether this model continues to work well in the face of the extreme observations that occurred during the pandemic year of 2020 and consider several extensions of it. These extensions include stochastic volatility and error distributions that are fat tailed or explicitly allow for outliers. We also investigate the performance of conditional forecasting, where we estimate our models using data through 2019 and then use these to nowcast throughout 2020. Nowcasts are updated each month of 2020 conditionally on the new data releases which occur each month, but the parameters are not re-estimated. In total we compare the real-time performance of 12 nowcasting approaches over the pandemic months. We find that our original model with Normal homoskedastic errors produces point nowcasts as good or better than any of the other approaches. A property of Normal homoskedastic models that is often considered bad (i.e. that they are not robust to outliers), actually benefits the KMMP model as it reacts confidently to the rapidly evolving economic data. In terms of nowcast densities, we find many of the extensions lead to larger predictive variances reflecting the great uncertainty of the pandemic months

Regional Output Growth in the United Kingdom : More Timely And Higher Frequency Estimates,1970-2017

Output growth estimates for the regions of the UK are currently published at the annual frequency only and are released with a long delay. Regional economists and policymakers would benefit from having higher frequency and more timely estimates. In this paper we develop a mixed frequency Vector Autoregressive (MF-VAR) model and use it to produce estimates of quarterly regional output growth. Temporal and cross-sectional restrictions are imposed in the model to ensure that our quarterly regional estimates are consistent with the annual regional observations and the observed quarterly UK totals. We use a machine learning method based on the hierarchical Dirichlet-Laplace prior to ensure optimal shrinkage and parsimony in our over-parameterised MF-VAR. Thus, this paper presents a new, regional quarterly database of nominal and real Gross Value Added dating back to 1970. We describe how we update and evaluate these estimates on an ongoing, quarterly basis to publish online (at www.escoe.ac.uk/regionalnowcasting) more timely estimates of regional economic growth. We illustrate how the new quarterly data can be used to contribute to our historical understanding of business cycle dynamics and connectedness between regions

Regional nowcasting : an illustration using the Scottish economy

The delays in the release of key economic variables mean that policymakers do not know their current values. Quickly produced, high frequency, indicators are essen- tial in understanding regional performance in a timely fashion. Thus, there is a need for nowcasts, which are estimates of the current values of such variables (e.g. GDP, employ- ment, etc.). This paper nowcasts growth in a regional economy, taking Scotland, UK, as our example. Regional nowcasting is complicated due to issues around data timeliness and availability. For instance, key nowcast predictors such as industrial production are often unavailable at the sub-national level. Accordingly, we use data on some non-traditional variables and investigate whether UK aggregates, and indicators for neighbouring regions of the UK, can help nowcast Scottish GDP growth. We show that these models and methods can be successfully adapted for use in a regional setting, and so produce timely macroeconomic indicators for the regional economy