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The goal of the paper is to examine the dynamics between innovation, market structure and trade performance. Firstly, the author first investigates the effects of innovation on trade performance. Secondly, the author then examines how market structure affect trade by classifying industries based on their innovation intensity.

The author uses a detailed level data set of eight OECD countries in a panel of 17 industries from the STAN and ANBERD Database. The author employs both a pooled regression and a two-stage quantile regression analysis. The author first investigates the effects of innovation at the aggregate level, and then the author assesses the effects at the disaggregated or firm level.

The author finds that at the aggregate level, innovation and market size have a positive and significant effect on competitivity in most of the specifications. However, innovation is negatively associated with trade performance in the case of bilateral trade between Spain and the Netherlands. Also, the sectoral analysis provides evidence that the innovation-trade nexus depends on technological classification. The author shows that: (1) the effect of innovation activity on trade performance economic performance is lower for the high technology and high concentration (HTHC) market compared to the low technology (LT) market; (2) the impact of innovation on economic performance is ambiguous for firms in the high technology and low concentration (HTLC) market.

Although the database provides a rich data set on industrial data, it fails to provide innovation output such as patent data which may underestimate the innovation activities of firms that do not have a separate R&D records. In the current context of subdue economic growth these research results have important policy implications. Firstly, the positive impact of innovation on trade performance strengthens its role for sustainable development. The negative coefficient on innovation is an indication that research intensity in some cases has not been able to create a new demand capable to boost economic performance.

The market classification analysis provides new evidence that innovation in the LT market has the potential to enhance competition. Secondly, market size supports industries that are competing in the international market. Policy makers must therefore put in place incentives to encourage firms to grow in size if they want to remain globally competitive.

Sustainable development can be supported through investment in research and development in the low technology sector.

The study is the first as far as the author knows, to examine the impact of innovation on bilateral trade performance using industry level data from OECD countries. Secondly, the author complements the existing literature by examining how innovation activities (classified as high technological intensive or low technological intensive) affect trade performance.

本研究擬探討創新觀念、市場結構和貿易表現之間的相互變革動力關係。我們首先研究創新觀念對貿易表現的影響,繼而探討市場結構對貿易表現的影響。根據各個行業的創新觀念強度,我們把行業分為不同類別。我們採用八個經濟合作暨發展組織國家的詳細級數據庫,而這八個國家、乃是STAN and ANBERD 數據庫內一個包括17個行業組別內的國家。我們採用混合估計和兩階段分位數回歸分析; 我們首先探討創新觀念所帶來的整體影響,繼而評估細分層面 (即公司層面) 上的影響。我們發現、在整體的層面上,創新觀念和市場規模、在我們大部份的規格上,均對競爭力帶來積極和重要的影響。唯在西班牙與荷蘭兩國之間的雙邊貿易上,創新觀念與貿易表現卻出現負相關的情況。而且,行業分析證實創新與貿易的關係是取決於技術分類的。我們的研究顯示:(1) 與低技術市場相比,於高技術、高集中程度的市場,創新觀念的活動對貿易表現和經濟表現的影響會較低; (2) 對處於高技術、低集中程度市場的公司而言、創新觀念對經濟表現的影響是不明確的。雖然該數據庫在工業數據方面提供一個豐富的數據集,卻未能提供如專利數據等的創新產出,這可能會導致沒有單獨研發記錄公司的創新觀念活動會被低估的情況。在現時經濟成長受到壓制的環境下,這些研究結果提供重要的政策啟示; 首先,創新觀念對貿易表現的積極影響增強了它在可持續發展方面所扮演的角色。創新觀念上的負系數顯示、在某些情況下,研究強度未能創造一個可提高經濟表現的新需求。市場分類分析提供新的證據、證明在低技術市場,創新觀念有提高競爭力的潛力; 其次,市場規模為於國際市場競爭的行業提供支援; 因此,政策制定者必須提供誘因、以鼓勵希望繼續具有全球競爭力的公司擴大其規模。

Having defined liquidity, the aim is to assess the predictive capacity of its representative variables, so that economic fluctuations may be better understood.

Conceptual variables that are representative of liquidity will be used to formulate the predictions. The results of various machine learning models will be compared, leading to some reflections on the predictive value of the liquidity variables, with a view to defining their selection.

The predictive capacity of the model was also found to vary depending on the source of the liquidity, in so far as the data on liquidity within the private sector contributed more than the data on public sector liquidity to the prediction of economic fluctuations. International liquidity was seen as a more diffuse concept, and the standardization of its definition could be the focus of future studies. A benchmarking process was also performed when applying the state-of-the-art machine learning models.

Better understanding of these variables might help us toward a deeper understanding of the operation of financial markets. Liquidity, one of the key financial market variables, is neither well-defined nor standardized in the existing literature, which calls for further study. Hence, the novelty of an applied study employing modern data science techniques can provide a fresh perspective on financial markets.

因此,就了解經濟週期和經濟發展而言,流動資金是一個極其重要的概念。本研究擬在安全資產的價格預測方面取得進步。安全資產代表了經濟的實際情況,特別是美國的十年期國債。

流動資金的定義上面已說明了; 為進一步了解經濟波動,本研究擬對流動資金代表性變量的預測能力進行評估。

研究使用作為流動資金代表的概念變項去規劃預測。各機器學習模型的結果會作比較,這會帶來對流動資金變量的預測值的深思,而深思的目的是確定其選擇。

只要在私營部門內流動資金的數據比公營部門的流動資金數據、在預測經濟波動方面貢獻更大時,我們發現、模型的預測能力也會依賴流動資金的來源而存在差異。國際流動資金被視為一個晦澀的概念,而它的定義的標準化,或許應是未來學術研究的焦點。當應用最先進的機器學習模型時,標桿分析法的步驟也施行了。

若我們對有關的變量加深認識,我們就可更深入地理解金融市場的運作。流動資金,雖是金融市場中一個極其重要的變量,但在現存的學術文獻裏,不但沒有明確的定義,而且也沒有被標準化; 就此而言,未來的研究或許可在這方面作進一步的探討。因此,本研究為富有新穎思維的應用研究,研究使用了現代數據科學技術,這可為探討金融市場提供一個全新的視角。