UK Regional Nowcasting Using a Mixed Frequency Vector Auto-Regressive Model with Entropic Tilting

Koop, Gary; McIntyre, Stuart; Mitchell, James

doi:10.1111/rssa.12491

Cited by 23 publications

(16 citation statements)

References 45 publications

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“…This method aims to find the relationship between the factors. It has some preconditions (Koop et al, 2020). First, the stationary forms of all variables should be used in the analysis.…”

Section: Analysis Resultsmentioning

confidence: 99%

The Positive Influences of Renewable Energy Consumption on Financial Development and Economic Growth

Yüksel

Dınçer

et al. 2021

Sage Open

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This study aims to evaluate the positive impacts of renewable energy usage on the economic growth and financial development. For this purpose, an evaluation has been performed with VAR analysis. In the analysis process, annual data for the period between 1990 and 2015 is used. The findings show that renewable energy usage and financial development do not have a powerful influence on the economic growth. However, it is identified that renewable energy usage has a positive impact on the financial development. Hence, it is strongly recommended that renewable energy usage should be encouraged by policy makers in the country. Nonetheless, to understand the main indicators of economic growth, many different factors should be taken into consideration. It is seen that owing to the use of environmentally friendly energy, the size of the financial sector increases. It is obvious that environment-friendly energy usage attracts the attention of the financial investors. In this context, it would be appropriate to give some incentives to renewable energy investors. Within this framework, issues such as tax reduction, location support, and low interest loan opportunities will make renewable energy investments more reasonable.

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“…This method aims to find the relationship between the factors. It has some preconditions (Koop et al, 2020). First, the stationary forms of all variables should be used in the analysis.…”

Section: Analysis Resultsmentioning

confidence: 99%

The Positive Influences of Renewable Energy Consumption on Financial Development and Economic Growth

Yüksel

Dınçer

et al. 2021

Sage Open

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“…The existing literature that uses this 3 For an overview of the NUTS classification system, see https://www.ons.gov.uk/methodology/geography/ukgeographies/eurostat 4 See www.escoe.ac.uk/regionalnowcasting 5 Ghysels (2016) offers a detailed discussion of the relationship between the state-space approach and other mixed-frequency methods. Koop, McIntyre, and Mitchell (2019) use one of these other approaches, the stacked VAR approach, in a UK regional nowcasting exercise. The stacked VAR approach does not allow for the calculation of smoothed historical quarterly estimates of regional GVA growth, which is a key innovation of the present paper.…”

Section: Introductionmentioning

confidence: 99%

Regional output growth in the United Kingdom: More timely and higher frequency estimates from 1970

Koop

McIntyre

Mitchell

et al. 2020

J of Applied Econometrics

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Summary Output growth estimates for regions of the UK are currently published at an annual frequency only, released with a long delay, and offer limited historical coverage. To improve the regional database this paper develops a mixed‐frequency multivariate model and uses it to produce consistent estimates of quarterly regional output growth dating back to 1970. We describe how these estimates are updated and evaluated on an ongoing, quarterly basis to publish online (at www.escoe.ac.uk/regionalnowcasting) more timely regional growth estimates. We illustrate how the new quarterly data can contribute to our historical understanding of business cycle dynamics and connectedness between regions.

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“…Finally, if spatial panel data happen to be available at the annual frequency but not at the sub-annual frequency, a mixed frequency model (Koop, McIntyre, and Mitchell 2020) may use the available spatial panel data at the annual frequency, while CCE is used in place of the absent sub-annual spatial panel data. Suppose, for example, in the house price experiment that annual spatial panel data are available for all variables, but sub-annual data are available for all variables apart from housing stocks.…”

Section: Discussionmentioning

confidence: 99%

A Solution for Absent Spatial Data: The Common Correlated Effects Estimator

Beenstock

Felsenstein

2020

International Regional Science Review

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Informed regional policy needs good regional data. As regional data series for key economic variables are generally absent whereas national-level time series data for the same variables are ubiquitous, we suggest an approach that leverages this advantage. We hypothesize the existence of a pervasive “common factor” represented by the national time series that affects regions differentially. We provide an empirical illustration in which national FDI is used in place of panel data for FDI, which are absent. The proposed methodology is tested empirically with respect to the determinants of regional demand for housing. We use a quasi-experimental approach to compare the results of a “common correlated effects” (CCE) estimator with a benchmark case when absent regional data are omitted. Using three common factors relating to national population, income and housing stock, we find mixed support for the common correlated effects hypothesis. We conclude by discussing how our experimental design may serve as a methodological prototype for further tests of CCE as a solution to the absent spatial data problem.

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UK Regional Nowcasting Using a Mixed Frequency Vector Auto-Regressive Model with Entropic Tilting

Cited by 23 publications

References 45 publications

The Positive Influences of Renewable Energy Consumption on Financial Development and Economic Growth

The Positive Influences of Renewable Energy Consumption on Financial Development and Economic Growth

Regional output growth in the United Kingdom: More timely and higher frequency estimates from 1970

A Solution for Absent Spatial Data: The Common Correlated Effects Estimator

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