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The interaction of macroeconomic variables may change as nominal short-term interest rates approach zero. In this paper, we propose to capture these changing dynamics with a state-switching parameter model which explicitly takes into account that the interest rate might be constrained near the zero lower bound by using a Tobit model. The probability of state transitions is affected by the lagged level of the interest rate. The endogenous specification of the state indicator permits dynamic conditional forecasts of the state and the system variables. We use Bayesian methods to estimate the model and to derive the forecast densities. In an application to Swiss data, we evaluate state-dependent impulse-responses to a risk premium shock identified with sign-restrictions. We provide an estimate of the latent rate, i.e. the rate lower than the constraint on the interest rate level which would be state- and model-consistent. Additionally, we discuss scenario-based forecasts and evaluate the probability of exiting the ZLB region. In terms of log predictive scores and the Bayesian information criterion, the model outperforms a model substituting switching with stochastic volatility and another including intercept switching only combined with stochastic volatility.

A new class of integer time series models is proposed to capture the dynamic transmission of count processes over time. The approach extends existing integer mixed autoregressive-moving average models (INARMA) by allowing for shifts in the dynamics of the count process through regime changes, referred to as a threshold integer autoregressive-moving average model (TINARMA). An efficient method of moments estimator is proposed, with standard errors based on subsampling, as maximum likelihood methods are infeasible for TINARMA processes. Applying the framework to global banking crises over 200 years of data, the empirical results show strong evidence of autoregressive and moving average dynamics which vary across systemic and nonsystemic regimes over time. Coherent forecast distributions are also produced with special attention given to the Great Depression and the more recent Global Financial Crisis.

This paper examines the impact of time averaging and interval sampling data assuming that the data generating process for a given series follows a random walk with iid errors. We provide exact expressions for the corresponding variances, and covariances, for both levels and higher order differences of the aggregated series, as well as that for the variance ratio, demonstrating exactly how the degree of temporal aggregation impacts these properties. We empirically investigate this issue on exchange rates and find that the values of the variance ratios and autocorrelation coefficients at different frequencies are consistent with our theoretical results. We also conduct a simulation exercise that illustrates the potential effect that conditional heteroskedasticity and fat tails may have on the temporal aggregation of a random walk and of a highly persistent autoregressive process.

Interdependencies among neighboring regions appear to be important in forming the shape of local labor markets. Nevertheless, only a few studies exist which have applied spatial models to forecast over small spatial units such as cities, districts or counties. The majority of predictions are developed with quarterly or yearly time series for a country or at regional level. The paper presents the above phenomena and deals with the problem of simultaneous forecasting of the unemployment rate over 35 poviats (districts and cities) in one of the Polish provinces. Two extremely different models with spatial dependencies were developed and estimated in this paper: the Spatial Vector Autoregressions (SpVAR) and the Spatial Artificial Neural Network (SpANN). The 13-month out-of-sample forecast is based on high frequency, raw, monthly panel data extracted from 31 local labor offices. The procedure worked out here allows comparing the forecasting performance of spatial models with their non-spatial and seasonal equivalents. The inclusion of a spatial component into the models significantly improves the accuracy of forecasts; however, the overall performance of SpVAR is 30% better than SpANN.

Within the context of a financial accelerator model, we model time-varying uncertainty (i.e. risk shocks) through the use of a mixture normal model with time variation in the weights applied to the underlying distributions characterizing entrepreneur productivity. Specifically, we model capital producers (i.e. the entrepreneurs) as either low-risk (a relatively small second moment of productivity) or high-risk (a relatively large second moment of productivity) and the fraction of both types is time-varying. We show that this modeling feature implies that the aggregate distribution of productivity shocks is non-normal and has time varying kurtosis and skewness; both of these features have important effects on equilibrium characteristics. In particular, after estimating the steady-state share and the change in the fraction of risky entrepreneurs, we show that a small change in the fraction of risky types can result in a large quantitative effect of a risk shock relative to standard models for both financial and real variables. Moreover, the bankruptcy rate and the risk premium in the economy are very sensitive to a change in the composition of entrepreneurs.

The rise in US partisan conflict following the Great Recession led to a popular belief that uncertainty about fiscal policy was impeding output growth. I explore this hypothesis by nesting it in a standard structural vector autoregression (SVAR) model traditionally used for estimating fiscal multipliers. I augment the model with stochastic volatility (a measure of uncertainty) and allow that to interact with the endogenous variables. I consider various trend assumptions, subsamples and information sets and find that the evidence does not support this hypothesis. The results reveal that there is no systematic relationship between fiscal policy uncertainty and output. Moreover, a time-varying parameter version of the model shows that the lack of consistency across specifications is not driven by changes in the transmission of uncertainty shocks over time. Finally, I revisit Fernández-Villaverde, Guerrón-Quintana, Kuester, and Rubio-Ramírez (Fernández-Villaverde, J., P. Guerrón-Quintana, K. Kuester, and J. Rubio-Ramírez. 2015. “Fiscal Volatility Shocks and Economic Activity.” American Economic Review 105: 3352–3384) who find a significant negative relationship between fiscal policy uncertainty and output. I show that when their estimation is modified to incorporate the uncertainty around the estimate of uncertainty, their empirical result falls in line with the findings in this paper.

The paper analyses from a disequilibrium perspective the role of banks’ “animal spirits” and collective behaviour in the creation of credit that, ultimately, determines the credit cycle. In particular, we propose a dynamic model to analyse how the transmission of waves of optimism and pessimism in the supply side of the credit market interacts with the business cycle. We adopt the Weidlich-Haag-Lux approach to model the opinion contagion of bankers. We test different assumptions on banks’ behaviour and find that opinion contagion and herding amongst banks play an important role in propagating the credit cycle and destabilizing the real economy. The boom phases trigger banks’ optimism that collectively lead the banks to lend excessively, thus reinforcing the credit bubble. Eventually the bubbles collapse due to an over-accumulation of debt, leading to a restrictive phase in the credit cycle.