I have got a database with balance sheet and income statement data of 150.000 firms for the period 1995-2014. I need to get a good forecast of each firm's sales. As exogenous variables I can use the industrial production index of the relative industrial sector and the lagged other balance sheet variables. I would be grateful if you could suggest me the best methodology to use. I was thinking to try ARIMA, ARIMAX and exponential smoothing.
[Sorry, I'm new here and accidently posted this as an answer and its just meant as a comment responding to a question, but it does not let me delete answers to put it under comments. If I last long enough, I'm sure I'll figure out how to edit things.]
PCA is an eigenvalue/eigenvector decomposition of the data frequently applied in risk management to look for systemic factors effecting a large portfolio. My favorite introduction to the concept is an excellent efficient and very focused chapter in Carol Alexander's Market Risk Analysis vol 2 (and the volume number is critical since there are 4 books). This can give you factors that explain the majority of the variability in your series and it reduces your problem from 150,000 series down to a few factors. There are lots of articles on this if you search. Alternatively, and less techy, you could create your own capital weighted indexes and use those to predict the 150,000 individual series, using betas estimated off that index for each firm. Your IP index could influence these indexes.