Economic sentiment and business cycles: A spillover methodology approach

This paper is one of the latest attempts to observe and explain the relationship between the Economic Sentiment Indicator (ESI) and GDP growth. This new approach uses the Diebold and Yilmaz (2012, 2009) spillover methodology by combining the feedback relationship and the time-varying (dynamic) aspect of that same relationship. The empirical analysis is based on quarterly data over different periods in the European Union (EU) (as a whole) and in ten Central and Eastern European (CEE) countries. The main results indicate that the spillovers of shocks between ESI and GDP growth are time-varying for all observed countries, with increasing values of total spillovers in the last recession and afterwards. The direction and strength of spillover effects between economic sentiment and GDP growth are different for the analysed CEE countries. The results are robust with respect to changing the rolling window length, additional Granger causality testing and rolling correlation coefficients.     

Partial parametric estimation for nonstationary nonlinear regressions

This paper proposes an estimation method for a partial parametric model with multiple integrated time series. Our estimation procedure is based on the decomposition of the nonparametric part of the regression function into homogeneous and integrable components. It consists of two steps: In the first step we parameterize and fit the homogeneous component of the nonparametric part by the nonlinear least squares with other parametric terms in the model, and use in the second step the standard kernel method to nonparametrically estimate the integrable component of the nonparametric part from the residuals in the first step. We establish consistency and obtain the asymptotic distribution of our estimator. A simulation shows that our estimator performs well in finite samples. For the empirical illustration, we estimate the money demand functions for the US and Japan using our model and methodology.     

Leading Indicators for Euro‐area Inflation and GDP Growth*

In this paper, we evaluate the role of a set of variables as leading indicators for Euro‐area inflation and GDP growth. Our leading indicators are taken from the variables in the European Central Bank's (ECB) Euro‐area‐wide model database, plus a set of similar variables for the US. We compare the forecasting performance of each indicator ex post with that of purely autoregressive models. We also analyse three different approaches to combining the information from several indicators. First, ex post, we discuss the use as indicators of the estimated factors from a dynamic factor model for all the indicators. Secondly, within an ex ante framework, an automated model selection procedure is applied to models with a large set of indicators. No future information is used, future values of the regressors are forecast, and the choice of the indicators is based on their past forecasting records. Finally, we consider the forecasting performance of groups of indicators and factors and methods of pooling the ex ante single‐indicator or factor‐based forecasts. Some sensitivity analyses are also undertaken for different forecasting horizons and weighting schemes of forecasts to assess the robustness of the results.     

Regression Models with Data‐based Indicator Variables*

Ordinary least squares estimation of an impulse‐indicator coefficient is inconsistent, but its variance can be consistently estimated. Although the ratio of the inconsistent estimator to its standard error has a t‐distribution, that test is inconsistent: one solution is to form an index of indicators. We provide Monte Carlo evidence that including a plethora of indicators need not distort model selection, permitting the use of many dummies in a general‐to‐specific framework. Although White's (1980) heteroskedasticity test is incorrectly sized in that context, we suggest an easy alteration. Finally, a possible modification to impulse ‘intercept corrections’ is considered.     

Asymmetry and nonstationarity for a seasonal time series model

Tests for symmetry and seasonal unit roots are developed for an extended model of Hylleberg et al. (1990. Seasonal integration and cointegration. Journal Econometrics 44, 215–238.) which can represent both partial seasonal unit roots and threshold effects. Methods based on ordinary least squares (OLS) estimation and instrumental variable (IV) estimation are proposed and compared. For adjusting mean functions, ordinary mean adjustment and recursive mean adjustment are both considered. Several tests are constructed from various combination of estimation schemes and mean adjustment schemes. Among the tests, the tests based on IV-estimation are recommended because they have very simple limiting null distributions and have finite sample power properties comparable to those based on the OLSE. The recommended tests are applied to a US unemployment rate data set and find evidences for both nonstationarities associated with zero frequency and threshold effects.     

The effect of European integration on trade with the APEC countries: 1981–2000

This study examines whether the European Union (EU) integration has resulted in significant trade decrease with the three Asia-Pacific Economic Cooperation (APEC) sub-groups during 1981–2000: NAFTA, ASEAN, and NIC. To include all the trade data including those with zero data values, this study estimates the modified gravity model using the scaled ordinary least squares method. First, as expected, the EU countries have reduced trade with all three sub-groups, especially during 1996–2000. However, the ASEAN countries maintain a stable level of trade growth with the EU countries. Second, the results indicate that the EU, ASEAN, and NIC countries trade significantly more among themselves due to their respective integration schemes. (JEL F20, F100)     

RETRACTED ARTICLE: Prediction of economic growth by extreme learning approach based on science and technology transfer

The purpose of this research is to develop and apply the extreme learning machine (ELM) to forecast gross domestic product (GDP) growth rate. Economic growth may be developed on the basis on combination of different factors. In this investigation was analyzed the economic growth prediction based on the science and technology transfer. The main goal was to analyze the influence of number of granted European patents on the economic growth by field of technology. GDP was used as economic growth indicator. The ELM results are compared with genetic programming (GP) and artificial neural network (ANN). The reliability of the computational models were accessed based on simulation results and using several statistical indicators. Coefficient of determination for ELM method is 0.9841, for ANN method it is 0.7956 and for the GP method it is 0.7561. Based upon simulation results, it is demonstrated that ELM can be utilized effectively in applications of GDP forecasting.

     

Bias Reduction in Dynamic Panel Data Models by Common Recursive Mean Adjustment*

The within‐group estimator (same as the least squares dummy variable estimator) of the dominant root in dynamic panel regression is known to be biased downwards. This article studies recursive mean adjustment (RMA) as a strategy to reduce this bias for AR(p) processes that may exhibit cross‐sectional dependence. Asymptotic properties for N,T→∞ jointly are developed. When ( log ²T)(N/T)→ζ, where ζ is a non‐zero constant, the estimator exhibits nearly negligible inconsistency. Simulation experiments demonstrate that the RMA estimator performs well in terms of reducing bias, variance and mean square error both when error terms are cross‐sectionally independent and when they are not. RMA dominates comparable estimators when T is small and/or when the underlying process is persistent.     

Panel data models with spatially correlated error components

In this paper we consider a panel data model with error components that are both spatially and time-wise correlated. The model blends specifications typically considered in the spatial literature with those considered in the error components literature. We introduce generalizations of the generalized moments estimators suggested in Kelejian and Prucha (1999. A generalized moments estimator for the autoregressive parameter in a spatial model. International Economic Review 40, 509–533) for estimating the spatial autoregressive parameter and the variance components of the disturbance process. We then use those estimators to define a feasible generalized least squares procedure for the regression parameters. We give formal large sample results for the proposed estimators. We emphasize that our estimators remain computationally feasible even in large samples.     

Out-of-sample forecasts of China's economic growth and inflation using rolling weighted least squares

Macroeconomic forecasting in China is essential for the government to take proper policy decisions on government expenditure and money supply, among other matters. The existing literature on forecasting Chinas macroeconomic variables is unclear on the crucial issue of how to choose an optimal window to estimate parameters with rolling out-of-sample forecasts. This study fills this gap in forecasting economic growth and inflation in China, by using the rolling weighted least squares (WLS) with the practically feasible cross-validation (CV) procedure of Hong et al. (2018) to choose an optimal estimation window. We undertake an empirical analysis of monthly data on up to 30 candidate indicators (mainly asset prices) for a span of 17 years (2000–2017). It is documented that the forecasting performance of rolling estimation is sensitive to the selection of rolling windows. The empirical analysis shows that the rolling WLS with the CV-based rolling window outperforms other rolling methods on univariate regressions in most cases. One possible explanation for this is that these macroeconomic variables often suffer from structural changes due to changes in institutional reforms, policies, crises, and other factors. Furthermore, we find that, in most cases, asset prices are key variables for forecasting macroeconomic variables, especially output growth rate.     

Economic cohesion and development of the European Union's regions and member states - A methodological proposal to measure and identify the degree of regional economic cohesion

The EU cohesion policy seeks to mitigate imbalances between countries and regions and enhance greater economic development of the whole Union. However, notwithstanding its efforts and certain progress on this issue, territorial disparities still represent an open challenge for the European Union, which requires improving the cohesion policy, particularly regarding its measurement, in order to allocate resources more efficiently.This article aims to propose an innovative methodology to measure and identify the degree of cohesion of both EU NUTS-2 regions and member states in economic terms. To this end we first selected the main indicators which would better explain GDP per capita growth by applying factor analysis; and second, we measure the degree of cohesion as the relationship between economic development and its potential attributable to the inequalities detected in the selected set of indicators. To ensure the robustness of this research, we compared the results obtained by applying the Gini, Atkinson, and Theil indexes. Finally, the Economic Cohesion Index (ECI) has been elaborated in order to identify regions that, regardless of their economic development, still present a very low level of cohesion within the group in which it is compared.The main findings indicate that the 27 EU countries are quite cohesive. On the contrary, from the regional perspective, the study shows important imbalances between economic development and its potential that mainly affect the regions of Greece, Spain, and Italy, where Greece is the leading country among less developed regions and Spain and Italy among more developed.     

Endogenous selection or treatment model estimation

In a sample selection or treatment effects model, common unobservables may affect both the outcome and the probability of selection in unknown ways. This paper shows that the distribution function of potential outcomes, conditional on covariates, can be identified given an observed variable V$V$ that affects the treatment or selection probability in certain ways and is conditionally independent of the error terms in a model of potential outcomes. Selection model estimators based on this identification are provided, which take the form of simple weighted averages, GMM, or two stage least squares. These estimators permit endogenous and mismeasured regressors. Empirical applications are provided to estimation of a firm investment model and a schooling effects on wages model.     

北京地区与全国总体经济增长阶段性特点的比较分析

本文以GDP增长指数、消费者价格指数、商品零售价格指数等指标为对象,以全国总体为参照．研究了北京地区经济增长和经济波动的特点,以及与全国变动的同步性、相关性。其主要结论如下：（1）北京的经济增长速度高于全国;（2）在剔除1981年异常值后,北京年经济增长率分布的集中程度并不低于全国;（3）北京与全国的经济增长都表现出显著的阶段特征,北京经济增长路径可能发生了上移．而且上移的幅度可能要大于全国．上移的时间要早于全国。     

Correcting Standard Errors in Two‐stage Estimation Procedures with Generated Regressands*

Feenstra and Hanson [NBER Working Paper No. 6052 (1997)] propose a procedure to correct the standard errors in a two‐stage regression with generated dependent variables. Their method has subsequently been used in two‐stage mandated wage models [Feenstra and Hanson, Quarterly Journal of Economics (1999) Vol. 114, pp. 907–940; Haskel and Slaughter, The Economic Journal (2001) Vol. 111, pp. 163–187; Review of International Economics (2003) Vol. 11, pp. 630–650] and for the estimation of the sector bias of skill‐biased technological change [Haskel and Slaughter, European Economic Review (2002) Vol. 46, pp. 1757–1783]. Unfortunately, the proposed correction is negatively biased (sometimes even resulting in negative estimated variances) and therefore leads to overestimation of the inferred significance. We present an unbiased correction procedure and apply it to the models reported by Feenstra and Hanson (1999) and Haskel and Slaughter (2002) .     

Difference in difference meets generalized least squares: Higher order properties of hypotheses tests

We investigate the estimation and inference in difference in difference econometric models used in the analysis of treatment effects. When the innovations in such models display serial correlation, commonly used ordinary least squares (OLS) procedures are inefficient and may lead to tests with incorrect size. Implementation of feasible generalized least squares (FGLS) procedures is often hindered by too few observations in the cross-section to allow for unrestricted estimation of the weight matrix without leading to tests with similar size distortions as conventional OLS based procedures. We analyze the small sample properties of FGLS based tests with a formal higher order Edgeworth expansion that allows us to construct a size corrected version of the test. We also address the question of optimal temporal aggregation as a method to reduce the dimension of the weight matrix. We apply our procedure to data on regulation of mobile telephone service prices. We find that a size corrected FGLS based test outperforms tests based on OLS.     

India's Evolving Economic Model:

Abstract Over the course of the past 50 years, India has developed as a stable economy. Economic policies of the Indian government have guided and shaped India into a mixed economy. Political stability has been a significant factor in this process. The United States and European economic and political systems had a significant impact on evolution of India's economic model. Financial and economic reforms since 1991 have accelerated the pace of change toward an open market economy both in its internal operations and in its linkages with the global markets. India's economic future is now promising as it moves forward on its unique path of economic policy.     

Does Economic Integration Affect the Structure of Industries? Empirical Evidence from the CEE

In this paper we study the impacts of regional integration on the structure of industries in the Central and Eastern European (CEE) transition economies. Our empirical analysis is based on the economic geography framework, which is able to predict not only the industry location after integration, but also to capture other general equilibrium effects, such as transition to market economy, which turn out to be highly significant in the CEE. Our empirical results complement previous findings that industry location is strongly related to economic integration. We also find that CEE integration of the Council on Mutual Economic Assistance (CMEA) has distorted the industry location pattern predicted by the underlying economic geography theory. These distortions are higher in those regions that were more integrated in the CMEA. Our ex-ante simulation results suggest a convergence in regional specialisation after CEE integration with the EU.     

Networks and market-based measures of systemic risk: the European banking system in the aftermath of the financial crisis

This article investigates the behaviour of the European banking system during the financial crises that occurred in the last decades. Among the various approaches for measuring systemic risk, we consider network analysis, which describes the linkages among financial institutions and their whole structure. We construct a time-varying network of the European banking system. Banks are linked to form a global interconnected system and they mutually influence one another in terms of risk. We model their reciprocal influence via a weighted and directed network, in which weights are related to risk measures that are based on equity returns. Then, we apply two network indicators to investigate the prominence of a bank in spreading and receiving risk from the others. The results enable us to capture many features of the banking system while identifying the global systemically important banks. Moreover, the results of the analysis over time show how interconnections change over periods that are characterized by various economic scenarios.

     

The impact ofThe Wall Street Journal’s closely watched reports on short-term interest rates

Using a reduced-form macroeconomic model, suggested by the Real Economic Activity hypothesis, in this study the authors examine the effect of certain economic indicators, published weekly byThe Wall Street Journal, on interest rates, for the period November 1987–November 1989. The results suggest that unexpected M3 announcements significantly affect market interest rates. However, there is no evidence of significant announcement effects for the other government statistics (new jobless claims, auto sales, M1, and M2). The evidence suggests that financial markets have not considered information in these economic announcements to be particularly newsworthy, in the face of M3 announcements.     

Further Evidence on PPP Adjustment Speeds: the Case of Effective Real Exchange Rates and the EMS*

Two different approaches intend to resolve the ‘puzzling’ slow convergence to purchasing power parity (PPP) reported in the literature [see Rogoff (1996) , Journal of Economic Literature, Vol. 34.] On the one hand, there are models that consider a non‐linear adjustment of real exchange rate to PPP induced by transaction costs. Such costs imply the presence of a certain transaction band where adjustment is too costly to be undertaken. On the other hand, there are models that relax the ‘classical’ PPP assumption of constant equilibrium real exchange rates. A prominent theory put together by Balassa (1964, Journal of Political Economy, Vol. 72) and Samuelson (1964 Review of Economics and Statistics, Vol. 46) , the BS effect, suggests that a non‐constant real exchange rate equilibrium is induced by different productivity growth rates between countries. This paper reconciles those two approaches by considering an exponential smooth transition‐in‐deviation non‐linear adjustment mechanism towards non‐constant equilibrium real exchange rates within the EMS (European Monetary System) and effective rates. The equilibrium is proxied, in a theoretically appealing manner, using deterministic trends and the relative price of non‐tradables to proxy for BS effects. The empirical results provide further support for the hypothesis that real exchange rates are well described by symmetric, nonlinear processes. Furthermore, the half‐life of shocks in such models is found to be dramatically shorter than that obtained in linear models.