Investment forecasting with business survey data

Business investment is a very important variable for short- and medium-term economic analysis, but it is volatile and difficult to predict. Qualitative business survey data are widely used to provide indicators of economic activity ahead of the publication of official data. Traditional indicators exploit only aggregate survey information, namely the proportions of respondents who report “up” and “down”. As a consequence, neither the heterogeneity of individual responses nor the panel dimension of microdata is used. We illustrate the use of a disaggregate panel-based indicator that exploits all information coming from two yearly industrial surveys carried out on the same sample of Italian manufacturing firms. Using the same sample allows us to match exactly investment plans and investment realisations for each firm and so estimate a panel data model linking individual investment realisations to investment intentions. The model generates a one-year-ahead forecast of investment variation that follows the aggregate dynamics with a limited bias.investment plans, dynamic panel data model, forecasting

Remote processing of firm microdata at the Bank of Italy

Providing the possibility to run personalised econometric/statistical analyses on the appropriate data sets by remote processing allows greater flexibility in the production of economic information. Binding confidentiality requirements are required with business survey data. The Bank of Italy's infrastructure allows its business survey data to be exploited, while preserving anonymity of individual data. The system is based on the LISSY platform and has been already adopted by the Luxembourg Income Study (LIS) and other research centres. Firms' privacy is safeguarded by forbidding potentially confidentiality-breaking programme statements and by denying the visualisation of individual data. Data confidentiality is protected by removing key identifiers from the database and by trimming data in the right tail of the distribution. The platform provides its services through plain-text e-mails. The authorised user sends an e-mail containing an identifying header followed by a statistical programme to a predetermined address. The system checks the validity of the header, strips out the code and submits it in a batch to one of the econometric/statistical packages available (SAS and Stata). The outputs are mailed back to the user after passing an array of automatic and manual checks.microdata, confidentiality, remote access

A neural network architecture for data editing in the Bank of ItalyÂ’s business surveys

This paper presents an application of neural network models to predictive classification for data quality control. Our aim is to identify data affected by measurement error in the Bank of ItalyÂ’s business surveys. We build an architecture consisting of three feed-forward networks for variables related to employment, sales and investment respectively: the networks are trained on input matrices extracted from the error-free final survey database for the 2003 wave, and subjected to stochastic transformations reproducing known error patterns. A binary indicator of unit perturbation is used as the output variable. The networks are trained with the Resilient Propagation learning algorithm. On the training and validation sets, correct predictions occur in about 90 per cent of the records for employment, 94 per cent for sales, and 75 per cent for investment. On independent test sets, the respective quotas average 92, 80 and 70 per cent. On our data, neural networks perform much better as classifiers than logistic regression, one of the most popular competing methods, on our data. They appear to provide a valid means of improving the efficiency of the quality control process and, ultimately, the reliability of survey data.data quality, data editing, binary classification, neural networks, measurement error

Family firms, soft information and bank lending in a financial crisis

This paper studies how access to bank lending differed between family and non-family firms in the 2007-2009 financial crisis. The theoretical prediction is that family block-holders' incentive structure results in lower agency conflict in the borrower-lender relationship. Using highly detailed data on bank-firm relations, we exploit the reduction in bank lending in Italy following the crisis in October 2008. We find statistically and economically significant evidence that the contraction in credit for family firms was smaller than that for non-family firms. Results are robust to ex-ante observable differences between the two types of firms and to time-varying bank fixed effects. We further show that the difference in the amount of credit granted to family and non-family firms is related to an increased role for soft information in Italian banks' operations, following the Lehman Brothers' failure. Finally, by identifying a match between those banks and family firms, we can control for time-varying unobserved heterogeneity among the firms and validate the hypothesis that our results are supply driven

The effectiveness of investment subsidies: evidence from survey data

This paper investigates the effects of subsidies on the investment decisions of a sample of Italian manufacturing firms. We use survey information on firmsÂ’ subjective evaluations of the investment they would have undertaken without financing, finding that subsidies have limited effectiveness as a stimulus. Without subsidies, three-quarters of the firms financed would have made the same amount of investment at the same date; most of the remaining firms would have made the same amount of investment at a future date.investment incentives, survey data