Business cycle asymmetries and non-linearities in U.K. macroeconomic time series

This paper empirically tests for and models non-linearities in a selection of U.K. macroeconomic time series. Attention is focused first on business cycle asymmetry, using Markov chain models to investigate whether cycles in macroeconomic time series display symmetric behaviour on both sides of a peak or trough. Next, a selection of statistical tests of non-linearity are employed to investigate formally the presence of departures from the linearity assumption. A variety of specific non-linear models of the business cycle that have been proposed recently are then fitted to ascertain how useful they are in explaining any non-linearities that have been observed in the series. Finally, the results are brought together in an extended discussion of their implications for business cycle research and policy analysis.     

Global imbalances,cross-market linkages,and the financial crisis: A multivariate Markov-switching analysis

Based on multivariate Markov-switching models, this paper presents new results on the interactions between global imbalances, credit spreads, housing markets, macroeconomic variables, commodities and equities during Q1-1987/Q1-2011. We show that rising global imbalances and the uncontrolled development of the US mortgage and housing markets have been deeply destabilizing the economy, with various shocks impacting subsequently equity markets and macroeconomic variables. But we also uncover, surprisingly, that the cross-market linkages with the commodity markets are strong. Finally, we identify that the US housing market lies at the epicenter of the crisis through its multiple and highly significant interactions with the other variables in the system (including the global imbalances). Sub-samples and alternative time series estimates are provided to check the statistical congruency of the various models.     

A multivariate analysis of forecast disagreement: Confronting models of disagreement with survey data

This paper documents multivariate forecast disagreement among professional forecasters and discusses implications for models of heterogeneous expectation formation. Disagreement varies over time and is positively correlated with general (economic) uncertainty. The degree to which individual forecasters disagree with the average forecast tends to persist over time. Models of heterogeneous expectation formation can be modified by introducing heterogeneous signal-to-noise ratios to match this feature. Furthermore, disagreement about correlations of different macroeconomic variables is high on average. In general, multivariate forecast data can be used more effectively than it has been to estimate models with heterogeneous expectations and to test the mechanisms used to generate disagreement in these models.     

Multiple time series analysis of simultaneous equations model specification

This paper presents an extension of the Zellner-Palm methodology using the multiple time series representation of an underlying structural econometric model. The multiple time series approach avoids the problem of cancellation of common factors that has made it difficult to infer structural model characteristics from univariate time series models. In addition the correspondence between the structural model and the multiple time series model provides structural content to the tests for Granger-causality. The approach is illustrated with applications to small macroeconomic models of Friedman and Sargent and Wallace.     

TAIL FORECASTING WITH MULTIVARIATE BAYESIAN ADDITIVE REGRESSION TREES

We develop multivariate time-series models using Bayesian additive regression trees that posit nonlinearities among macroeconomic variables, their lags, and possibly their lagged errors. The error variances can be stable, feature stochastic volatility, or follow a nonparametric specification. We evaluate density and tail forecast performance for a set of U.S. macroeconomic and financial indicators. Our results suggest that the proposed models improve forecast accuracy both overall and in the tails. Another finding is that when allowing for nonlinearities in the conditional mean, heteroskedasticity becomes less important. A scenario analysis reveals nonlinear relations between predictive distributions and financial conditions.     

On business cycle fluctuations in USA macroeconomic time series

This study employs eighteen USA macroeconomic time series variables to investigate possible existence of asymmetries in business cycle fluctuations in the series. Detection of asymmetric fluctuations in economic activity is important for policymakers since effective monetary policy relies on asymmetric business cycle fluctuations in all the series. The asymmetric deviations from the long-term growth trend in each of the series are modeled using regime switching models and artificial neural networks. The results based on nonlinear switching time series models reveal strong evidence of business cycle asymmetries in most of the series. The results based on in-sample approximations from artificial neural networks show statistically significant evidence of asymmetries in all the series. Similar results are obtained when jackknife out-of-sample approximations from artificial neural networks are used. Thus, the study results show statistically significant evidence of asymmetries in all the series which indicates that business cycle fluctuations in the series are asymmetric, thus alike. Therefore, the impact of monetary policy shocks on the output and the other macroeconomic variables can be anticipated using nonlinear models only. The results on asymmetric business cycle fluctuations in real GDP are in line with recent studies but in sharp contrast with Balke and Fomby (1994).     

Seasonality in macroeconomic time series

Recently, the seasonal characteristics of macroeconomic time series have drawn a lot of attention. It has been argued that the seasonal component of many macroeconomic time series constitutes a major part of the series measured as a proportion of the variance. In addition it has been found that the seasonal component of most macroeconomic time series is constant and best “explained” by seasonal dummies. Specifically it is often found that a Christmas boom is followed by a beginning of the year trough. Based on quarterly and monthly macroeconomic time series from a large number of countries this paper shows that many macroeconomic time series have seasonal components that are changing over time. Furthermore, the Christmas boom and especially the 1st quarter trough is not found nearly as often as one might expect.     

Forecast accuracy of small and large scale dynamic factor models in developing economies

Developing economies usually present limitations in the availability of economic data. This constraint may affect the capacity of dynamic factor models to summarize large amounts of information into latent factors that reflect macroeconomic performance. This paper addresses this issue by comparing the accuracy of two kinds of dynamic factor models at GDP forecasting for six Latin American countries. Each model is based on a dataset of different dimensions: a large dataset composed of series belonging to several macroeconomic categories (large scale dynamic factor model) and a small dataset with a few prescreened variables considered as the most representative ones (small scale dynamic factor model). Short‐term pseudo real time out‐of‐sample forecast of GDP growth is carried out with both models reproducing the real time situation of data accessibility derived from the publication lags of the series in each country. Results (i) confirm the important role of the inclusion of latest released data in the forecast accuracy of both models, (ii) show better precision of predictions based on factors with respect to autoregressive models and (iii) identify the most adequate model for each country according to availability of the observed data.     

Bayesian model averaging and identification of structural breaks in time series

Bayesian Model Averaging (BMA) is used for testing for multiple break points in univariate series using conjugate normal-gamma priors. This approach can test for the number of structural breaks and produce posterior probabilities for a break at each point in time. Results are averaged over specifications including: stationary; stationary around trend and unit root models, each containing different types and number of breaks and different lag lengths. The procedures are used to test for structural breaks on 14 annual macroeconomic series and 11 natural resource price series. The results indicate that there are structural breaks in all of the natural resource series and most of the macroeconomic series. Many of the series had multiple breaks. Our findings regarding the existence of unit roots, having allowed for structural breaks in the data, are largely consistent with previous work.     

Seasonal fractional components in macroeconomic time series

Seasonal fractional models are shown in this article to be alternative credible ways of modelling the seasonal component in macroeconomic time series. A testing procedure that allows one to test different orders of integration at zero and at each of the seasonal frequencies is described. This procedure is then applied to the Italian consumption and income series, the results being very sensitive to the way of modelling the I(0) disturbances.     

A note on modelling downturns: A non-linear model vs. simple linear autoregressive schemes

This note presents empirical/simulations results which compare a simple Kaldor-type non-linear model and comparable linear autoregressive schemes as models of sharp movements often observed in macroeconomic time series that exhibit persistent fluctuations.     

Stationarity and structural breaks — evidence from classical and Bayesian approaches

The purpose of this paper is to analyze and compare the results of applying classical and Bayesian methods to testing for a unit root in time series with a single endogenous structural break. We utilize a data set of macroeconomic time series for the Mexican economy similar to the Nelson–Plosser one. Under both approaches, we make use of innovational outlier models allowing for an unknown break in the trend function. Classical inference relies on bootstrapped critical values, in order to make inference comparable to the finite sample Bayesian one. Results from both approaches are discussed and compared.     

Estimating parameters of real business cycle models

The calibration technique is the most common procedure to match the data generated from an equilibrium business cycle model with actual macroeconomic time series. This paper goes a step further and tests and applies a maximum likelihood procedure, in combination with the simulated annealing, to estimate the parameters of a baseline RBC model from U.S. macroeconomic time series data. The procedure is tested on a simulated data set where the parameters are known and then applied to U.S. time series data. This permits us to evaluate the efficiency of the procedure and the extent to which the RBC model is a good representation of macroeconomic data.     

Analysing shock transmission in a data-rich environment: a large BVAR for New Zealand

We analyse a large Bayesian Vector Autoregression (BVAR) containing almost 100 New Zealand macroeconomic time series. Methods for allowing multiple blocks of equations with block-specific Bayesian priors are described, and forecasting results show that our model compares favourably to a range of other time series models. Examining the impulse responses to a monetary policy shock and to two less conventional shocks—net migration and the climate—we highlight the usefulness of the large BVAR in analysing shock transmission.     

Deleveraging crises and deep recessions: a behavioural approach

Macroeconomic dynamics are characterized by alternating patterns of periods of relative stability and large swings. Standard microfounded macroeconomic models account for these patterns through exogenous and persistent shocks. In this article, we develop a fully decentralized and microfounded macroeconomic agent-based model, augmented with an opinion model, which produces endogenous waves of pessimism and optimism that feed back into firms’ leverage and households’ precautionary saving behaviour. A major emergent property of our model is precisely the complex successions of stable and unstable macroeconomic regimes. The model is further able to account for a wide spectrum of macro and micro empirical regularities. Within this framework, we analyse a series of macroeconomic phenomena of key relevance in the current macroeconomic debate, especially the occurrence of deleveraging crises and Fisherian debt-deflation recessions. Our analysis suggests that the relative dynamics of prices and wages and the resulting income distribution along a deflationary path are critical determinants of the severity of the recession and the chances of recovery.     

Measuring the Euro area output gap using a multivariate unobserved components model containing phase shifts

Several recent studies have used multivariate unobserved components models to identify the output gap and the non-accelerating inflation rate of unemployment. A key assumption of these models is that one common cycle component, such as the output gap, drives the cyclical fluctuations in all variables included in the model. This article also uses the multivariate approach to estimate the euro area output gap and the trends and cycles in other macroeconomic variables. However, it adopts a flexible way of linking the output gap to the cycle components in the other variables, in that we do not impose any leading or lagging restrictions between cycle components, as has been done in most previous studies. Our approach also allows us to assess the strength of cycle association and cross-correlation among cycle components using the model??s parameter estimates. Finally, we demonstrate that our multivariate model can provide a satisfactory historical output gap estimate and also a ??real-time?? estimate for the aggregate euro area.     

A medium-N approach to macroeconomic forecasting

This paper considers methods for forecasting macroeconomic time series in a framework where the number of predictors, N, is too large to apply traditional regression models but not sufficiently large to resort to statistical inference based on double asymptotics. Our interest is motivated by a body of empirical research suggesting that popular data-rich prediction methods perform best when N ranges from 20 to 40. In order to accomplish our goal, we resort to partial least squares and principal component regression to consistently estimate a stable dynamic regression model with many predictors as only the number of observations, T, diverges. We show both by simulations and empirical applications that the considered methods, especially partial least squares, compare well to models that are widely used in macroeconomic forecasting.     

Improving forecast accuracy of financial vulnerability: PLS factor model approach

We present a factor augmented forecasting model for assessing the financial vulnerability in Korea. Dynamic factor models often extract latent common factors from a large panel of time series data via the method of the principal components (PC). Instead, we employ the partial least squares (PLS) method that estimates target specific common factors, utilizing covariances between predictors and the target variable. Applying PLS to 198 monthly frequency macroeconomic time series variables and the Bank of Korea's Financial Stress Index (KFSTI), our PLS factor augmented forecasting models consistently outperformed the random walk benchmark model in out-of-sample prediction exercises in all forecast horizons we considered. Our models also outperformed the autoregressive benchmark model in short-term forecast horizons. We expect our models would provide useful early warning signs of the emergence of systemic risks in Korea's financial markets.     

An estimate of the capital stock for the Belgian industrial sector

The paper submitted for publication considers the conditions for and the construction of a capital stock series, consistent with all macroeconomic relations. A careful study of the literature has shown that most existing and widely used capital stock series do not meet that requirement. A generative method with a general model for the real sector is proposed. The choice of the parameters of this model was decided upon the general consistency with the main macroeconomic aggregates. The results are compared with existing series of capital stock. What was obtained was a capital stock series that gives non-diverging results for the macroeconomic series when using the capital stock in alternative specifications. The main conclusions on depreciation rate, capital stock, technological change, return on capital and share of capital are condensed in a set of tables and diagrams.     

Dynamic forecasting of monetary exchange rate models: Evidence from cointegration

The Frenkel-Bilson and Dornbusch-Frankel monetary exchange rate models are used to estimate the out-of-sample forecasting performance for the U.S. dollar/Canadian dollar exchange rate. By using Johansen's multivariate cointegration, up to three cointegrating vectors were found between the exchange rate and macroeconomic fundamentals. This means that there is a long-run relationship between the exchange rate and economic fundamentals. Based on error correction models, two monetary models outperform the random walk model at the three-, six-, and 12-month forecasting horizons. Therefore, monetary exchange rate models are still useful in forecasting exchange rates.