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].     

CO‐INTEGRATING VAR MODELS AND ECONOMIC POLICY

This paper surveys some relevant contributions to the economic literature on co‐integrating vector autoregressive (VAR) models [vector error correction mechanisms (VECMs)], emphasizing their usefulness for economic policy. It further discusses some theoretical aspects that are necessary for a complete understanding of their potential. The theoretical introduction of the co‐integrating VAR model is followed by an illustration of its applications to monetary policy, fiscal policy and exchanges rates as well as in establishing the effects of structural bilateral shocks between countries (the so‐called global VAR, or GVAR, models). Special attention is paid to the VECM capacities of being used in conjunction with dynamic stochastic general equilibrium models and of jointly specifying the short‐ and long‐run dynamics, thus representing the steady‐state of economic systems (by means of the co‐integration relations) and the short‐run dynamics around it.     

DEMAND,CUSTOMER BASE‐LINE AND DEMAND RESPONSE IN THE ELECTRICITY MARKET: A SURVEY

The increasing use of demand‐side management, as a tool to reliably meet electricity demands at peak time, has stimulated interest among researchers, consumers and producer organiza‐tions, managers, regulators and policymakers. This research reviews the growing literature on models which are used to study demand, customer base‐line (CBL) and demand response in the electricity market. After characterizing the general demand models, the CBL, based on which the demand response models are studied, is reviewed. Given the experience gained from the review and existing conditions, the study combines an appropriate model for each case for a possible application to the electricity market; moreover, it discusses the implications of the results. In the literature, these aspects are studied independently. The main contribution of this survey is attributed to the treatment of the three issues as sequentially interdependent. The review is expected to enhance the understanding of the demand, CBL and demand response in the electricity market and their relationships. The objective is conducted through a combination of demand and supply side managements in order to reduce demand through different demand response programs during peak times. This enables electricity suppliers to save costly electricity generation and at the same time reduce energy vulnerability.     

Robust Small Area Estimation under Spatial Non‐stationarity

The effective use of spatial information in a regression‐based approach to small area estimation is an important practical issue. One approach to account for geographic information is by extending the linear mixed model to allow for spatially correlated random area effects. An alternative is to include the spatial information by a non‐parametric mixed models. Another option is geographic weighted regression where the model coefficients vary spatially across the geography of interest. Although these approaches are useful for estimating small area means efficiently under strict parametric assumptions, they can be sensitive to outliers. In this paper, we propose robust extensions of the geographically weighted empirical best linear unbiased predictor. In particular, we introduce robust projective and predictive estimators under spatial non‐stationarity. Mean squared error estimation is performed by two analytic approaches that account for the spatial structure in the data. Model‐based simulations show that the methodology proposed often leads to more efficient estimators. Furthermore, the analytic mean squared error estimators introduced have appealing properties in terms of stability and bias. Finally, we demonstrate in the application that the new methodology is a good choice for producing estimates for average rent prices of apartments in urban planning areas in Berlin.     

SHIFTING SANDS: NON‐LINEARITY,COMPLEXITY AND RANDOMNESS IN ECONOMICS

Abstract The great crash of 2008 and the associated banking crisis have exposed the increasing irrelevance of much mainstream economics and provoked some economists to re‐examine their discipline. Linear or linearized models with well‐behaved additive stochastic disturbances, based on ‘microeconomic foundations’ are no longer anywhere near adequate. Non‐linearity, complexity and randomness cannot be avoided, and the ideas of the British Emergentists have recently been given a new lease of life. Basing economics on algorithmic foundations provides a means of restoring genuine rigour to economics and, it is hoped, allowing the discipline to respond in a rational and humane way the next time a major crisis looms.     

THE IMPACT OF LABOR MARKET REGULATION ON EMPLOYMENT IN LOW‐INCOME COUNTRIES: A META‐ANALYSIS

Controversy over labor market policy often centers on achieving a balance between preventing worker exploitation, and avoiding loss of productivity or employment through excessive regulation. Although the literature documenting the impact of labor market regulation on employment is extensive, there is a dearth of evidence on the impact of such policies in low‐income countries (LICs). Since it is easier for workers, especially women, to slip into the informal sector in LICs, regulations are likely to have stronger impacts on formal employment in these countries (but lower impacts on unemployment). We systematically reviewed available research from countries that are, or were until recently, LICs. Most studies document that more stringent labor regulations are associated with lower formal sector employment and higher informal sector employment. We also conducted a metaregression analysis of the impact of minimum wages on formal and informal employment. After controlling for publication bias, higher minimum wages are associated with lower formal employment and a higher share of informal workers.     

OWNERSHIP CONCENTRATION AND FIRM PERFORMANCE IN EMERGING MARKETS: A META‐ANALYSIS

The literature concerned with relations between ownership concentration and corporate performance in emerging markets presents conflicting theoretical predications and inconclusive empirical results. We use meta‐analytical techniques to integrate the diverse empirical findings and investigate factors contribute to the inconsistencies in the empirical evidence. Using 419 correlations collected from 42 primary studies of listed corporations in 18 emerging markets, heterogeneity tests show that only a small proportion of the variation in reported ownership concentration–performance relations can be attributed to sampling error. Our meta‐regressions reveal how population differences, researchers’ modeling choices, and inadequate treatment of endogeneity explain a large portion of the true heterogeneity. After adjusting for these effects, we find ownership concentration has negative relation with firm performance across countries. Our results emphasize the importance of model specification and methods of addressing endogeneity, and support further comparative study of the ownership concentration–corporate performance relation between countries with seemingly similar corporate governance environments.