Forecasting with factor-augmented error correction models

As a generalization of the factor-augmented VAR (FAVAR) and of the Error Correction Model (ECM), Banerjee and Marcellino (2009) introduced the Factor-augmented Error Correction Model (FECM). The FECM combines error-correction, cointegration and dynamic factor models, and has several conceptual advantages over the standard ECM and FAVAR models. In particular, it uses a larger dataset than the ECM and incorporates the long-run information which the FAVAR is missing because of its specification in differences. In this paper, we examine the forecasting performance of the FECM by means of an analytical example, Monte Carlo simulations and several empirical applications. We show that FECM generally offers a higher forecasting precision relative to the FAVAR, and marks a useful step forward for forecasting with large datasets.     

Econometric analysis of structural systems with permanent and transitory shocks

This paper considers the implications of the permanent/transitory decomposition of shocks for identification of structural models in the general case where the model might contain more than one permanent structural shock. It provides a simple and intuitive generalization of the influential work of Blanchard and Quah [1989. The dynamic effects of aggregate demand and supply disturbances. The American Economic Review 79, 655–673], and shows that structural equations with known permanent shocks cannot contain error correction terms, thereby freeing up the latter to be used as instruments in estimating their parameters. The approach is illustrated by a re-examination of the identification schemes used by Wickens and Motto [2001. Estimating shocks and impulse response functions. Journal of Applied Econometrics 16, 371–387], Shapiro and Watson [1988. Sources of business cycle fluctuations. NBER Macroeconomics Annual 3, 111–148], King et al. [1991. Stochastic trends and economic fluctuations. American Economic Review 81, 819–840], Gali [1992. How well does the ISLM model fit postwar US data? Quarterly Journal of Economics 107, 709–735; 1999. Technology, employment, and the business cycle: Do technology shocks explain aggregate fluctuations? American Economic Review 89, 249–271] and Fisher [2006. The dynamic effects of neutral and investment-specific technology shocks. Journal of Political Economy 114, 413–451].     

Forecasting and explaining aggregate consumer credit delinquency behaviour

We model aggregate delinquency behaviour for consumer credit (including credit card loans and other consumer loans) and for residential real estate loans using data up until 2008. We test for cointegrating relationships and then estimate short run error correction models. We find evidence to support the portfolio explanations of declines in credit quality for consumer and for real estate loans, but support for the reduced stigma explanation was restricted to real estate loans. Evidence supportive of household-level explanations of irrational borrowing and unexpected net income shocks was found for consumer and real estate loans, but evidence of strategic default was restricted to the volume of consumer loans and real estate loans, and not for credit cards. We also found that the error correction model gave forecasts of the volume of delinquent consumer debt which were of an accuracy comparable to that of an ARIMA model.     

Forecasting the US housing market

The recent housing market boom and bust in the United States illustrates that real estate returns are characterized by short-term positive serial correlation and long-term mean reversion to fundamental values. We develop an econometric model that includes these two components, but with weights that vary dynamically through time depending on recent forecasting performances. The smooth transition weighting mechanism can assign more weight to positive serial correlation in boom times, and more weight to reversal to fundamental values during downturns. We estimate the model with US national house price index data. In-sample, the switching mechanism significantly improves the fit of the model. In an out-of-sample forecasting assessment the model performs better than competing benchmark models.     

Virtual special issue on regional inequality

ABSTRACT

This virtual special issue of Spatial Economic Analysis marks the keynote lecture by Professor Sergio Rey of the University of California – Riverside at the 58th Annual Congress of the European Regional Science Association in Cork, Ireland. It draws together nine papers from previous issues of the journal that deal with regional and spatial inequalities, a central theme of regional science in general and Rey’s research in particular, thereby providing an overview of the main sources of regional and spatial inequalities both within and across countries.     

Forecasting residential burglary

Following the work of Dhiri et al. [Modelling and predicting property crime trends. Home Office Research Study 198 (1999). London: HMSO] at the Home Office predicting recorded burglary and theft for England and Wales to the year 2001, econometric and time series models were constructed for predicting recorded residential burglary to the same date. A comparison between the Home Office econometric predictions and the less alarming econometric predictions made in this paper identified the differences as stemming from the particular set of variables used in the models. However, the Home Office and one of our econometric models adopted an error correction form which appeared to be the main reason why these models predicted increases in burglary. To identify the role of error correction in these models, time series models were built for the purpose of comparison, all of which predicted substantially lower numbers of residential burglaries. The years 1998–2001 appeared to offer an opportunity to test the utility of error correction models in the analysis of criminal behaviour. Subsequent to the forecasting exercise carried out in 1999, recorded outcomes have materialised, which point to the superiority of time series models compared to error correction models for the short-run forecasting of property crime. This result calls into question the concept of a long-run equilibrium relationship for crime.     

An empirical analysis of international labour migration in the Philippines

This study empirically investigates the impact of economic, demographic, and political factors on the size of emigration from the Philippines. In 2007, overseas workers from the Philippines sent remittances in excess of US$14 billion annually to their families back home. Although these remittances are an important source of foreign exchange and play an important role in economic development, the determinants of emigration in the Philippines are not well established. A simple unrestricted error correction model of migration was specified and estimated using data spanning the period 1975–2005. Results indicate that the level of unemployment, adult literacy and population density are the key determinants of emigration in the Philippines. The result also indicates that government instability impacts negatively on emigration in the Philippines. The policy implications of the results are discussed.     

Growth, cycles and convergence in US regional time series

This article reports the results of fitting unobserved components (structural) time series models to data on real income per capita in eight regions of the United States. The aim is to establish stylised facts about cycles and convergence. It appears that while the cycles are highly correlated, the two richest regions have been diverging from the others in recent years. A new model is developed in order to characterise the converging behaviour of the six poorest regions. The model combines convergence components with a common trend and cycles. These convergence components are formulated as a second-order error correction mechanism which allows temporary divergence while imposing eventual convergence. After fitting the model, the implications for forecasting are examined. Finally, the use of unit root tests for testing convergence is critically assessed in the light of the stylised facts obtained from the fitted models.     

Foreign direct investment and its determinants: A regional panel causality analysis

Moving beyond traditional one- or possibly two-way causality involving foreign direct investment (FDI), a systematic approach is implemented for delineating both short- and long-run flows of causality involving FDI and a comprehensive set of FDI's possible determinants. Granger causality procedures incorporating error correction terms are implemented, using provincial panel data from China. In both the short and long run, growth in GDP directly influences FDI, while growth in local infrastructure and local investment provide indirect but not direct influence.     

Analysis of cointegrated VARMA processes

VARMA (vector autoregressive moving average) processes are proposed for modelling cointegrated variables. For this purpose the echelon form is combined with the error correction form. Procedures for estimating the Kronecker indices which characterize the echelon form and for specifying the cointegration rank are discussed. The asymptotic distribution of the coefficient estimators is given. An example based o n US macroeconomic data illustrates the procedure and demonstrates its feasibility in practice.     

IDENTIFICATION METHODS IN VECTOR‐ERROR CORRECTION MODELS: EQUIVALENCE RESULTS

In a structural vector‐error correction (VEC) model, it is possible to decompose the shocks into those with permanent and transitory effects on the levels of the variables. Pagan and Pesaran derive the restrictions which the permanent–transitory decomposition of the shocks imposes on the structural VEC model. This paper shows that these restrictions are equivalent to a set of restrictions that are applied in the methods of Gonzalo and Ng and King et al. (KPSW). Using this result, it is shown that the Pagan and Pesaran method can be used to recover the structural shocks with permanent effects identically to those from the Gonzalo and Ng and KPSW methods. In the former case, this is illustrated in the context of Lettau and Ludvigson's consumption model and in the latter case in KPSW's six variable model. There are also two other methods for which the Pagan and Pesaran approach can deliver identical permanent shocks which are also discussed.     

Dynamic Modeling with Structural Equations and Stochastic Differential Equations: Applications with the German Socio-economic Panel

The paper discusses an application of linear dynamic models to multi-wave longitudinal data. Starting from three-wave and four-wave simplex models using standard structural equations, linear dynamic state space models with stochastic differential equations are presented. The main differences between longitudinal structural equations (static view) and stochastic differential equations (dynamic view) are emphasized. Substantively, the models prove the relation, stability and change of two concepts in a period of 10 years: National Identity and Intention to stay in Germany. Data from a sample of migrant workers in Germany included in the German Socio-economic Panel (GSOEP) are used for the analyses. Results and further developments of dynamic models are discussed in the final section.The authors thank Hermann Singer for his comments and discussions on applications of dynamic models.     

汇率波动率与中国对主要贸易伙伴的出口

本文研究汇率波动率对中国向三个主要贸易伙伴美国、欧盟和日本出口的影响。通过协整检验,误差修正模型和Granger非因果检验等方法估计变量间长短期的关系。研究表明,中国向美国和欧盟的实际出口与实际汇率波动率存在长期显著的负相关关系,而中国向日本的出口与汇率波动率无关。短期内汇率波动率只影响中国向美国的出口,对向欧盟和日本的出口没有影响。     

Automatic leading indicators versus macroeconometric structural models: A comparison of inflation and GDP growth forecasting

This paper compares the forecast performance of automatic leading indicators (ALIs) and macroeconometric structural models (MESMs) commonly used by non-academic macroeconomists. Inflation and GDP growth form the forecast objects for comparison, using data from China, Indonesia and the Philippines. ALIs are found to outperform MESMs for one-period-ahead forecasts, but this superiority disappears as the forecast horizon increases. It is also found that ALIs involve greater uncertainty in choosing indicators, mixing data frequencies and utilizing unrestricted VARs. Two ways of reducing the uncertainty are explored: (i) give theory priority in choosing indicators, and include theory-based disequilibrium shocks in the indicator sets; and (ii) reduce the VARs by means of the general-to-specific modeling procedure.