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.     

SPATIAL LIMITED DEPENDENT VARIABLE MODELS: A REVIEW FOCUSED ON SPECIFICATION,ESTIMATION, AND HEALTH ECONOMICS APPLICATIONS

Modeling individual choices is one of the main aim in microeconometrics. Discrete choice models have been widely used to describe economic agents' utility functions and most of them play a paramount role in applied health economics. On the other hand, spatial econometrics collects a series of econometric tools, which are particularly useful when we deal with spatially distributed data sets. Accounting for spatial dependence can avoid inconsistency problems of the commonly used statistical estimators. However, the complex structure of spatial dependence in most of the nonlinear models still precludes a large diffusion of these spatial techniques. The purpose of this paper is then twofold. The former is to review the main methodological problems and their different solutions in spatial nonlinear modeling. The latter is to review their applications to health issues, especially those appeared in the last few years, by highlighting the main reasons why spatial discrete neighboring effects should be considered and suggesting possible future lines of development in this emerging field. Particular attention has been paid to cross‐sectional spatial discrete choice modeling. However, discussions on the main methodological advancements in other spatial limited dependent variable models and spatial panel data models are also included.     

ESTIMATION OF PROBABILITY MODELS FROM INCOME CLASS DATA

Abstract. Many household surveys report income by a limited number of classes only. In Engel curve analyses of such data, income is usually equated to the class midpoint. We argue that it is better to specify a continuous income distribution over the entire range, and to estimate its parameters jointly with the Engel curve coefficients; the position of the dependent variable within income classes is sufficiently informative about the income distribution parameters. The method is illustrated for a simple logit model of automobile ownership. The effect on estimation is perceptible, but not impressive; further analysis suggests that the standard method is acceptable provided some care is taken over the income class limits at the lower end of the range.     

ARCH MODELS: PROPERTIES, ESTIMATION AND TESTING

Abstract. The aim of this survey paper is to provide an account of some of the important developments in the autoregressive conditional heteroskedasticity (ARCH) model since its inception in a seminal paper by Engle (1982). This model takes account of many observed properties of asset prices, and therefore, various interpretations can be attributed to it. We start with the basic ARCH models and discuss their different interpretations. ARCH models have been generalized in different directions to accommodate more and more features of the real world. We provide a comprehensive treatment of many of the extensions of the original ARCH model. Next we discuss estimation and testing for ARCH models and note that these models lead to some interesting and unique problems. There have been numerous applications and we mention some of these as we present different models. The paper includes a glossary of the acronyms for the models we describe.     

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].     

MIXED‐FREQUENCY STRUCTURAL MODELS: IDENTIFICATION,ESTIMATION, AND POLICY ANALYSIS

The mismatch between the timescale of DSGE (dynamic stochastic general equilibrium) models and the data used in their estimation translates into identification problems, estimation bias, and distortions in policy analysis. We propose an estimation strategy based on mixed‐frequency data to alleviate these shortcomings. The virtues of our approach are explored for two monetary policy models. Copyright © 2014 John Wiley & Sons, Ltd.     

NOMINAL AND REAL WAGE RIGIDITIES IN NEW KEYNESIAN MODELS: A CRITICAL SURVEY

Abstract The labour market is receiving increasing attention in the New Keynesian literature. In this paper, I critically survey this literature in order to highlight the role played by wage rigidities in the explanation of fluctuations caused by technology shocks. To this aim, I present a dynamic stochastic general equilibrium model with sticky prices, nominal wage rigidities and hiring costs. The comparison between this model and that of Blanchard and Gali highlights the non‐trivial differences which exist in the way nominal wage and real wage rigidities drive the economy's dynamics. My conclusion is that models incorporating nominal wage rigidities and some degree of price stickiness provide a better account of macroeconomic dynamics than models with real wage rigidities.     

MODELS OF REGIONAL GROWTH: PAST,PRESENT AND FUTURE

Abstract This paper presents an overview of various models of regional growth that have appeared in the literature in the last 40 years. It considers the past, and therefore supply‐side models, such as the standard neoclassical, juxtaposed against essentially demand‐side approaches such as the export‐base and cumulative causation models (as integrated into the Kaldorian approach); before moving on to the ‘present’ and more recent versions of the neoclassical model involving spatial weights and ‘convergence clubs’, as well as new economic geography core–periphery models, and the ‘innovation systems’ approach. A key feature of the more recent literature is an attempt to explicitly include spatial factors into the model, and thus there is a renewed emphasis on agglomeration economies and spillovers. Discussing ‘present’ and ‘future’ approaches to regional growth overlaps with the current emphasis in the literature on the importance of more intangible factors such as the role of ‘knowledge’ and its influence on growth. Finally, there is a discussion of the greater emphasis that needs to be placed at the ‘micro‐level’ when considering what drives growth, and thus factors such as inter alia firm heterogeneity, entrepreneurship and absorptive capacity.     

INTER-REGIONAL TRADE FLOW ESTIMATION THROUGH NON-SURVEY MODELS: AN EMPIRICAL ASSESSMENT

Inter-regional trade estimation has been pointed out as a crucial problem when constructing a multiregional input–output system. Knowledge of inter-regional trade flows, at least of the pooled volume of exports and imports by commodity, is critical in accounting for important spillover and feedback effects deriving from inter-regional linkages. However, in most countries, there are no completely reliable survey-based statistics on inter-regional trade. Thus, this paper intends to evaluate the reasonability of using indirect inter-regional trade estimates, comparing different estimating methods and assessing the sensitivity of the model results. Based on our empirical comparisons we conclude that input–output models are not greatly affected by the insertion of different trade values. Thus, our results support the use of indirect estimates for inter-regional trade, whenever survey-based data are unavailable.     

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.     

RANDOM COEFFICIENT MODELS: THEORY AND APPLICATIONS

Abstract. This paper provides an overview of the rationale behind, and the implementation, and uses of, the random coefficient approach to econometric modelling. A simple random coefficient model is presented, and methods for estimating, testing, and validating such a model are described. A more general model is then presented. The general model is shown to include several fixed-coefficient models as special cases and can be estimated incorporating a variety of judgements concerning simplification. Finally, the paper reviews recent applications of random coefficient estimation.     

A CRITICAL REVIEW ON EVOLUTION OF RISK FACTORS AND FACTOR MODELS

The purpose of this work is to critically evaluate the evolution of risk factors and factor models. A systematic and structured literature review is carried out to observe and understand the past trends and extant patterns/themes in the present research area, evaluate contributions and summarize knowledge, thereby identifying limitations, implications and potential directions of further research. The main message from the study is that evolution of risk factors and factor models are continuous and endless development. Still today over 300 risk factors are identified by the researchers and many other yet to be discovered but out of them all only few are significantly responsible in explaining the stock markets risk return relationship. Study classifies risk factors into two groups: global and specific risk factors. Study answer the question ‘whether evolution of risk factors and factor models are endless development’. Finally, the present study gives an appropriate direction to the future studies to be taken in terms of risk factors and factor models. Due to continuous evolution and changing of nature of the risk factor it seems quite impossible to have a stable efficient factor models that can explain stock market risk return relationship globally in long run.     

Forecasting bulk prices of Bordeaux wines using leading indicators

Agricultural price forecasting has been being abandoned progressively by researchers ever since the development of large-scale agricultural futures markets. However, as with many other agricultural goods, there is no futures market for wine. This paper draws on the agricultural prices forecasting literature to develop a forecasting model for bulk wine prices. The price data include annual and monthly series for various wine types that are produced in the Bordeaux region. The predictors include several leading economic indicators of supply and demand shifts. The stock levels and quantities produced are found to have the highest predictive power. The preferred annual and monthly forecasting models outperform naive random walk forecasts by 27.1% and 3.4% respectively; their mean absolute percentage errors are 2.7% and 3.4% respectively. A simple trading strategy based on monthly forecasts is estimated to increase profits by 3.3% relative to a blind strategy that consists of always selling at the spot price.     

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.     

Combining multiple probability predictions using a simple logit model

This paper begins by presenting a simple model of the way in which experts estimate probabilities. The model is then used to construct a likelihood-based aggregation formula for combining multiple probability forecasts. The resulting aggregator has a simple analytical form that depends on a single, easily-interpretable parameter. This makes it computationally simple, attractive for further development, and robust against overfitting. Based on a large-scale dataset in which over 1300 experts tried to predict 69 geopolitical events, our aggregator is found to be superior to several widely-used aggregation algorithms.