Publications

Often, variables are linked to each other via a network. When such a network structure is known, this knowledge can be incorporated into regularized regression settings. In particular, an additional network penalty can be added on top of another penalty term, such as a Lasso penalty. However, when the type of interaction via the network is unknown (that is, whether connections are of an activating or a repressing type), the connection signs have to be estimated simultaneously with the covariate coefficients. This can be done with an algorithm iterating a connection sign estimation step and a covariate coefficient estimation step. We show detailed simulation results of such an algorithm. The algorithm performs well in a variety of settings. We also briefly describe the R-package that we developed for this purpose, which is publicly available.

The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.

Currency unions are often modeled as homogeneous economies, although they are fundamentally different. The expectations that impact macroeconomic behavior in any given country are not the expectations of variables at the currency-union level but at the country level. We model these expectations with a behavioral reinforcement learning model. In our model, economic integration is of particular importance in determining whether economic behavior in a currency union is stable. Monetary policy alone is insufficient to guarantee stable economic behavior, as the central bank cannot conduct different monetary policies in different countries. These results are easily overlooked when modeling expectations as rational.

JEL Codes: E03, F45, E52, D84.

The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.

Expectations play a crucial role in modern macroeconomic models. We consider a New Keynesian framework under rational expectations and under a behavioral model of expectation formation. We show how the economy behaves in the alternative scenarios with a focus on inflation volatility. Contrary to the rational model, the behavioral model predicts that inflation volatility can be lowered if the central bank reacts to the output gap in addition to inflation. We test the opposing theoretical predictions in a learning-to-forecast experiment. The results support the behavioral model and the claim that output stabilization can lead to less volatile inflation.

JEL Codes: C90, E03, E52, D84.

The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.

Each time researchers jointly write an article, a decision must be made about the order in which the authors are listed. There are two main norms for doing so. The vast majority of scientific disciplines use a contribution-based norm according to which authors who contributed the most are listed first. Very few disciplines, most notably economics, instead resort primarily to the norm of listing authors in alphabetical order. It has been argued that (i) this alphabetical norm gives an unfair advantage to researchers with last names starting with a letter early in the alphabet and that (ii) researchers are aware of this “alphabetical discrimination” and react strategically to it, for example through avoiding collaborations with multiple others. This article surveys the empirical literature on these two related topics. Overall, there is convincing evidence that alphabetical discrimination exists and that researchers react to it.

The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.

The experimental literature on asset markets has provided many useful insights on the efficiency of market mechanisms. It is unclear whether these results carry over to bonds markets, however. An important feature of bond markets is the relationship between initial public offering (IPO) prices and the probability that the bond issuer will default. First, this probability affects the value of the bond and, therefore, the bond prices. Second, prices paid for the bonds in the IPO determine the bond issuer’s financing costs and, therefore, affect the probability that the bond issuer defaults. We develop a flexible bond market model that accounts for this two-way interaction and that is easily implemented in the laboratory. We examine how subjects price bonds in this setting and find that subjects learn to price bonds rather well after only a few repetitions (both during the IPO and while trading in the secondary market afterward). Bubbles in bond prices are only observed among inexperienced traders. The overall high degree of market efficiency that we find occurs in environments with both increasing and decreasing (equilibrium) fundamental values for bonds.

JEL Codes: C92, C90, D47, G12.

The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.