Predicting the term structure of interest rates incorporating parameter uncertainty, model uncertainty and macroeconomic information

We forecast the term structure of U.S. Treasury zero-coupon bond yields by analyzing a range of models that have been used in the literature. We assess the relevance of parameter uncertainty by examining the added value of using Bayesian inference compared to frequentist estimation techniques, and model uncertainty by combining forecasts from individual models. Following current literature we also investigate the benefits of incorporating macroeconomic information in yield curve models. Our results show that adding macroeconomic factors is very beneficial for improving the out-of-sample forecasting performance of individual models. Despite this, the predictive accuracy of models varies over time considerably, irrespective of using the Bayesian or frequentist approach. We show that mitigating model uncertainty by combining forecasts leads to substantial gains in forecasting performance, especially when applying Bayesian model averaging

Panel Smooth Transition Regression Models

We develop a non-dynamic panel smooth transition regression model with fixed individual effects. The model is useful for describing heterogenous panels, with regression coefficients that vary across individuals and over time. Heterogeneity is allowed for by assuming that these coefficients are continuous functions of an observable variable through a bounded function of this variable and fluctuate between a limited number (often two) of “extreme regimes”. The model can be viewed as a generalization of the threshold panel model of Hansen (1999). We extend the modelling strategy for univariate smooth transition regression models to the panel context. This comprises of model specification based on homogeneity tests, parameter estimation, and diagnostic checking, including tests for parameter constancy and no remaining nonlinearity. The new model is applied to describe firms’ investment decisions in the presence of capital market imperfections.financial constraints; heterogenous panel; investment; misspecification test; nonlinear modelling panel data; smooth transition models

Out-of-sample comparison of copula specifications in multivariate density forecasts

We introduce a statistical test for comparing the predictive accuracy of competing copula specifications in multivariate density forecasts, based on the Kullback-Leibler Information Criterion (KLIC). The test is valid under general conditions: in particular it allows for parameter estimation uncertainty and for the copulas to be nested or nonnested. Monte Carlo simulations demonstrate that the proposed test has satisfactory size and power properties in finite samples. Applying the test to daily exchange rate returns of several major currencies against the US dollar we find that the Student’s t copula is favored over Gaussian, Gumbel and Clayton copulas. This suggests that these exchange rate returns are characterized by symmetric tail dependence.Copula-based density forecast; semiparametric statistics; out-of-sample forecast evaluation; Kullback-Leibler Information Criterion; empirical copula

Measuring and Predicting Heterogeneous Recessions

This paper conducts an empirical analysis of the heterogeneity of recessions in monthly U.S. coincident and leading indicator variables. Univariate Markovswitching models indicate that it is appropriate to allow for two distinct recession regimes, corresponding with ‘mild’ and ‘severe’ recessions. All downturns start with a mild decline in the level of economic activity. Contractions that develop into severe recessions mostly correspond with periods of substantial credit squeezes as suggested by the ‘financial accelerator’ theory. Multivariate Markov-switching models that allow for phase shifts between the cyclical regimes of industrial production and the Conference Board Leading Economic Index confirm these findings.Business cycle, phase shifts, regime-switching models, Bayesian analysis

Panel Smooth Transition Regression Models

We develop a non-dynamic panel smooth transition regression model with fixed individual effects. The model is useful for describing heterogenous panels, with regression coefficients that vary across individuals and over time. Heterogeneity is allowed for by assuming that these coefficients are continuous functions of an observable variable through a bounded function of this variable and fluctuate between a limited number (often two) of “extreme regimes”. The model can be viewed as a generalization of the threshold panel model of Hansen (1999). We extend the modelling strategy for univariate smooth transition regression models to the panel context. This comprises of model specification based on homogeneity tests, parameter estimation, and diagnostic checking, including tests for parameter constancy and no remaining nonlinearity. The new model is applied to describe firms' investment decisions in the presence of capital market imperfections.financial constraints; heterogeneous panel; invesatment; misspecification test; nonlinear modelling panel data; smooth transition model

Instability and nonlinearity in the euro area Phillips curve

This paper provides a comprehensive analysis of the functional form of the euro area Phillips curve over the past three decades. In particular, compared to previous literature we analyse the stability of the relationship in detail, especially as regards the possibility of a time-varying mean of inflation. Moreover, we conduct a sensitivity analysis across different measures of economic slack. Our main findings are two. First, there is strong evidence of time variation in the mean and slope of the Phillips curve occurring in the early to mid 1980s, but not in inflation persistence once the mean shift is allowed for. As a result of the structural change, the Phillips curve became flatter around a lower mean of inflation. Second, we find no significant evidence of non-linearity, in particular in relation to the output gap. JEL Classification: E52, E58Asymmetry, inflation, output gap, smooth transition model, Structural change

Predicting the term structure of interest rates incorporating parameter uncertainty, model uncertainty and macroeconomic information

We forecast the term structure of U.S. Treasury zero-coupon bond yields by analyzing a range of models that have been used in the literature. We assess the relevance of parameter uncertainty by examining the added value of using Bayesian inference compared to frequentist estimation techniques, and model uncertainty by combining forecasts from individual models. Following current literature we also investigate the benefits of incorporating macroeconomic information in yield curve models. Our results show that adding macroeconomic factors is very beneficial for improving the out-of-sample forecasting performance of individual models. Despite this, the predictive accuracy of models varies over time considerably, irrespective of using the Bayesian or frequentist approach. We show that mitigating model uncertainty by combining forecasts leads to substantial gains in forecasting performance, especially when applying Bayesian model averaging.Term structure of interest rates; Nelson-Siegel model; Affine term structure model; forecast combination; Bayesian analysis

Linear models, smooth transition autoregressions and neural networks for forecasting macroeconomic time series: A reexamination

In this paper we examine the forecast accuracy of linear autoregressive, smooth transition autoregressive (STAR), and neural network (NN) time series models for 47 monthly macroeconomic variables of the G7 economies. Unlike previous studies that typically consider multiple but fixed model specifications, we use a single but dynamic specification for each model class. The point forecast results indicate that the STAR model generally outperforms linear autoregressive models. It also improves upon several fixed STAR models, demonstrating that careful specification of nonlinear time series models is of crucial importance. The results for neural network models are mixed in the sense that at long forecast horizons, an NN model obtained using Bayesian regularization produces more accurate forecasts than a corresponding model specified using the specific-to-general approach. Reasons for this outcome are discussed.forecast combination; forecast evaluation; neural network model; nonlinear modelling; nonlinear forecasting JEL Codes: C22; C53

Short-term Volatility versus Long-term Growth: Evidence in US Macroeconomic Time Series

We test for a change in the volatility of 215 US macroeconomic time series over the period 1960-1996. We find that about 90% of these series have experienced a break in volatility during this period. This result is robust to controlling for instability in the mean and business cycle nonlinearities. Real variables have seen a reduction in volatility since the early 1980s, which is accompanied by lower but steadier output growth. Furthermore, nominal variables have seen temporary increases in their volatility around the early 1980s. This suggests the existence of a trade-off between short-term volatility and the long-term pattern of growth.