In fact, I find the latter to be increasingly helpful as a complement to formal statistical analysis. The challenge is visualising complex datasets that have many dimensions. Since at best we can only look across two or three dimensions of a dataset at once, we have to be careful about the way we choose to ‘cut’ the data. Excel pivot tables and pivot charts are a pretty good way of doing this, although the presentation of the data in Excel is not as clean as it could be.

I also find it very helpful to think about the possible relationships that could exist in a dataset before diving in to the analysis. This does not necessarily involve coming up with formal hypotheses to test, but rather just thinking about what are the sensible correlations or patterns to look for.

Basic data problems that need to be dealt with before analysis include:

• Missing or incomplete data
• Inconsistent definitions across datasets
• Different frequencies (e.g. annual, monthly, quarterly) used in different datasets

Data wrangling is the process of sorting out these types of problems to produce a nice clean dataset. The objective is usually a flat database-style dataset, with columns for data descriptors and then the data itself. Missing data often needs to be imputed or otherwise estimated. It is often necessary to come up with creative ways to adjust the data to account for inconsistencies in definitions across datasets. In fact, inconsistencies can arise within the same data series, for example when the definition of a time-series has changed at some point. In such a case, some kind of back-casting can often be used to produce a series that is consistent over time.

I use Excel most often for data wrangling. Useful tools include pivot tables, the database commands like DSUM, and conditional commands like SUMIF or the newer SUMIFS. I also sometimes write custom macros that re-shape data into the format that I want, if there is too much data for manual editing. However, often the process of macro-writing and debugging can take longer than just doing the data edits manually.

We examine the effects of informational intermediation by a third party ‘infomediary’ in a search model with heterogeneous seller qualities and asymmetric information. The infomediary earns revenue by selling a list of accredited sellers (a ‘guidebook’) to buyers and selling accreditations to sellers. We show that such information always improves welfare but can hurt informed buyers (as well as the uninformed) if it is not sufficiently informative about seller qualities. The model also provides a number of insights concerning the private and social incentives to create information, and the determinants and nature of demand for infomediation services by buyers and sellers.

This paper investigates conditions that generate the so-called “waterbed” effect under price regulation. This is the effect whereby regulation of one price of a multiproduct firm causes one or more of its unregulated prices to change as a result of the firm’s profit-maximizing behavior. A waterbed effect is shown to arise when demands and/or marginal costs are interdependent, firms use nonlinear pricing, or there is a zero-profit constraint or global price cap. Some implications for market definition, welfare analysis of regulation, non-price competition, collusion and two-sided markets are also discussed, as well as specific applications to fixed-to-mobile termination and bank overcharges.

This paper reviews and compares patent pools, intellectual property (IP) clearinghouses, and copyright collectives as systems for promoting efficient access to licensable IP in a ‘market for technology’. These systems promote downstream use of innovations by economizing on search and transaction costs in licensing, as well as potentially mitigating the conditions that lead to the ‘tragedy of the anti-commons’ and other coordination problems in multilateral licensing. We compare and classify different systems in terms of their features, review some existing systems, and discuss their economic characteristics.

We develop a model of simple “reputation systems” that monitor and publish information about the behavior of sellers in a search market with asymmetric information. The reputations created influence the equilibrium search patterns of buyers and thus provide for market-based “punishment” of bad behavior. We show that a reputation system that rewards honesty can enhance welfare by allowing good sellers to truthfully signal their type. However, the same system can be prone to strategic manipulation by sellers who always have low-quality products. In that case, we show that an alternative system which assigns reputations based on product quality rather than honesty can be superior.

Firms with two-sided networks facilitate connections or transactions between two distinct populations of consumers. This paper analyzes the behavior of such firms where there are no intrinsic benefits to consumers other than the network effects, such as employment agencies, real estate agents and videogame platforms. The modelling framework encompasses both matching service and platform business models and allows for subscription or per-transaction pricing. Three different market structures are considered: monopoly, and duopoly with and without compatibility. Comparisons of prices, profits, consumer surplus, and welfare are made between the three regimes. It is shown that duopoly with compatibility is socially preferable to the other regimes, while monopoly is socially preferable to duopoly without compatibility.

This paper reviews the recent literature on the economics of open source software. Two different sets of issues are addressed. The first looks at the incentives of programmers to participate in open source projects. The second considers the business models used by profit-making firms in the open source industry, and the effects on existing closed source firms. Some possible future research directions are also given.

This paper incorporates partial consumer participation in a model of competition between telecommunications networks with two-way interconnection. It is shown, in contrast to the results of similar models with full participation, that the firms’ equilibrium profits depend on the level of a reciprocal access charge under two-part retail pricing. Under some simplifying assumptions, it is shown that firms prefer the access charge be set equal to the marginal cost of termination, which coincides with the social optimum. Without these additional assumptions the model is analytically complex and simulation results are presented that suggest firms prefer the access charge to be less than marginal cost, while the socially optimal access charge may be above or below cost depending on the differentiation of the firms.

Recent time series methods are applied to the problem of forecasting New Zealand’s real GDP. Model selection is conducted within autoregressive (AR) and vector autoregressive (VAR) classes, allowing for evolution in the form of the models over time. The selections are performed using the Schwarz (1978) BIC and the Phillips-Ploberger (1996) PIC criteria. The forecasts generated by the data-determined AR models and an international VAR model are found to be competitive with forecasts from fixed format models and forecasts produced by the NZIER. Two illustrations of the methodology in conditional forecasting settings are performed with the VAR models. The first provides conditional predictions of New Zealand’s real GDP when there is a future recession in the United States. The second gives conditional predictions of New Zealand’s real GDP under a variety of profiles that allow for tightening in monetary conditions by the Reserve Bank.