​Department of Economics Management and Statistics

Codes and database originated for the manuscript published in the DOI article: 10.1016/j.jdeveco.2023.103053

Datasets and codes associate wih the paper "Interpreting the oil risk premium: do oil price shocks matter?". Detailed description of the data is contained in the .zip file

Data for "Convergence of European natural gas prices"

time series of selected macroeconomic variables used as observables in our estimation exercise.

Stata data set

This dataset contains the data from the a set of laboratory experiments with human subjects run at the University of Milano Bicocca. The experiment was programmed using zTree (Fischbacher, 2007). Our experimental design involved four sessions of each of our two treatments. As we have 27 subjects per session, we have collected data from a total of 216 subjects, from October 2014 to January 2015. Subjects were recruited from the undergraduate population of the University of Milano-Bicocca, via the ORSEE software (Greiner, 2004). No subject participated in more than one session of this experiment. In December 2015 we have run two additional sessions per treatment where the order of the games played by the subject was reversed.

The above are two of the main codes written in RATS which are used to carry empirical estimates and bootstrapping analysis of the baseline regression (Table 3) used in our study.

Abstract of associated article: According to the Rockets and Feathers Hypothesis (RFH), the transmission mechanism of positive and negative changes in the price of crude oil to the price of gasoline is asymmetric. Although there have been many contributions documenting that downstream prices are more reactive to increases than to decreases in upstream prices, little is known about the forecasting performance of econometric models incorporating asymmetric price transmission from crude oil to gasoline. In this paper we fill this gap by comparing point, sign and probability forecasts from a variety of Asymmetric-ECM (A-ECM) and Threshold Autoregressive ECM (TAR-ECM) specifications against a standard ECM. Forecasts from A-ECM and TAR-ECM subsume the RFH, while the ECM implies symmetric price transmission from crude oil to gasoline. We quantify the forecast accuracy gains due to incorporating the RFH in predictive models for the prices of gasoline and diesel. We show that, as far as point forecasts are involved, the RFH does not lead to significant improvements, while it can be exploited to produce more accurate sign and probability forecasts. Finally, we highlight that the forecasting performance of the estimated models is time-varying.

This folder contains the data and the R code to reproduce the figures and tables in the paper Zito, Rigon and Dunson (2022) - "Inferring Taxonomic placement from DNA barcoding aiding in discovery of new taxa", accepted as open access publication in Methods in Ecology and Evolution. The file "main_FinBOL.R" reproduces the tables in the main document and in the Supporting information available online for the analysis of the FinBOL data, while "main_Simulation_Section4_SI.R" reproduces the simulation in Section 4 of the Supporting information. All data are saved in the folder "data". For replicability purposes, we added version 2.13 of the RDP classifier to the repository, in the folder "RDP/java". This has been downloaded from https://sourceforge.net/projects/rdp-classifier/. For questions, contact the author at alessandro.zito@duke.edu

The AgrImOnIA dataset is a comprehensive dataset relating air quality and livestock (expressed as the density of bovines and swine bred) along with weather and other variables. The AgrImOnIA Dataset represents the first step of the AgrImOnIA project. The purpose of this data set is to give the opportunity to assess the impact of agriculture on air quality in Lombardy through statistical techniques capable of highlighting the relationship between the livestock sector and air pollutants concentrations. This dataset is a collection of estimated daily values for a range of measurements of different dimensions as: air quality, meteorology, emissions, livestock animals and land use. Data are related to Lombardy and the surrounding area for 2016-2021, inclusive. The surrounding area is obtained by applying a 0.3° buffer on Lombardy borders. The data uses several aggregation and interpolation methods to estimate the measurement for all days. For more details see the paper: A. Fassò, J. Rodeschini, A. Fusta Moro, Q. Shaboviq, P. Maranzano, M. Cameletti, F. Finazzi, N. Golini, R. Ignaccolo, and P. Otto (2022) Agrimonia: a dataset on livestock, meteorology and air quality in the Lombardy region, Italy. Arxiv preprint, arxiv:2210.10604. (click here). The files in the folder are: Agrimonia_Dataset.csv(.Rdata,.mat) which is built by joining the daily time series related to the AQ, WE, EM, LI and LA variables. In order to simplify access to variables in the Agrimonia dataset, the variable name starts with the dimension of the variable, i.e., the name of the variables related to the AQ dimension start with 'AQ_'. This file is archived also in the and format for MATLAB and R software, respectively. Metadata_Agrimonia.csv which provides further information for the sources used, variables imported, transformations applied, and about the Agrimonia variables. Metadata_AQ_imputation_uncertainty.csv which contains the daily uncertainty estimate of the imputed observation for the AQ to mitigate missing data in the hourly time series. Metadata_LA_CORINE_labels.csv which contains the label and the description associated with the CLC class. Metadata_monitoring_network_registry.csv which contains all details about the AQ monitoring station used to build the dataset. Information about pollutant stations includes: station type, municipality code, environment type, altitude, pollutants sampled and other information. Each row represents a single sensor. Metadata_LA_SIARL_labels.csv which contains the label and the description associated with the SIARL class. The dataset can be reproduced using the code available at the GitHub page: https://github.com/AgrImOnIA-project/AgrImOnIA_Data