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This paper studies the relationship between bank characteristics, such as size, nationality, operating currency and sovereign debt in the parent country, and the distribution of funding spreads observed in the e-MID interbank money market during the Great financial crisis. Our setup is a pseudo-panel with a random number of international banks acting in the interbank market in each period. We develop new econometric tools for panel data with random effects and discrete covariates, such as a nonparametric kernel estimator of the distribution function of the response variable conditional on a set of covariates and a consistent test of first order stochastic dominance. Our empirical results, based on these tests, shed light on the survivorship bias in the e-Mid market, and reveal the existence of a risk premium on small banks, banks with currencies different from the Euro, and banks based on countries under sovereign debt distress in the periphery of the European Union. Finally we assess the impact of policy intervention in the aftermath of the financial crisis.

We investigate the effects of government spending on US output with a threshold structural vector autoregressive model. We consider Bayesian model comparison and generalized impulse response analysis to test for nonlinearities in the responses of output to government spending. Our empirical findings support state-dependent effects of fiscal policy, with the government spending multiplier larger and more persistent whenever there is considerable economic slack. Based on capacity utilization as the preferred threshold variable, the estimated multiplier is large (1.6) for a low-utilization regime that accounts for more than half of the sample observations from 1967 to 2012 according to the estimated threshold level.

Panel conditional logit estimators (PCLE) in the literature use mostly time-constant parameters. If the panel periods are volatile or long, however, the model parameters can change much. Hence this paper generalizes PCLE with time-constant parameters to PCLE with time-varying parameters; both static and dynamic PCLE are considered for this. The main finding is that time-varying parameters are fully allowed for static PCLE and the dynamic “pseudo” PCLE of [Bartolucci, F. and V. Nigro. 2010. “A Dynamic Model for Binary Panel Data with Unobserved Heterogeneity Admitting a n$\sqrt n$-Consistent Conditional Estimator.” Econometrica 78: 719–733] that are thus recommended to practitioners. As a further generalization, static “panel conditional multinomial logit estimator (PML)” with time-varying parameters is also examined. As it turns out, time-varying parameters are also fully allowed for PML. With no error term serial correlation allowed in PCLE and dynamic PCLE’s being restrictive in their assumptions, time-varying parameters provide an alternative avenue to inject dynamics and flexibility into PCLE and PML. Since PCLE and PML converge straightforwardly in computation, allowing time-varying parameters in PCLE and PML is “computationally free.” A simulation study is also provided.

This article introduces the concept of co-nonlinearity . Co-nonlinearity is an example of a common feature in time series [Engle, Robert F., and Sharon Kozicki. 1993. “Testing for Common Features.” Journal of Business & Economic Statistics 11 (4): 369–380] and an extension of the concept of common nonlinear components [Anderson, Heather M., and Farshid Vahid. 1998. “Testing Multiple Equation Systems for Common Nonlinear Components.” Journal of Econometrics 84 (1): 1–36]. If some time series follow a nonlinear process but where a linear relationship between the levels of these series removes the nonlinearity, such a relationship is defined as co-nonlinear. In this article I show how to determine the number of such co-nonlinear relationships. Furthermore, I show how to formulate hypothesis tests on the co-nonlinear relationships in a full maximum likelihood framework. The framework for identifying co-nonlinear relationships is illustrated in a system of Norwegian interest rates.

In this paper we analyze the stability of the money demand system in the US. To this aim, we develop an estimation and testing framework for a threshold vector error-correction model (VECM), where short-run dynamics are regime dependent and are driven by an exogenous, stationary and ergodic threshold variable. We modify a traditional Wald-type test for linearity and derive its asymptotic distribution, which turns out to be non-standard, but similar to the one proposed by Andrews [Andrews, D. 1993. “Tests for Parameter Instability and Structural Change with Unknown Change Point.” Econometrica 61 (4): 821–856]. Accounting for the diversity of the potential determinants of money holdings, a broad scope of money demand systems is analyzed. The linearity test confirms that money demand is not only suffering from structural breaks, but is driven by regimes governed by business cycles. Moreover, a similar pattern and a robust dating of the regimes is observed no matter the money demand system considered, offering hence a reconciling picture of previous empirical studies. From a policy perspective, our findings supports the implementation of cyclical monetary policy by the Federal Reserve.

We examine the short-term performance of two alternative approaches of forecasting from dynamic factor models. The first approach extracts the seasonal component of the individual variables before estimating the model, while the alternative uses the non seasonally adjusted data in a model that endogenously accounts for seasonal adjustment. Our Monte Carlo analysis reveals that the performance of the former is always comparable to or even better than that of the latter in all the simulated scenarios. Our results have important implications for the factor models literature because they show the that the common practice of using seasonally adjusted data in this type of models is very accurate in terms of forecasting ability. Using five coincident indicators, we illustrate this result for US data.