Factor Model Forecasts for New Zealand

This paper focuses on forecasting four key New Zealand macroeconomic variables using a dynamic factor model and a large number of predictors. We compare the (simulated) real-time forecasting performance of the factor model with a variety of other time-series models (including the Reserve Bank of New Zealand’s published forecasts), and we gauge the sensitivity of our results to alternative variable-selection algorithms. We find that the factor model performs particularly well at longer horizons.

Factor Model Forecasts for New Zealand

This paper focuses on forecasting four key New Zealand macroeconomic variables using a dynamic factor model and a large number of predictors. We compare the (simulated) real-time forecasting performance of the factor model with a variety of other time-series models (including the Reserve Bank of New Zealand’s published forecasts), and we gauge the sensitivity of our results to alternative variable-selection algorithms. We find that the factor model performs particularly well at longer horizons

Factor Model Forecasts for New Zealand

This paper focuses on forecasting four key New Zealand macroeconomic variables using a dynamic factor model and a large number of predictors. We compare the (simulated) real-time forecasting performance of the factor model with a variety of other time-series models (including the Reserve Bank of New Zealand’s published forecasts), and we gauge the sensitivity of our results to alternative variable-selection algorithms. We find that the factor model performs particularly well at longer horizons

Antibody-free magnetic cell sorting of genetically modified primary human CD4+ T cells by one-step streptavidin affinity purification.

Existing methods for phenotypic selection of genetically modified mammalian cells suffer disadvantages of time, cost and scalability and, where antibodies are used to bind exogenous cell surface markers for magnetic selection, typically yield cells coated with antibody-antigen complexes and beads. To overcome these limitations we have developed a method termed Antibody-Free Magnetic Cell Sorting in which the 38 amino acid Streptavidin Binding Peptide (SBP) is displayed at the cell surface by the truncated Low Affinity Nerve Growth Receptor (LNGFRF) and used as an affinity tag for one-step selection with streptavidin-conjugated magnetic beads. Cells are released through competition with the naturally occurring vitamin biotin, free of either beads or antibody-antigen complexes and ready for culture or use in downstream applications. Antibody-Free Magnetic Cell Sorting is a rapid, cost-effective, scalable method of magnetic selection applicable to either viral transduction or transient transfection of cell lines or primary cells. We have optimised the system for enrichment of primary human CD4+ T cells expressing shRNAs and exogenous genes of interest to purities of >99%, and used it to isolate cells following Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9 genome editing

Factor Model Forecasts for New Zealand

This paper focuses on forecasting four key New Zealand macroeconomic variables using a dynamic factor model and a large number of predictors. We compare the (simulated) real-time forecasting performance of the factor model with a variety of other time-series models (including the Reserve Bank of New Zealand’s published forecasts), and we gauge the sensitivity of our results to alternative variable-selection algorithms. We find that the factor model performs particularly well at longer horizons.

An analysis of the informational content of New Zealand data releases: The importance of business opinion surveys

We examine the informational content of New Zealand data releases using a parametric dynamic factor model estimated with unbalanced real-time panels of quarterly data. The data are categorised into 21 different release blocks, allowing us to make 21 different factor model forecasts each quarter. We compare three of these factor model forecasts for real GDP growth, CPI inflation, non-tradable CPI inflation, and tradable CPI inflation with three different real-time forecasts made by the Reserve Bank of New Zealand each quarter. We find that, at some horizons, the factor model produces forecasts of similar accuracy to the Reserve Bank's forecasts. Analysing the marginal value of each of the data releases reveals the importance of the business opinion survey data--the Quarterly Survey of Business Opinion and the National Bank's Business Outlook survey--in determining how factor model predictions, and the uncertainty around those predictions, evolve through each quarter.Real-time forecasting Survey data Factor model

A New Core Inflation Indicator for New Zealand

This paper introduces a new indicator of core inflation for New Zealand, estimated using a dynamic factor model and disaggregate consumer price data. Using disaggregate consumer price data, we can directly compare the predictive performance of our core indicator with a wide range of other 'core inflation' measures estimated from disaggregate consumer prices, such as the weighted median and the trimmed mean. The mediumterm inflation target of the Reserve Bank of New Zealand is used as a guide to define our target measure of core inflation— a centered two-year moving average of past and future inflation outcomes. We find that our indicator produces relatively good estimates of this characterization of core inflation when compared with estimates derived from a range of other models.