Forecasting innovative start-ups through automatic variable selection and MIDAS regressions

IF 3.2 3区 经济学 Q1 ECONOMICS Economics of Innovation and New Technology Pub Date : 2023-12-20 DOI:10.1080/10438599.2023.2295939
Consuelo Rubina Nava, Luigi Riso, Maria Grazia Zoia
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Abstract

The paper focuses on the role that both macroeconomic indicators and financial factors play as drivers for the birth and the evolution of the Italian innovative start-ups over time. The analysis ma...
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通过自动变量选择和 MIDAS 回归预测创新型初创企业
本文重点探讨了宏观经济指标和金融因素对意大利创新型初创企业的诞生和发展所起的推动作用。分析结果表明...
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来源期刊
CiteScore
7.20
自引率
3.00%
发文量
30
期刊介绍: Economics of Innovation and New Technology is devoted to the theoretical and empirical analysis of the determinants and effects of innovation, new technology and technological knowledge. The journal aims to provide a bridge between different strands of literature and different contributions of economic theory and empirical economics. This bridge is built in two ways. First, by encouraging empirical research (including case studies, econometric work and historical research), evaluating existing economic theory, and suggesting appropriate directions for future effort in theoretical work. Second, by exploring ways of applying and testing existing areas of theory to the economics of innovation and new technology, and ways of using theoretical insights to inform data collection and other empirical research. The journal welcomes contributions across a wide range of issues concerned with innovation, including: the generation of new technological knowledge, innovation in product markets, process innovation, patenting, adoption, diffusion, innovation and technology policy, international competitiveness, standardization and network externalities, innovation and growth, technology transfer, innovation and market structure, innovation and the environment, and across a broad range of economic activity not just in ‘high technology’ areas. The journal is open to a variety of methodological approaches ranging from case studies to econometric exercises with sound theoretical modelling, empirical evidence both longitudinal and cross-sectional about technologies, regions, firms, industries and countries.
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