We compile all return and macroeconomic data from Kenneth French's website and the OECD statistical data warehouse, respectively, for the period from January 1990 to December 2018. All return and macroeconomic data include the following countries: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland and United Kingdom.The dataset comprises the following series:

1. Fama-French factors, 3-factor model, as provided by Kenneth French (Europe_3_Factors.txt).
2. Fama-French factors, 5-factor model, as provided by Kenneth French (Europe_5_Factors.txt).
3. Returns for 25 size-BE/ME portfolios, as provided by Kenneth French (Europe_25_Portfolios_ME_BE-ME.txt).
4. Returns for 25 size-momentum, as provided by Kenneth French (Europe_25_Portfolios_ME_Prior_12_2.txt).
5. Weighted average per capita consumption growth. We first collect quarterly chained volume estimates for consumption in nondurables and services, non-seasonally adjusted, in national currency, for the 16 countries under consideration (‘Non-durable goods’ and ‘Services’ series, LNBQR measure). Second, we use the population series provided by the OECD to determine per capita consumption growth series for each country. Finally, we estimate the average consumption growth for the economies under consideration, weighting by population (Europe_Consumption_Q.txt).
6. Weighted average consumer confidence index (CCI). We collect monthly CCI data as provided by the OECD (‘OECD Standardised CCI, Amplitude adjusted, sa’ series, dataset ‘Composite Leading Indicators’, MEI). We determine the average CCI for the economies under consideration, weighting by population (Europe_Indicators_Q.txt).

This is a dataset used for comparing different particle Markov chain Monte Carlo (PMCMC) methods, found in: Efficiently combining pseudo marginal and particle Gibbs sampling by D. Gunawan, C. Carter, and R. Kohn. This dataset contains de-meaned daily returns for 26 US industry portfolios, from 11 December 2001 to 11 November 2013 (a total of 3001 daily observations of the 26-dimensional vector of industry portfolios). The original dataset is obtained from the website of Kenneth French.

Our research is about robust analysis for high dimensional factor model in present of heavy-tailed data. We propose novel methods by integrating the modified Huber loss function and the common Principal Component Analysis. The methods are superior or comparable to others in numerical studies and the estimated factor number is more aligned with financial practice.

The real data in finance is from Kenneth R. French's website: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html. We use three portfolio pools: Pool A, Pool B, and Pool C to do factor analysis. Each pool contains 100 portfolios with complete monthly average value-weighted returns data from July 2016 to June 2024. The Portfolios in each pool are influenced by two primary factors. The authors have no permission to share the data or make the data public available.

We give the R codes for data generating, parameter setting and computational details in simulations.