index.json

[{"authors":["admin"],"categories":null,"content":"I am an econometrician passionate about data science and economics. I exploit alternative, unstructured and big data for economic forecasting using time series and machine learning. I work with macroeconomic and natural resource data to provide better predictions and policy support.\nI am part of the Competence Centre on Microeconomic Evaluation (CC-ME) at the European Commission - Joint Research Centre (JRC). Previously, I was part of the Nowcasting team.\n  Download my CV for an update on recent working papers.\n","date":1607817600,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1607817600,"objectID":"2525497d367e79493fd32b198b28f040","permalink":"","publishdate":"0001-01-01T00:00:00Z","relpermalink":"","section":"authors","summary":"I am an econometrician passionate about data science and economics. I exploit alternative, unstructured and big data for economic forecasting using time series and machine learning. I work with macroeconomic and natural resource data to provide better predictions and policy support.","tags":null,"title":"","type":"authors"},{"authors":null,"categories":null,"content":"   Table of Contents  What you will learn Program overview Courses in this program Meet your instructor FAQs    What you will learn  Fundamental Python programming skills Statistical concepts and how to apply them in practice Gain experience with the Scikit, including data visualization with Plotly and data wrangling with Pandas  Program overview The demand for skilled data science practitioners is rapidly growing. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi.\nCourses in this program  Python basics Build a foundation in Python.   Visualization Learn how to visualize data with Plotly.   Statistics Introduction to statistics for data science.   Meet your instructor admin FAQs Are there prerequisites? There are no prerequisites for the first course.\n How often do the courses run? Continuously, at your own pace.\n  Begin the course   ","date":1611446400,"expirydate":-62135596800,"kind":"section","lang":"en","lastmod":1611446400,"objectID":"59c3ce8e202293146a8a934d37a4070b","permalink":"https://lucabarbaglia.github.io/courses/example/","publishdate":"2021-01-24T00:00:00Z","relpermalink":"/courses/example/","section":"courses","summary":"An example of using Wowchemy's Book layout for publishing online courses.","tags":null,"title":"📊 Learn Data Science","type":"book"},{"authors":null,"categories":null,"content":"Build a foundation in Python.\n  1-2 hours per week, for 8 weeks\nLearn   Quiz What is the difference between lists and tuples? Lists\n Lists are mutable - they can be changed Slower than tuples Syntax: a_list = [1, 2.0, 'Hello world']  Tuples\n Tuples are immutable - they can\u0026rsquo;t be changed Tuples are faster than lists Syntax: a_tuple = (1, 2.0, 'Hello world')   Is Python case-sensitive? Yes\n","date":1609459200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1609459200,"objectID":"17a31b92253d299002593b7491eedeea","permalink":"https://lucabarbaglia.github.io/courses/example/python/","publishdate":"2021-01-01T00:00:00Z","relpermalink":"/courses/example/python/","section":"courses","summary":"Build a foundation in Python.\n","tags":null,"title":"Python basics","type":"book"},{"authors":null,"categories":null,"content":"Learn how to visualize data with Plotly.\n  1-2 hours per week, for 8 weeks\nLearn   Quiz When is a heatmap useful? Lorem ipsum dolor sit amet, consectetur adipiscing elit.\n Write Plotly code to render a bar chart import plotly.express as px data_canada = px.data.gapminder().query(\u0026quot;country == 'Canada'\u0026quot;) fig = px.bar(data_canada, x='year', y='pop') fig.show()  ","date":1609459200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1609459200,"objectID":"1b341b3479c8c6b1f807553b77e21b7c","permalink":"https://lucabarbaglia.github.io/courses/example/visualization/","publishdate":"2021-01-01T00:00:00Z","relpermalink":"/courses/example/visualization/","section":"courses","summary":"Learn how to visualize data with Plotly.\n","tags":null,"title":"Visualization","type":"book"},{"authors":null,"categories":null,"content":"Introduction to statistics for data science.\n  1-2 hours per week, for 8 weeks\nLearn The general form of the normal probability density function is:\n$$ f(x) = \\frac{1}{\\sigma \\sqrt{2\\pi} } e^{-\\frac{1}{2}\\left(\\frac{x-\\mu}{\\sigma}\\right)^2} $$\n The parameter $\\mu$ is the mean or expectation of the distribution. $\\sigma$ is its standard deviation. The variance of the distribution is $\\sigma^{2}$.   Quiz What is the parameter $\\mu$? The parameter $\\mu$ is the mean or expectation of the distribution.\n","date":1609459200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1609459200,"objectID":"6f4078728d71b1b791d39f218bf2bdb1","permalink":"https://lucabarbaglia.github.io/courses/example/stats/","publishdate":"2021-01-01T00:00:00Z","relpermalink":"/courses/example/stats/","section":"courses","summary":"Introduction to statistics for data science.\n","tags":null,"title":"Statistics","type":"book"},{"authors":[],"categories":null,"content":" Click on the Slides button above to view the built-in slides feature.   Slides can be added in a few ways:\n Create slides using Wowchemy\u0026rsquo;s Slides feature and link using slides parameter in the front matter of the talk file Upload an existing slide deck to static/ and link using url_slides parameter in the front matter of the talk file Embed your slides (e.g. Google Slides) or presentation video on this page using shortcodes.  Further event details, including page elements such as image galleries, can be added to the body of this page.\n","date":1906549200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1906549200,"objectID":"a8edef490afe42206247b6ac05657af0","permalink":"https://lucabarbaglia.github.io/talk/example-talk/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/talk/example-talk/","section":"event","summary":"An example talk using Wowchemy's Markdown slides feature.","tags":[],"title":"Example Talk","type":"event"},{"authors":["Barbaglia","Manzan","Tosetti"],"categories":null,"content":"s\n","date":1711929600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1711929600,"objectID":"aa575a390996a06b0c661bbed85c6327","permalink":"https://lucabarbaglia.github.io/publication/2020_ed_macro/","publishdate":"2024-04-01T00:00:00Z","relpermalink":"/publication/2020_ed_macro/","section":"publication","summary":"We investigate the role and impact of household debt on the economic performance of the European economy during the double-dip recession of 2008-2013. We use a loan-level data set of millions of residential mortgages originated between 2000 and 2013 to calculate regional indicators of household debt and property prices. The detailed information allows us to construct a measure of interest rate mispricing during the housing boom that we use to identify the effect of a credit shock on household debt. Our analysis provides three main conclusions. First, in the period 2004-2006 the measure of credit shock was negative in most European regions which indicates that credit conditions were significantly relaxed relative to earlier years. Second, we find that regions in which household leverage increased more rapidly during the 2004-2006 period experienced a more severe decline in output and employment after 2008. These results are consistent with the view that an aggregate credit supply shock in Europe boosted household leverage and house prices. Third, we find that the credit shock had the largest effect on increasing leverage for the low-income and the middle-income households, although the change in leverage of the middle-income households represents a more powerful predictor of the decline in economic activity.","tags":"","title":"Household Debt and Economic Growth in Europe","type":"publication"},{"authors":["Barbaglia","Consoli","Manzan"],"categories":null,"content":"","date":1705104000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1705104000,"objectID":"6718fc90ab7a088660f09b9835afe454","permalink":"https://lucabarbaglia.github.io/publication/2022_forecastingwitheconomicnews_eu/","publishdate":"2024-01-13T00:00:00Z","relpermalink":"/publication/2022_forecastingwitheconomicnews_eu/","section":"publication","summary":"We evaluate the informational content of news-based sentiment indicators for forecasting the Gross Domestic Product (GDP) of the five major European economies. The sentiment indicators that we construct are aspect-based, in the sense that we consider only the text that is related to a specific economic aspect of interest. In addition, the sentiment is fine-grained as each word is assigned a score in the interval [-1, 1]. Our data set includes over 27 million articles for 26 major newspapers in 5 different languages. The evidence indicates that these sentiment indicators are significant predictors to forecast GDP and their predictive content is robust to controlling for macroeconomic and survey confidence indicators available to forecasters in real-time. We also discuss the application of the sentiment indicators during the COVID-19 pandemic and demonstrate their relevance in nowcasting GDP.","tags":"","title":"Forecasting GDP in Europe with Textual Data","type":"publication"},{"authors":["Barbaglia","Consoli","Manzan"],"categories":null,"content":"","date":1691107200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1691107200,"objectID":"702f77406bd48eaa54d5ff0d14b9815c","permalink":"https://lucabarbaglia.github.io/publication/2022_forecastingwitheconomicnews/","publishdate":"2023-08-04T00:00:00Z","relpermalink":"/publication/2022_forecastingwitheconomicnews/","section":"publication","summary":"The goal of this paper is to evaluate the informational content of sentiment extracted from news articles about the state of the economy. We propose a Fine-Grained Aspect-based Sentiment analysis that has two main characteristics: 1) we consider only the text in the article that is semantically dependent on a term of interest (aspect-based) and, 2) assign a sentiment score to each word based on a dictionary that we develop for applications in economics and finance (fine-grained). Our data set includes six large US newspapers, for a total of over 6.6 million articles and 4.2 billion words. Our findings suggest that several measures of economic sentiment track closely business cycle fluctuations and that they are relevant predictors for four major macroeconomic variables. The forecast accuracy increases significantly when economic sentiment is used in a time series model as these measures tend to proxy for the overall state of the economy. We also find that there are significant improvements in forecasting when sentiment is considered along with macroeconomic factors. In addition, we also consider the role of sentiment in the tails of the distribution and find that economic sentiment matters, in particular at low quantiles.","tags":"","title":"Forecasting with Economic News","type":"publication"},{"authors":["Barbaglia","Manzan","Tosetti"],"categories":null,"content":"","date":1688342400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1688342400,"objectID":"c8d51226d953ba7400a3bb111d263c45","permalink":"https://lucabarbaglia.github.io/publication/2021_ed_default/","publishdate":"2023-07-03T00:00:00Z","relpermalink":"/publication/2021_ed_default/","section":"publication","summary":"We use a large data set of over 12 million residential mortgages observed over time to investigate the loan default behavior in several European countries. We model the occurrence of default as a function of borrower characteristics, loan-specific variables, and a set of local economic conditions. Given the high geographical heterogeneity in default and its drivers, we carry out the analysis at the regional level. We adopt boosting algorithms from the machine learning literature and compare their performance relative to the logistic regression. With respect to the logistic benchmark, boosting models perform significantly better in providing predictions. The most important variables in explaining loan default are the interest rate currently applied to the mortgage and the local economic characteristics, while other loan- or borrower-specific features are less relevant. Our results indicate the existence of relevant geographical heterogeneity in the importance of the variables, pointing at the need for regionally tailored risk assessment and policies in Europe.","tags":"","title":"Forecasting Loan Default in Europe with Machine Learning","type":"publication"},{"authors":["Barbaglia","Frattarolo","Onorante","Pericoli","Ratto","Tiozzo Pezzoli"],"categories":null,"content":"","date":1667520000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1667520000,"objectID":"28d71e99f965e40be6c3a50d11bd98cc","permalink":"https://lucabarbaglia.github.io/publication/2022_testingbigdataincovid19/","publishdate":"2022-11-04T00:00:00Z","relpermalink":"/publication/2022_testingbigdataincovid19/","section":"publication","summary":"","tags":"","title":"Testing Big Data in a Big Crisis: Nowcasting under COVID-19","type":"publication"},{"authors":["Barbaglia","Consoli","Manzan","Tiozzo Pezzoli","Tosetti"],"categories":null,"content":"","date":1652227200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1652227200,"objectID":"2dd130a6039b0eefc9e1ba31ad542afd","permalink":"https://lucabarbaglia.github.io/publication/2022_el_dictionary/","publishdate":"2022-05-11T00:00:00Z","relpermalink":"/publication/2022_el_dictionary/","section":"publication","summary":"The goal of this paper is to propose a dictionary specifically designed for textual applications in economics. We construct the dictionary with two important characteristics: i) to have a wide coverage of terms typically used in documents discussing economic and financial concepts, and ii) to provide a human-annotated sentiment score in the range [-1,1]. The sentiment score is a useful feature when the interest is to weight words by their sentiment content as opposed to categorizing terms in positive and negative. We use the dictionary to construct a measure of economic pessimism and show that it captures the business cycle and correlates with measures of economic and financial uncertainty.","tags":"","title":"Sentiment Analysis of Economic Text: A Lexicon-based Approach","type":"publication"},{"authors":["Consoli","Barbaglia","Manzan"],"categories":null,"content":"","date":1648771200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1648771200,"objectID":"bdd28ff1fec9399747eb22e11294b0c0","permalink":"https://lucabarbaglia.github.io/publication/2022_eval/","publishdate":"2022-04-01T00:00:00Z","relpermalink":"/publication/2022_eval/","section":"publication","summary":"The last two decades have seen a tremendous increase in the adoption of Semantic Web technologies as a result of the availability of big data, the growth in computational power and the advancement of artificial intelligence (AI) technologies. Cutting-edge semantic techniques are now able to capture sentiments more accurately in various practical applications, including economic and financial forecasting. In particular, the extraction of sentiment from news text, social media and blogs for the prediction of economic and financial variables has attracted attention in recent years. Despite many successful applications of sentiment analysis (SA) in these domains, the range of semantic techniques employed is still limited and mostly focused on the detection of sentiment at a coarse-grained level, that is, whether the sentiment expressed by the entire text of a sentence is either positive or negative. This paper proposes a novel methodology for Fine-Grained Aspect-based Sentiment (FiGAS) analysis. The aim of the approach is to identify the sentiment associated to specific topics of interest in each sentence of a document and assigning real-valued polarity scores between -1 and +1 to those topics. The proposed approach is completely unsupervised and customized to the economic and financial domains by using a specialized lexicon make available along with the source code of FiGAS. Our lexicon-based SA approach relies on a detailed set of semantic polarity rules that allow understanding the origin of sentiment, in the spirit of the recent trend on Interpretable AI. We provide an in-depth comparison of the performance of the FiGAS algorithm relative to other popular lexicon-based SA approaches in predicting a humanly annotated data set in the economic and financial domains. Our results indicate that FiGAS statistically outperforms the other methods by providing a sentiment score that is closer to one of the human annotators.","tags":"","title":"Fine-Grained, Aspect-Based Sentiment Analysis on Economic and Financial Lexicon","type":"publication"},{"authors":["Barbaglia","Croux","Wilms"],"categories":null,"content":"","date":1640995200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1640995200,"objectID":"c7b7305388684dd243abd1c4f1384732","permalink":"https://lucabarbaglia.github.io/publication/2022_antidumping/","publishdate":"2022-01-01T00:00:00Z","relpermalink":"/publication/2022_antidumping/","section":"publication","summary":"Despite the increasing integration of the global economic system, anti-dumping measures are a common tool used by governments to protect their national economy. In this paper, we propose a methodology to detect cases of anti-dumping circumvention through re-routing trade via a third country. Based on the observed full network of trade flows, we propose a measure to proxy the evasion of an anti-dumping duty for a subset of trade flows directed to the European Union, and look for possible cases of circumvention of an active anti-dumping duty. Using panel regression, we are able correctly classify 86% of the trade flows, on which an investigation of  anti-dumping circumvention has been opened by the European authorities.","tags":"","title":"Detecting Anti-dumping Circumvention: A Network Approach","type":"publication"},{"authors":["Consoli","Barbaglia","Manzan"],"categories":null,"content":"","date":1610665200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1610665200,"objectID":"04dd02960fb071081ae214410f1332b0","permalink":"https://lucabarbaglia.github.io/publication/2021_midas/","publishdate":"2021-01-15T00:00:00+01:00","relpermalink":"/publication/2021_midas/","section":"publication","summary":"Forecasting economic and financial variables is a challenging task for several reasons, such as the low signal-to-noise ratio, regime changes, and the effect of volatility among others. A recent trend is to extract information from news as an additional source to forecast economic activity and financial variables. The goal is to evaluate if news can improve forecasts from standard methods that usually are not well-specified and have poor out-of-sample performance. In a currently on-going project, our goal is to combine a richer information set that includes news with a state-of-the-art machine learning model. In particular, we leverage on two recent advances in Data Science, specifically on Word Embedding and Deep Learning models, which have recently attracted extensive attention in many scientific fields. We believe that by combining the two methodologies, effective solutions can be built to improve the prediction accuracy for economic and financial time series. In this preliminary contribution, we provide an overview of the methodology under development and some initial empirical findings. The forecasting model is based on DeepAR, an auto-regressive probabilistic Recurrent Neural Network model, that is combined with GloVe Word Embeddings extracted from economic news. The target variable is the spread between the US 10-Year Treasury Constant Maturity and the 3-Month Treasury Constant Maturity (T10Y3M). The DeepAR model is trained on a large number of related GloVe Word Embedding time series, and employed to produce point and density forecasts.","tags":null,"title":"Exploring the Predictive Power of News and Neural Machine Learning Models for Economic Forecasting","type":"publication"},{"authors":["","吳恩達"],"categories":["Demo","教程"],"content":"Overview  The Wowchemy website builder for Hugo, along with its starter templates, is designed for professional creators, educators, and teams/organizations - although it can be used to create any kind of site The template can be modified and customised to suit your needs. It\u0026rsquo;s a good platform for anyone looking to take control of their data and online identity whilst having the convenience to start off with a no-code solution (write in Markdown and customize with YAML parameters) and having flexibility to later add even deeper personalization with HTML and CSS You can work with all your favourite tools and apps with hundreds of plugins and integrations to speed up your workflows, interact with your readers, and much more    The template is mobile first with a responsive design to ensure that your site looks stunning on every device.   Get Started  👉 Create a new site 📚 Personalize your site 💬 Chat with the Wowchemy community or Hugo community 🐦 Twitter: @wowchemy @GeorgeCushen #MadeWithWowchemy 💡 Request a feature or report a bug for Wowchemy ⬆️ Updating Wowchemy? View the Update Guide and Release Notes  Crowd-funded open-source software To help us develop this template and software sustainably under the MIT license, we ask all individuals and businesses that use it to help support its ongoing maintenance and development via sponsorship.\n❤️ Click here to become a sponsor and help support Wowchemy\u0026rsquo;s future ❤️ As a token of appreciation for sponsoring, you can unlock these awesome rewards and extra features 🦄✨\nEcosystem  Hugo Academic CLI: Automatically import publications from BibTeX  Inspiration Check out the latest demo of what you\u0026rsquo;ll get in less than 10 minutes, or view the showcase of personal, project, and business sites.\nFeatures  Page builder - Create anything with widgets and elements Edit any type of content - Blog posts, publications, talks, slides, projects, and more! Create content in Markdown, Jupyter, or RStudio Plugin System - Fully customizable color and font themes Display Code and Math - Code highlighting and LaTeX math supported Integrations - Google Analytics, Disqus commenting, Maps, Contact Forms, and more! Beautiful Site - Simple and refreshing one page design Industry-Leading SEO - Help get your website found on search engines and social media Media Galleries - Display your images and videos with captions in a customizable gallery Mobile Friendly - Look amazing on every screen with a mobile friendly version of your site Multi-language - 34+ language packs including English, 中文, and Português Multi-user - Each author gets their own profile page Privacy Pack - Assists with GDPR Stand Out - Bring your site to life with animation, parallax backgrounds, and scroll effects One-Click Deployment - No servers. No databases. Only files.  Themes Wowchemy and its templates come with automatic day (light) and night (dark) mode built-in. Alternatively, visitors can choose their preferred mode - click the moon icon in the top right of the Demo to see it in action! Day/night mode can also be disabled by the site admin in params.toml.\nChoose a stunning theme and font for your site. Themes are fully customizable.\nLicense Copyright 2016-present George Cushen.\nReleased under the MIT license.\n","date":1607817600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1607817600,"objectID":"279b9966ca9cf3121ce924dca452bb1c","permalink":"https://lucabarbaglia.github.io/post/getting-started/","publishdate":"2020-12-13T00:00:00Z","relpermalink":"/post/getting-started/","section":"post","summary":"Welcome 👋 We know that first impressions are important, so we've populated your new site with some initial content to help you get familiar with everything in no time.","tags":["Academic","开源"],"title":"Welcome to Wowchemy, the website builder for Hugo","type":"post"},{"authors":["Barbaglia","Consoli","Manzan"],"categories":null,"content":"","date":1578006000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1578006000,"objectID":"4f21f9131707726176801ff636a989d5","permalink":"https://lucabarbaglia.github.io/publication/2020_dna_midas/","publishdate":"2020-01-03T00:00:00+01:00","relpermalink":"/publication/2020_dna_midas/","section":"publication","summary":"We provide an overview on the development of a fine-grained, aspect-based sentiment analysis approach aimed at providing useful signals to improve forecasts of economic models and produce more accurate predictions. The approach is unsupervised since it relies on external lexical resources to associate a polarity score to a given term or concept. After providing an overview of the method under development, some preliminary findings are also given.","tags":null,"title":"Monitoring the Business Cycle with Fine-Grained, Aspect-Based Sentiment Extraction from News","type":"publication"},{"authors":["Barbaglia","Croux","Wilms"],"categories":null,"content":"","date":1577833200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1577833200,"objectID":"7a5b1d2202fccc83f224656149ef3205","permalink":"https://lucabarbaglia.github.io/publication/2019_eneco_tvar/","publishdate":"2020-01-01T00:00:00+01:00","relpermalink":"/publication/2019_eneco_tvar/","section":"publication","summary":"Volatility is a key measure of risk in financial analysis. The high volatility of one financial asset today could affect the volatility of another asset tomorrow. These lagged effects among volatilities - which we call volatility spillovers - are studied using the Vector AutoRegressive (VAR) model. We account for the possible fat-tailed distribution of the VAR model errors using a VAR model with errors following a multivariate Student t-distribution with unknown degrees of freedom. Moreover, we study volatility spillovers among a large number of assets. To this end, we use penalized estimation of the VAR model with t-distributed errors. We study volatility spillovers among energy, biofuel and agricultural commodities and reveal bidirectional volatility spillovers between energy and biofuel, and between energy and agricultural commodities.","tags":null,"title":"Volatility spillovers in commodity markets: A large t-vector autoregressive approach","type":"publication"},{"authors":null,"categories":null,"content":"Academic is designed to give technical content creators a seamless experience. You can focus on the content and Academic handles the rest.\nHighlight your code snippets, take notes on math classes, and draw diagrams from textual representation.\nOn this page, you\u0026rsquo;ll find some examples of the types of technical content that can be rendered with Academic.\nExamples Code Academic supports a Markdown extension for highlighting code syntax. You can enable this feature by toggling the highlight option in your config/_default/params.toml file.\n```python import pandas as pd data = pd.read_csv(\u0026quot;data.csv\u0026quot;) data.head() ```  renders as\nimport pandas as pd data = pd.read_csv(\u0026quot;data.csv\u0026quot;) data.head()  Charts Academic supports the popular Plotly chart format.\nSave your Plotly JSON in your page folder, for example chart.json, and then add the {{\u0026lt; chart data=\u0026quot;chart\u0026quot; \u0026gt;}} shortcode where you would like the chart to appear.\nDemo:\n  (function() { let a = setInterval( function() { if ( typeof window.Plotly === 'undefined' ) { return; } clearInterval( a ); Plotly.d3.json(\"./line-chart.json\", function(chart) { Plotly.plot('chart-297684513', chart.data, chart.layout, {responsive: true}); }); }, 500 ); })();  You might also find the Plotly JSON Editor useful.\nMath Academic supports a Markdown extension for $\\LaTeX$ math. You can enable this feature by toggling the math option in your config/_default/params.toml file.\nTo render inline or block math, wrap your LaTeX math with $...$ or $$...$$, respectively.\nExample math block:\n$$\\gamma_{n} = \\frac{ \\left | \\left (\\mathbf x_{n} - \\mathbf x_{n-1} \\right )^T \\left [\\nabla F (\\mathbf x_{n}) - \\nabla F (\\mathbf x_{n-1}) \\right ] \\right |} {\\left \\|\\nabla F(\\mathbf{x}_{n}) - \\nabla F(\\mathbf{x}_{n-1}) \\right \\|^2}$$  renders as\n$$\\gamma_{n} = \\frac{ \\left | \\left (\\mathbf x_{n} - \\mathbf x_{n-1} \\right )^T \\left [\\nabla F (\\mathbf x_{n}) - \\nabla F (\\mathbf x_{n-1}) \\right ] \\right |}{\\left |\\nabla F(\\mathbf{x}_{n}) - \\nabla F(\\mathbf{x}_{n-1}) \\right |^2}$$\nExample inline math $\\nabla F(\\mathbf{x}_{n})$ renders as $\\nabla F(\\mathbf{x}_{n})$.\nExample multi-line math using the \\\\\\\\ math linebreak:\n$$f(k;p_{0}^{*}) = \\begin{cases}p_{0}^{*} \u0026amp; \\text{if }k=1, \\\\\\\\ 1-p_{0}^{*} \u0026amp; \\text{if }k=0.\\end{cases}$$  renders as\n$$f(k;p_{0}^{*}) = \\begin{cases}p_{0}^{*} \u0026amp; \\text{if }k=1, \\\\\n1-p_{0}^{*} \u0026amp; \\text{if }k=0.\\end{cases}$$\nDiagrams Academic supports a Markdown extension for diagrams. You can enable this feature by toggling the diagram option in your config/_default/params.toml file or by adding diagram: true to your page front matter.\nAn example flowchart:\n```mermaid graph TD A[Hard] --\u0026gt;|Text| B(Round) B --\u0026gt; C{Decision} C --\u0026gt;|One| D[Result 1] C --\u0026gt;|Two| E[Result 2] ```  renders as\ngraph TD A[Hard] --\u0026gt;|Text| B(Round) B --\u0026gt; C{Decision} C --\u0026gt;|One| D[Result 1] C --\u0026gt;|Two| E[Result 2]  An example sequence diagram:\n```mermaid sequenceDiagram Alice-\u0026gt;\u0026gt;John: Hello John, how are you? loop Healthcheck John-\u0026gt;\u0026gt;John: Fight against hypochondria end Note right of John: Rational thoughts! John--\u0026gt;\u0026gt;Alice: Great! John-\u0026gt;\u0026gt;Bob: How about you? Bob--\u0026gt;\u0026gt;John: Jolly good! ```  renders as\nsequenceDiagram Alice-\u0026gt;\u0026gt;John: Hello John, how are you? loop Healthcheck John-\u0026gt;\u0026gt;John: Fight against hypochondria end Note right of John: Rational thoughts! John--\u0026gt;\u0026gt;Alice: Great! John-\u0026gt;\u0026gt;Bob: How about you? Bob--\u0026gt;\u0026gt;John: Jolly good!  An example Gantt diagram:\n```mermaid gantt section Section Completed :done, des1, 2014-01-06,2014-01-08 Active :active, des2, 2014-01-07, 3d Parallel 1 : des3, after des1, 1d Parallel 2 : des4, after des1, 1d Parallel 3 : des5, after des3, 1d Parallel 4 : des6, after des4, 1d ```  renders as\ngantt section Section Completed :done, des1, 2014-01-06,2014-01-08 Active :active, des2, 2014-01-07, 3d Parallel 1 : des3, after des1, 1d Parallel 2 : des4, after des1, 1d Parallel 3 : des5, after des3, 1d Parallel 4 : des6, after des4, 1d  An example class diagram:\n```mermaid classDiagram Class01 \u0026lt;|-- AveryLongClass : Cool \u0026lt;\u0026lt;interface\u0026gt;\u0026gt; Class01 Class09 --\u0026gt; C2 : Where am i? Class09 --* C3 Class09 --|\u0026gt; Class07 Class07 : equals() Class07 : Object[] elementData Class01 : size() Class01 : int chimp Class01 : int gorilla class Class10 { \u0026lt;\u0026lt;service\u0026gt;\u0026gt; int id size() } ```  renders as\nclassDiagram Class01 \u0026lt;|-- AveryLongClass : Cool \u0026lt;\u0026lt;interface\u0026gt;\u0026gt; Class01 Class09 --\u0026gt; C2 : Where am i? Class09 --* C3 Class09 --|\u0026gt; Class07 Class07 : equals() Class07 : Object[] elementData Class01 : size() Class01 : int chimp Class01 : int gorilla class Class10 { \u0026lt;\u0026lt;service\u0026gt;\u0026gt; int id size() }  An example state diagram:\n```mermaid stateDiagram [*] --\u0026gt; Still Still --\u0026gt; [*] Still --\u0026gt; Moving Moving --\u0026gt; Still Moving --\u0026gt; Crash Crash --\u0026gt; [*] ```  renders as\nstateDiagram [*] --\u0026gt; Still Still --\u0026gt; [*] Still --\u0026gt; Moving Moving --\u0026gt; Still Moving --\u0026gt; Crash Crash --\u0026gt; [*]  Todo lists You can even write your todo lists in Academic too:\n- [x] Write math example - [x] Write diagram example - [ ] Do something else  renders as\n Write math example Write diagram example Do something else  Tables Represent your data in tables:\n| First Header | Second Header | | ------------- | ------------- | | Content Cell | Content Cell | | Content Cell | Content Cell |  renders as\n   First Header Second Header     Content Cell Content Cell   Content Cell Content Cell    Callouts Academic supports a shortcode for callouts, also referred to as asides, hints, or alerts. By wrapping a paragraph in {{% callout note %}} ... {{% /callout %}}, it will render as an aside.\n{{% callout note %}} A Markdown aside is useful for displaying notices, hints, or definitions to your readers. {{% /callout %}}  renders as\n A Markdown aside is useful for displaying notices, hints, or definitions to your readers.   Spoilers Add a spoiler to a page to reveal text, such as an answer to a question, after a button is clicked.\n{{\u0026lt; spoiler text=\u0026quot;Click to view the spoiler\u0026quot; \u0026gt;}} You found me! {{\u0026lt; /spoiler \u0026gt;}}  renders as\nClick to view the spoiler You found me!\n Icons Academic enables you to use a wide range of icons from Font Awesome and Academicons in addition to emojis.\nHere are some examples using the icon shortcode to render icons:\n{{\u0026lt; icon name=\u0026quot;terminal\u0026quot; pack=\u0026quot;fas\u0026quot; \u0026gt;}} Terminal {{\u0026lt; icon name=\u0026quot;python\u0026quot; pack=\u0026quot;fab\u0026quot; \u0026gt;}} Python {{\u0026lt; icon name=\u0026quot;r-project\u0026quot; pack=\u0026quot;fab\u0026quot; \u0026gt;}} R  renders as\n  Terminal\n Python\n R\nDid you find this page helpful? Consider sharing it 🙌 ","date":1562889600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1562889600,"objectID":"07e02bccc368a192a0c76c44918396c3","permalink":"https://lucabarbaglia.github.io/post/writing-technical-content/","publishdate":"2019-07-12T00:00:00Z","relpermalink":"/post/writing-technical-content/","section":"post","summary":"Academic is designed to give technical content creators a seamless experience. You can focus on the content and Academic handles the rest.\nHighlight your code snippets, take notes on math classes, and draw diagrams from textual representation.","tags":null,"title":"Writing technical content in Academic","type":"post"},{"authors":[""],"categories":[],"content":"from IPython.core.display import Image Image('https://www.python.org/static/community_logos/python-logo-master-v3-TM-flattened.png')  print(\u0026quot;Welcome to Academic!\u0026quot;)  Welcome to Academic!  Install Python and JupyterLab Install Anaconda which includes Python 3 and JupyterLab.\nAlternatively, install JupyterLab with pip3 install jupyterlab.\nCreate or upload a Jupyter notebook Run the following commands in your Terminal, substituting \u0026lt;MY-WEBSITE-FOLDER\u0026gt; and \u0026lt;SHORT-POST-TITLE\u0026gt; with the file path to your Academic website folder and a short title for your blog post (use hyphens instead of spaces), respectively:\nmkdir -p \u0026lt;MY-WEBSITE-FOLDER\u0026gt;/content/post/\u0026lt;SHORT-POST-TITLE\u0026gt;/ cd \u0026lt;MY-WEBSITE-FOLDER\u0026gt;/content/post/\u0026lt;SHORT-POST-TITLE\u0026gt;/ jupyter lab index.ipynb  The jupyter command above will launch the JupyterLab editor, allowing us to add Academic metadata and write the content.\nEdit your post metadata The first cell of your Jupter notebook will contain your post metadata (front matter).\nIn Jupter, choose Markdown as the type of the first cell and wrap your Academic metadata in three dashes, indicating that it is YAML front matter:\n--- title: My post's title date: 2019-09-01 # Put any other Academic metadata here... ---  Edit the metadata of your post, using the documentation as a guide to the available options.\nTo set a featured image, place an image named featured into your post\u0026rsquo;s folder.\nFor other tips, such as using math, see the guide on writing content with Academic.\nConvert notebook to Markdown jupyter nbconvert index.ipynb --to markdown --NbConvertApp.output_files_dir=.  Example This post was created with Jupyter. The orginal files can be found at https://github.com/gcushen/hugo-academic/tree/master/exampleSite/content/post/jupyter\n","date":1549324800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1567641600,"objectID":"6e929dc84ed3ef80467b02e64cd2ed64","permalink":"https://lucabarbaglia.github.io/post/jupyter/","publishdate":"2019-02-05T00:00:00Z","relpermalink":"/post/jupyter/","section":"post","summary":"Learn how to blog in Academic using Jupyter notebooks","tags":[],"title":"Display Jupyter Notebooks with Academic","type":"post"},{"authors":[],"categories":[],"content":"Create slides in Markdown with Wowchemy Wowchemy | Documentation\n Features  Efficiently write slides in Markdown 3-in-1: Create, Present, and Publish your slides Supports speaker notes Mobile friendly slides   Controls  Next: Right Arrow or Space Previous: Left Arrow Start: Home Finish: End Overview: Esc Speaker notes: S Fullscreen: F Zoom: Alt + Click PDF Export: E   Code Highlighting Inline code: variable\nCode block:\nporridge = \u0026quot;blueberry\u0026quot; if porridge == \u0026quot;blueberry\u0026quot;: print(\u0026quot;Eating...\u0026quot;)   Math In-line math: $x + y = z$\nBlock math:\n$$ f\\left( x \\right) = ;\\frac{{2\\left( {x + 4} \\right)\\left( {x - 4} \\right)}}{{\\left( {x + 4} \\right)\\left( {x + 1} \\right)}} $$\n Fragments Make content appear incrementally\n{{% fragment %}} One {{% /fragment %}} {{% fragment %}} **Two** {{% /fragment %}} {{% fragment %}} Three {{% /fragment %}}  Press Space to play!\nOne  Two  Three \n A fragment can accept two optional parameters:\n class: use a custom style (requires definition in custom CSS) weight: sets the order in which a fragment appears   Speaker Notes Add speaker notes to your presentation\n{{% speaker_note %}} - Only the speaker can read these notes - Press `S` key to view {{% /speaker_note %}}  Press the S key to view the speaker notes!\n Only the speaker can read these notes Press S key to view    Themes  black: Black background, white text, blue links (default) white: White background, black text, blue links league: Gray background, white text, blue links beige: Beige background, dark text, brown links sky: Blue background, thin dark text, blue links    night: Black background, thick white text, orange links serif: Cappuccino background, gray text, brown links simple: White background, black text, blue links solarized: Cream-colored background, dark green text, blue links   Custom Slide Customize the slide style and background\n{{\u0026lt; slide background-image=\u0026quot;/media/boards.jpg\u0026quot; \u0026gt;}} {{\u0026lt; slide background-color=\u0026quot;#0000FF\u0026quot; \u0026gt;}} {{\u0026lt; slide class=\u0026quot;my-style\u0026quot; \u0026gt;}}   Custom CSS Example Let\u0026rsquo;s make headers navy colored.\nCreate assets/css/reveal_custom.css with:\n.reveal section h1, .reveal section h2, .reveal section h3 { color: navy; }   Questions? Ask\nDocumentation\n","date":1549324800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1549324800,"objectID":"0e6de1a61aa83269ff13324f3167c1a9","permalink":"https://lucabarbaglia.github.io/slides/example/","publishdate":"2019-02-05T00:00:00Z","relpermalink":"/slides/example/","section":"slides","summary":"An introduction to using Wowchemy's Slides feature.","tags":[],"title":"Slides","type":"slides"},{"authors":["Wilms","Barbaglia","Croux"],"categories":null,"content":"","date":1515970800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1515970800,"objectID":"ac88ae26ab9384f782eef38bde4ffe1b","permalink":"https://lucabarbaglia.github.io/publication/2018_jrssc_multiclassvar/","publishdate":"2018-01-15T00:00:00+01:00","relpermalink":"/publication/2018_jrssc_multiclassvar/","section":"publication","summary":"Retailers use the vector auto‐regressive (VAR) model as a standard tool to estimate the effects of prices, promotions and sales in one product category on the sales of another product category. Besides, these price, promotion and sales data are available not just for one store, but for a whole chain of stores. We propose to study cross‐category effects by using a multiclass VAR model: we jointly estimate cross‐category effects for several distinct but related VAR models, one for each store. Our methodology encourages effects to be similar across stores, while still allowing for small differences between stores to account for store heterogeneity. Moreover, our estimator is sparse: unimportant effects are estimated as exactly 0, which facilitates the interpretation of the results. A simulation study shows that the multiclass estimator proposed improves estimation accuracy by borrowing strength across classes. Finally, we provide three visual tools showing clustering of stores with similar cross‐category effects, networks of product categories and similarity matrices of shared cross‐category effects across stores.","tags":null,"title":"Multi-class vector autoregressive models for multi-store sales data","type":"publication"},{"authors":["Barbaglia","Wilms","Croux"],"categories":null,"content":"","date":1479164400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1479164400,"objectID":"b2caad83dec1d8e75c36b7cef7cc5a36","permalink":"https://lucabarbaglia.github.io/publication/2016_eneco_commodity_dynamics/","publishdate":"2016-11-15T00:00:00+01:00","relpermalink":"/publication/2016_eneco_commodity_dynamics/","section":"publication","summary":"The correct understanding of commodity price dynamics can bring relevant improvements in terms of policy formulation both for developing and developed countries. Agricultural, metal and energy commodity prices might depend on each other: although we expect few important effects among the total number of possible ones, some price effects among different commodities might still be substantial. Moreover, the increasing integration of the world economy suggests that these effects should be comparable for different markets. This paper introduces a sparse estimator of the Multi-class Vector AutoRegressive model to detect common price effects between a large number of commodities, for different markets or investment portfolios. In a first application, we consider agricultural, metal and energy commodities for three different markets. We show a large prevalence of effects involving metal commodities in the Chinese and Indian markets, and the existence of asymmetric price effects. In a second application, we analyze commodity prices for five different investment portfolios, and highlight the existence of important effects from energy to agricultural commodities. The relevance of biofuels is hereby confirmed. Overall, we find stronger similarities in commodity price effects among portfolios than among markets.","tags":null,"title":"Commodity dynamics: A sparse multi-class approach","type":"publication"},{"authors":null,"categories":null,"content":"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim. Donec condimentum, sem id dapibus fringilla, tellus enim condimentum arcu, nec volutpat est felis vel metus. Vestibulum sit amet erat at nulla eleifend gravida.\nNullam vel molestie justo. Curabitur vitae efficitur leo. In hac habitasse platea dictumst. Sed pulvinar mauris dui, eget varius purus congue ac. Nulla euismod, lorem vel elementum dapibus, nunc justo porta mi, sed tempus est est vel tellus. Nam et enim eleifend, laoreet sem sit amet, elementum sem. Morbi ut leo congue, maximus velit ut, finibus arcu. In et libero cursus, rutrum risus non, molestie leo. Nullam congue quam et volutpat malesuada. Sed risus tortor, pulvinar et dictum nec, sodales non mi. Phasellus lacinia commodo laoreet. Nam mollis, erat in feugiat consectetur, purus eros egestas tellus, in auctor urna odio at nibh. Mauris imperdiet nisi ac magna convallis, at rhoncus ligula cursus.\nCras aliquam rhoncus ipsum, in hendrerit nunc mattis vitae. Duis vitae efficitur metus, ac tempus leo. Cras nec fringilla lacus. Quisque sit amet risus at ipsum pharetra commodo. Sed aliquam mauris at consequat eleifend. Praesent porta, augue sed viverra bibendum, neque ante euismod ante, in vehicula justo lorem ac eros. Suspendisse augue libero, venenatis eget tincidunt ut, malesuada at lorem. Donec vitae bibendum arcu. Aenean maximus nulla non pretium iaculis. Quisque imperdiet, nulla in pulvinar aliquet, velit quam ultrices quam, sit amet fringilla leo sem vel nunc. Mauris in lacinia lacus.\nSuspendisse a tincidunt lacus. Curabitur at urna sagittis, dictum ante sit amet, euismod magna. Sed rutrum massa id tortor commodo, vitae elementum turpis tempus. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean purus turpis, venenatis a ullamcorper nec, tincidunt et massa. Integer posuere quam rutrum arcu vehicula imperdiet. Mauris ullamcorper quam vitae purus congue, quis euismod magna eleifend. Vestibulum semper vel augue eget tincidunt. Fusce eget justo sodales, dapibus odio eu, ultrices lorem. Duis condimentum lorem id eros commodo, in facilisis mauris scelerisque. Morbi sed auctor leo. Nullam volutpat a lacus quis pharetra. Nulla congue rutrum magna a ornare.\nAliquam in turpis accumsan, malesuada nibh ut, hendrerit justo. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Quisque sed erat nec justo posuere suscipit. Donec ut efficitur arcu, in malesuada neque. Nunc dignissim nisl massa, id vulputate nunc pretium nec. Quisque eget urna in risus suscipit ultricies. Pellentesque odio odio, tincidunt in eleifend sed, posuere a diam. Nam gravida nisl convallis semper elementum. Morbi vitae felis faucibus, vulputate orci placerat, aliquet nisi. Aliquam erat volutpat. Maecenas sagittis pulvinar purus, sed porta quam laoreet at.\n","date":1461715200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1461715200,"objectID":"e8f8d235e8e7f2efd912bfe865363fc3","permalink":"https://lucabarbaglia.github.io/project/example/","publishdate":"2016-04-27T00:00:00Z","relpermalink":"/project/example/","section":"project","summary":"An example of using the in-built project page.","tags":["Deep Learning"],"title":"Example Project","type":"project"},{"authors":null,"categories":null,"content":"","date":1461715200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1461715200,"objectID":"d1311ddf745551c9e117aa4bb7e28516","permalink":"https://lucabarbaglia.github.io/project/external-project/","publishdate":"2016-04-27T00:00:00Z","relpermalink":"/project/external-project/","section":"project","summary":"An example of linking directly to an external project website using `external_link`.","tags":["Demo"],"title":"External Project","type":"project"},{"authors":null,"categories":null,"content":"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim. Donec condimentum, sem id dapibus fringilla, tellus enim condimentum arcu, nec volutpat est felis vel metus. Vestibulum sit amet erat at nulla eleifend gravida.\nNullam vel molestie justo. Curabitur vitae efficitur leo. In hac habitasse platea dictumst. Sed pulvinar mauris dui, eget varius purus congue ac. Nulla euismod, lorem vel elementum dapibus, nunc justo porta mi, sed tempus est est vel tellus. Nam et enim eleifend, laoreet sem sit amet, elementum sem. Morbi ut leo congue, maximus velit ut, finibus arcu. In et libero cursus, rutrum risus non, molestie leo. Nullam congue quam et volutpat malesuada. Sed risus tortor, pulvinar et dictum nec, sodales non mi. Phasellus lacinia commodo laoreet. Nam mollis, erat in feugiat consectetur, purus eros egestas tellus, in auctor urna odio at nibh. Mauris imperdiet nisi ac magna convallis, at rhoncus ligula cursus.\nCras aliquam rhoncus ipsum, in hendrerit nunc mattis vitae. Duis vitae efficitur metus, ac tempus leo. Cras nec fringilla lacus. Quisque sit amet risus at ipsum pharetra commodo. Sed aliquam mauris at consequat eleifend. Praesent porta, augue sed viverra bibendum, neque ante euismod ante, in vehicula justo lorem ac eros. Suspendisse augue libero, venenatis eget tincidunt ut, malesuada at lorem. Donec vitae bibendum arcu. Aenean maximus nulla non pretium iaculis. Quisque imperdiet, nulla in pulvinar aliquet, velit quam ultrices quam, sit amet fringilla leo sem vel nunc. Mauris in lacinia lacus.\nSuspendisse a tincidunt lacus. Curabitur at urna sagittis, dictum ante sit amet, euismod magna. Sed rutrum massa id tortor commodo, vitae elementum turpis tempus. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean purus turpis, venenatis a ullamcorper nec, tincidunt et massa. Integer posuere quam rutrum arcu vehicula imperdiet. Mauris ullamcorper quam vitae purus congue, quis euismod magna eleifend. Vestibulum semper vel augue eget tincidunt. Fusce eget justo sodales, dapibus odio eu, ultrices lorem. Duis condimentum lorem id eros commodo, in facilisis mauris scelerisque. Morbi sed auctor leo. Nullam volutpat a lacus quis pharetra. Nulla congue rutrum magna a ornare.\nAliquam in turpis accumsan, malesuada nibh ut, hendrerit justo. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Quisque sed erat nec justo posuere suscipit. Donec ut efficitur arcu, in malesuada neque. Nunc dignissim nisl massa, id vulputate nunc pretium nec. Quisque eget urna in risus suscipit ultricies. Pellentesque odio odio, tincidunt in eleifend sed, posuere a diam. Nam gravida nisl convallis semper elementum. Morbi vitae felis faucibus, vulputate orci placerat, aliquet nisi. Aliquam erat volutpat. Maecenas sagittis pulvinar purus, sed porta quam laoreet at.\n","date":1461715200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1461715200,"objectID":"8f66d660a9a2edc2d08e68cc30f701f7","permalink":"https://lucabarbaglia.github.io/project/internal-project/","publishdate":"2016-04-27T00:00:00Z","relpermalink":"/project/internal-project/","section":"project","summary":"An example of using the in-built project page.","tags":["Deep Learning"],"title":"Internal Project","type":"project"}]