business cycle analysis with a markov switching model this article explores the hamilton markov switching model through an analysis of the business cycles of eight developed market economies forecasting and specification tests suggest only marginal improvements over linear autoregressive models yet filtered and smoothed conditional probabilities indicate turning points in business cycles that closely correlate with turning points from traditional methods tests regarding the asymmetry of business cycles reject the null of symmetry for most countries

predicting recessions with interest rate spreads a multicountry regime switching analysis abstract this study uses markov switching models to evaluate the informational content of the term structure as a predictor of recessions in eight oecd countries the empirical results suggest that for all countries the term spread is sensibly modelled as a two state regime switching process moreover a simple univariate model turns out to be a filter that transforms accurately term spread changes into turning point predictions the term structure is confirmed to be a reliable recession indicator however the results of probit estimations show that on average the markov switching filter does not improve the forecasting ability of the spread

decarbonization under green growth strategies the case of south korea the win win opportunities connected to green growth are appealing to academics and policy makers alike but empirical evaluations about the effectiveness of green growth policies are still scattered taking south korea as case study which set up a highly ambitious green growth program in 2009 our research casts light on the extent to which the korean green growth strategy has been effective in decarbonizing the economy our methodology combines decomposition analysis and econometrics with a review of energy and climate policies including related structural changes on the short term 2008 2012 most of the drivers displayed an enhancing effect on co2 emissions from fuel combustion with gdp per capita being the strongest driver from a historical perspective 1971 2012 findings reveal that important drivers such as energy and co2 intensity even worsened their effects during the first years under the green growth strategy regression statistics revealed that gdp per capita was in fact the driver with the most explanatory power for co2 emissions followed by energy intensity the korean policy mix of modest government support to low carbon energy technologies and a lack of complementary pricing policies did not deliver the targeted emissions reduction at least in the short term despite recent policy developments i e the introduction of a renewable portfolio standard in 2012 and an emissions trading system in 2015 several key policy challenges for decarbonization remain less

the index of leading indicators measurement without theory twenty five years later if the success of a specific approach to economic analysis can be measured by its longevity and continued popularity under a variety of environments then the use in business cycle forecasting of leading economic indicators as originally developed by wesley mitchell and arthur burns 1938 at the national bureau of economic research must stand near the top one might say peak of such successes this is rather remarkable in light of the criticism it has faced continually since koopmans 1947 in a review of burns and mitchell 1946 first decried the lack of any theory behind the nber business cycle methodology since its introduction the composite index of leading indicators now published monthly by the u s bureau of economic analysis bea has been widely monitored as a guide to future economic performance the use of leading indicators survived the period of advancement and demise of the structural approach to econometric forecasting and if anything has enjoyed a resurgence in recent years as more sophisticated time series techniques of measurement without theory have been developed in fact the relationship between the leading indicator approach and general time series methods of the type described by box and jenkins 1970 was formalized by sargent and sims 1977 who pointed out that forecasting with a composite index may be seen as the imposition of a specific set of restrictions on the vector autoregression containing the individual series included in the index from this standpoint it is possible to evaluate formally the index of leading indicators as a tool for prediction and such is the purpose of this paper there are two main questions to be answered

what do the leading indicators lead the authors find that the composite leading index cli is useful for forecasting gross national product gnp both in sample and in an out of sample real time exercise they propose a nonlinear specification in which cyclical shifts of the cli precede those in gnp however the authors find that better forecasts are provided by a simple linear relation between current gnp growth and the growth rate of the cli during the previous quarter along with an error correction term corresponding to the previous quarter s logarithmic difference between the level of the cli and the level of gnp copyright 1996 by university of chicago press

a two country model of stochastic output with changes in regime abstract this paper evaluates the transmission of business cycles between countries and presents evidence that world wide shocks dominate any transmission of business cycles i examine industrial production and look at the relationships of the united states with canada germany and the united kingdom in the process i present a specialized markov switching time series model and evaluate it relative to other more familiar stochastic models i show that for the data sets considered markov switching models have much better goodness of fit but larger forecast variance relative to arma or var models

predicting u s business cycle regimes this article examines the use of logistic classification methods for the identification and prediction of postwar u s business cycle expansion and contraction regimes as defined by the national bureau of economic research nber reference turning point dates we present a coherent theoretical framework for this task using measures of discriminatory information the analysis encompasses model selection parameter estimation and classification decision rules we examine the performance of logistic procedures in reproducing the nber regime classifications and in predicting one and three months ahead using leading indicator variables our models are shown to provide substantially more accurate business cycle regime predictions than markov switching specifications

bayes estimates of markov trends in possibly cointegrated series stylized facts show that average growth rates of u s per capita consumption and income differ in recession and expansion periods because a linear combination of such series does not have to be a constant mean process standard cointegration analysis between the variables to examine the permanent income hypothesis may not be valid to model the changing growth rates in both series we introduce a multivariate markov trend model that accounts for different growth rates in consumption and income during expansions and recessions and across variables within both regimes the deviations from the multivariate markov trend are modeled by a vector autoregression var model bayes estimates of this model are obtained using markov chain monte carlo methods the empirical results suggest the existence of a cointegration relation between u s per capita disposable income and consumption after correction for a multivariate markov trend this result is also obtained when per capita investment is added to the var

gdp growth and the composite leading index a nonlinear causality analysis for eleven countries abstract this paper examines the ability of the composite leading index of economic activity to predict future movements in gdp growth using a nonlinear granger causality test our empirical results are shown to contrast sharply with those from the conventional linear causality test

this is what the leading indicators lead we propose an optimal filter to transform the conference board composite leading index cli into recession probabilities in the us economy we also analyse the cli s accuracy at anticipating us output growth we compare the predictive performance of linear var extensions of smooth transition regression and switching regimes probit non parametric models and conclude that a combination of the switching regimes and non parametric forecasts is the best strategy at predicting both the nber business cycle schedule and gdp growth this confirms the usefulness of cli even in a real time analysis copyright 2002 john wiley sons ltd

model averaging in markov switching models predicting national recessions with regional data this paper introduces new weighting schemes for model averaging when one is interested in combining discrete forecasts from competing markov switching models in the empirical application we forecast u s business cycle turning points with state level employment data we find that forecasts obtained with our best combination scheme provide timely updates of u s recessions in that they outperform a notoriously difficult benchmark to beat the anxious index from the survey of professional forecasters for short term forecasts

constructing a new leading indicator for unemployment from a survey among german employment agencies the article investigates the predictive power of a new survey implemented by the federal employment agency fea for forecasting german unemployment in the short run every month the ceos of the fea s regional agencies are asked about their expectations of future labour market developments we generate an aggregate unemployment leading indicator that exploits serial correlation in response behaviour through identifying and adjusting temporarily unreliable predictions we use out of sample tests suitable in nested model environments to compare forecasting performance of models including the new indicator to that of purely autoregressive benchmarks for all investigated forecast horizons 1 2 3 and 6 months test results show that models enhanced by the new leading indicator significantly outperform their benchmark counterparts to compare our indicator to potential competitors we employ the model confidence set results reveal that models including the new indicator perform very well at the 10 level

measuring and predicting heterogeneous recessions this paper examines the usefulness of a more refined business cycle classification for monthly industrial production ip beyond the usual distinction between expansions and contractions univariate markov switching models show that a three regime model is more appropriate than a model with only two regimes interestingly the third regime captures severe recessions contrasting the conventional view that the additional third regime represents a recovery phase this is confirmed by means of markov switching vector autoregressive models that allow for phase shifts between the cyclical regimes of ip and the conference board s leading economic index lei the timing of the severe recession regime mostly corresponds with periods of substantial financial market distress and severe credit squeezes providing empirical evidence for the financial accelerator theory

economic development and environmental policy in turkey an institutionalist critique turkey has had a prolonged exemplary experience with economic development in this paper we will trace the emergence of an environmental concern within development planning in turkey after a brief theoretical discussion on rival frameworks it is our contention that whereas economic policies are planned to meet certain goals environmental policies are formulated after observing the results of these economic policies and only in a fragmented manner what is needed is an alternative comprehensive policy based on a theoretical framework in tune with karl polanyi gunnar myrdal william kapp and nicholas georgescu roegen in order to uplift the analysis and policy design to a comprehensive level copyright oxford university press

business cycle duration dependence a parametric approach this paper reexamines duration dependence in u s business cycles using parametric hazard models positive duration dependence would indicate that expansions or contractions are more likely to end as they become older this paper provides statistically significant evidence of positive duration dependence for expansions before world war ii and contractions after world war ii the evidence is stronger than in earlier research utilizing nonparametric techniques because certain nonparametric techniques have low statistical power against the type of duration dependence found in this paper evidence is also presented suggesting that expansions became longer on average after world war ii while contractions became shorter copyright 1991 by mit press

are statistical reporting agencies getting it right data rationality and business cycle asymmetry this article provides new evidence on the rationality of early releases of industrial production ip and producer price index ppi data rather than following the usual practice of examining only first available and fully revised data we examine the entire revision history for each variable thus we are able to assess for example whether earlier releases of data are in any sense less rational than later releases and when data become rational our findings suggest that seasonally unadjusted ip and ppi become rational after approximately 3 4 months whereas seasonally adjusted versions of these series remain irrational for at least 6 12 months after initial release for all variables examined we find evidence that the remaining revision is predictable from its own past or from publicly available information in other economic and financial variables in addition we find a clear increase in the volatility of revisions during recessions suggesting that early data releases are less reliable in toughe

measuring wellbeing performance of carbon emissions using hybrid measure and meta frontier techniques empirical tests for g20 countries and implications for china abstract a quantitative measure of performance that integrates national level carbon emission profiles with key parameters of social and economic wellbeing can provide an effective management tool for policy interventions this paper constructs non parametric evaluation and decomposition models using hybrid measure and meta frontier techniques the proposed models are employed to estimate the wellbeing performance of carbon emissions wpce and identify the sources of wpce inefficiency empirical analyses based on the group 20 g20 countries for the 2000 2015 period shows the worst performance is in the brics country group i e brazil russia india china and south africa known for rapid growth whereas the developed and the developing economies of the g20 group show positive trajectories of performance as for china its performance measure of wpce was the lowest among the g20 indicating significant potential for further improvement the analysis also highlights the role of managerial failure as the primary driver of inefficiency more important than technological inefficiency in explaining efficiency loss within the g20 countries this critical finding the relevance and importance of managerial capability suggests the need for a high level of attention to governance and managerial capacity to ensure continued improvements in performance on carbon emissions and human wellbeing a lesson to be drawn from this study is that china s policies to promote a low carbon economy can benefit from increased priority given to low carbon technological innovation for the energy system transition and enhancement of environmental governance and managerial capacity to achieve a long term sustainability

an econometric characterization of business cycle dynamics with factor structure and regime switching a dynamic factor model with regime switching is proposed as an empirical characterization of business cycles the approach integrates the idea of comovements among macroeconomic variables and asymmetries of business cycle expansions and contractions the first is captured with an unobservable dynamic factor and the second by allowing the factor to switch regimes the model is estimated by maximizing its likelihood function and the empirical results indicate that the combination of these two features leads to a successful representation of the data relative to extant literature this holds for within and out of sample and for both revised and real time data copyright 1998 by economics department of the university of pennsylvania and the osaka university institute of social and economic research association

optimal prediction of cyclical downturns abstract in the conventional way of visualizing economic time series the prediction problem does not dichotomize into predicting turning points and predicting conditional means yet the prediction of turning points has always been considered a separate issue in business cycle literature the paper first discusses these issues and then describes a model where the prediction of turning points does separate from the prediction of conditional means using the model an optimal way of predicting turning points is derived and applied to u s data the paper uses time series methodology and the theory of optimal stopping times

a nonparametric investigation of duration dependence in the american business cycle does the termination probability of a business expansion or contraction increase with age this question may be formally addressed by analyzing the nature of duration dependence in aggregate economic activity our null hypothesis is that there is no duration dependence which we test via intentionally nonparametric procedures we also argue that common notion of business cycle periodicity can be usefully interpreted in terms of whole cycle duration dependence we find some evidence for duration dependence in whole cycles and in prewar expansions but little evidence elsewhere

modelling business cycle movements in the uk economy this paper models the phases of the uk business cycle using gdp data with a time varying transition probabilities tvtp markov switching regime model and exogenous leading indicator variables single indicators in linear models are compared with the tvtp framework with logistic and exponential functions used in the latter the markov switching models capture the major recessions of the sample but the use of leading indicators through the tvtp framework can improve this regime recognition finally a forecast comparison shows that the tvtp models perform relatively well in predicting during the 1990s particularly when nominal interest rates are used to generate the regime switching probabilities

new index of coincident indicators a multivariate markov switching factor model approach abstract this paper extends the univariate markov switching unobserved component model to the multivariate markov switching factor model of coincident economic indicators an approximate ml method is developed to estimate the model the extracted markov switching factor may be interpreted as the coincident index and is comparable to the stock watson index which differs only by the markov switching component using four constituent series of the doc coincident index for the period january 1960 to june 1992 the proposed model generates recessionary and expansionary periods which are remarkably consistent with the nber chronology of the business cycles from the noisy monthly data this paper also finds that duration dependence does matter

the transmission mechanism in a changing world this paper aims at improving the understanding of the transmission of shocks across countries and how this transmission may have changed over time by employing a model that allows for parameter changes across regimes we show that transmission of shocks from the us to european countries may depend on the values of transition variables such as financial prices exchange rates international capital flows trade links and monetary policy instruments we also show that transmission mechanisms estimated with the proposed models have good performance in describing the 2001 downturn in some european countries as an effect of a us shock more generally the models have a good forecasting performance over short horizons