You’re an economist looking to improve your forecasting. What tools should you be using?

When using data sources for forecasting economic events, it's important to prioritize reliability and relevance. Ensure that the data sources you use are reliable and regularly updated to maintain accuracy in your forecasts. Consider the specific economic event you are forecasting and use data sources that are relevant to that event, such as government statistics, financial market data, surveys, and econometric models. Additionally, leveraging multiple data sources can help provide a more comprehensive understanding of the economic landscape and reduce the potential for discrepancies. By using reliable and relevant data sources and triangulating results, economists can improve the accuracy of their economic forecasts.

As an economist aiming to enhance forecasting, you should consider utilizing various tools such as econometric models, economic base analysis, input-output models, and consensus forecasts. Additionally, techniques like regression analysis, time series analysis, and surveys of economic indicators can offer valuable insights. These tools enable economists to analyze historical data and economic variables, which are essential for making informed predictions about future economic conditions.

Para realizar previs?es econ?micas, é fundamental buscar dados em fontes confiáveis, aqui v?o algumas: Economia Geral e mercado Financeiro: IPEA Data Estatísticas do Banco central IBGE-SIDRA ANBIMA CVM Ministério da Fazenda B3 IBRE – FGV FIPE Febraban Invest S?o Paulo Setores econ?micos: Geralmente as associa??es de classes, agências governamentais e institutos de pesquisas possuem pesquisas e bancos de dados para consulta, como: ANFAVE (Automobilístico) IBEVAR, Fecomercio (Varejo e Comércio) ANEEL (Setor elétrico) ANP (Petróleo e gás) Abical?ados (Cal?ados) ABES, SEPROSP (Tecnologia da Informa??o) SECOVI, CBIC, Sinduscon, Abramat (Constru??o civil) SEBRAE (Negócios em Geral) CNA (Agropecuária) CNI (Indústria)

Collecting accurate historical data is essential to creating accurate forecasts. I rely heavily on several main sources, so I'll briefly address each of them. 1. BLS employment situation The Bureau of Labor Statistics releases a report called the 'employment situation' every month, and it contains some incredibly valuable information about employment and labor stats. 2. Case-Shiller HPI I use the Corelogic Case-Shiller Home Price Indices to analyze trends in the housing market. It has MTD, QTD, YTD, and 1, 3, 5, and 10 year views. 3. National Association of Realtors NAR provides an excellent report on the real estate industry. I frequently use it for quick analyses of mortgage rates, sales, and more. 4. I use FRED for most everything else!

Utilize a variety of data sources to capture a comprehensive view of economic indicators. This may include government reports, financial market data, consumer surveys, and industry-specific data. Diversifying data sources helps in constructing robust models that consider multiple factors influencing the economy. Incorporate real-time or near-real-time data sources to enhance the timeliness and accuracy of forecasts. This can include social media trends, web analytics, and other sources that provide up-to-the-minute insights into economic activities.

Use packages to carry out analysis of Econometric Models. These models use historical data and economic relationships to predict future economic outcomes. Popular models include VARs, ARIMAs, and structural models.

Common packages used include: Excel: - Widely used in industry but lacks specialised econometric and statistical tools. Stata: - Specialised for econometrics. - Robust statistical functions. - Popular in academia and research. R: - Open-source with extensive statistical libraries. - Widely used in academia and research. Eviews: - Specialized for time series analysis. - User-friendly with comprehensive econometric tools. Python: - General-purpose with powerful libraries like Pandas and Statsmodels. - Highly flexible and customisable.

-Statistical Software: Familiarize yourself with statistical software packages like R or Python with libraries such as pandas, NumPy, and statsmodels. These tools enable economists to perform sophisticated data analysis, build predictive models, and visualize results. -Machine Learning Tools: Explore machine learning frameworks such as scikit-learn or TensorFlow. Machine learning algorithms can uncover complex patterns in data, improving the accuracy of economic forecasts. -Visualization Tools: Utilize visualization tools like Tableau or Power BI to create compelling visual representations of economic trends. Clear visualizations enhance communication and interpretation of forecasting results.

A día de hoy es difícil no encontrar un software que se ajuste a nuestras necesidades. Los softwares que procesan modelos econométricos deben ser flexibles, permitir la automatización de la captura de datos (variables como PIB, desempleo, población, etc.). Es muy importante que el programa, una vez seleccionadas las variables y se analice el comportamiento, permita el cálculo de la dependencia entre variables y permita conocer la bondad del ajuste. Debe tener la capacidad de representar gráficamente las variables y resultados en diversos formatos de cara a tener que presentarlos en un informe. Es importante que se puedan programar diversos escenarios de evolución para comprobar las opciones futuras en diferentes supuestos.

Aqui est?o algumas indica??es de cursos para aprender a realizar previs?es utilizando o poder dos dados: Canais do Youtube: Prof. Alexendre C. Costa - Análise de Séries Temporais Docsity Português - Econometria Programa??o Dinamica (Kizzy e Hallison) – ciência de dados Trading com Dados, Código Quant, Outspoken Market – Python e Finan?as Quantitativas Cursos: Escola Análise Macro - Data science para econometria, finan?as quantitativas Professor Fernando Amaral (Udemy) – Análise de séries temporais com Python Data Science Academy - Engenharia Financeira com Inteligência Artificial Coursera - Econometria Básica Aplicada da USP FIPE - Econometria Aplicada: Séries Temporais Usando R

- Data Science and Machine Learning Courses: Enroll in online courses focused on data science and machine learning. Platforms like Coursera, edX, and Udacity offer courses covering topics such as predictive modeling, time series analysis, and machine learning algorithms relevant to forecasting. - Economic Forecasting Courses: Explore specialized courses in economic forecasting to deepen your understanding of economic models, time series analysis, and forecasting techniques specific to economic indicators.

Having a solid foundation in theory and stats is important before jumping into forecasting and using the latest tools. I don't know how many time series regressions have been handed over to me in my career, where the R squared was pointed to as evidence of the models importance in forecasting-- the data left in levels with clear trends. Did you test for stationarity? What about cointegration? What about dividing the sample into a training and test set? This is what I've learned about forecasting: 1. Fundamentals are important 2. So is humility to admit what we don't know 3. Always keep learning Consider the basis for your forecasts and bridge gaps in your understanding of both theory and practice. Substance over style always wins.

Strong foundation in statistics is equally important as basic economics concepts, and being able to apply the two into practice I would recommend be familiar with at least one of the programming language. For better application in Economics forecasting R/Stata/Matlab are preferred, while Python is convenient in commercial environment because of the ease of connectivity to other systems.

Additional considerations: - Interdisciplinary Collaboration: Foster collaboration with experts from other disciplines, such as data science and computer science. Interdisciplinary teams can bring diverse perspectives and skills to the forecasting process. - Continuous Learning: Stay updated on advancements in forecasting methodologies and tools. Attend conferences, participate in webinars, and engage with industry experts to remain at the forefront of forecasting practices. - Validation and Feedback: Regularly validate your forecasting models against actual outcomes and seek feedback from peers. This iterative process helps refine models and improve forecasting accuracy over time.

El análisis económico tiene una base muy importante de matemáticas, teoría económica y métodos cuantitativos (como la econometría). Pero es muy importante saber definir correctamente el modelo. Con esto quiero decir que el mejor y más potente software no tendrá utilidad si no hay "alguien a los mandos", si se me permite la expresión. También es importante saber qué pedirle al programa, que dependerá de lo que estemos analizando. Y la interpretación de los resultados es algo que también depende del analista. En resumen, el software es una herramienta poderosa que resuelve cálculos y representaciones gráficas, pero es muy importante que tengamos una idea muy clara de lo que necesitamos analizar y cómo queremos hacerlo.

When it comes to forecasting, the 'human factor' should not be underestimated. So the ability to look through and beyond data and consider the political and geopolitical situation as well as the institutional and regulatory environment is crucial. Therefore, an interdisciplinary approach and diverse skills are highly recommended.