the predictive content of the output gap for inflation resolving in sample and out of sample evidence this paper evaluates potential explanations for the sometimes poor forecasting performance of the phillips curve one explanation is that out of sample metrics are noisy or equivalently have relatively low power another potential explanation is instability in the coefficients of the model to assess these forces this paper compares sample forecasting results to results from bootstrap simulations of models that either assume stability or allow breaks in the coefficients of the model this analysis indicates that a significant portion of the weakness of the out of sample evidence is attributable to power limitations but instabilities in the coefficients on the output gap also play a role

the fisher hypothesis and the forecastability and persistence of inflation abstract for the period 1860 to 1939 the simple correlation of the u s commercial paper rate with the contemporaneous inflation rate is 0 17 the corresponding correlation for the period 1950 to 1979 is 0 71 this paper attributes this apparent change in the fisher relation to differences in the stochastic process of inflation rather than a change in any structural relationship between interest rates and expected inflation contrary to recent claims in the literature there is little evidence of inflation non neutrality in data from the pre world war i period

debt dynamics in europe a network general equilibrium gvar approach in this work we investigate the dynamic interdependencies among the eu12 economies using a competitive general equilibrium network system representation additionally using bayesian techniques we estimate the autoregressive scheme that characterizes the equilibrium price system of the network while characterizing each economy node in the universe of our network in terms of its degree of pervasiveness in this context we unveil the dominant s unit s in our model and estimate the dynamic linkages between the economies nodes lastly in terms of robustness analysis we compare the findings of the degree pervasiveness of each economy against other popular quantitative methods in the literature according to our findings the economy of germany acts as weakly dominant entity in the eu12 economy meanwhile all shocks die out in the short run without any long lasting effect

earnings risk taking and capital accumulation in small and large community banks abstract we examine the relationships between ownership structure and both earnings and risk taking among community banks before during and after the us financial crisis we find that publicly held small community banks had lower earnings than privately held ones before the recession but had higher earnings during and after the recession publicly held small community banks exhibited similar risk taking to privately held ones before and during the recession but greater risk taking after we also find that publicly held small community banks de risked more slowly than privately held ones following the recession large community banks on the other hand show no consistent relationship between ownership structure and earnings and a strong cyclical relationship between ownership structure and risk taking these findings expand our understanding of how community bank performance and capital accumulation behaves through different cyclical periods and how ownership structure affects that behavior

economic mobility and poverty dynamics in developing countries this study provides an introduction to this special issue of the journal of development studies on economic mobility and poverty dynamics in developing countries in addition to providing a conceptual framework it outlines how the contributions fit into the extant literature a series of regularities emerge across these studies the poor consist of those who are always poor poor at all dates and those who move in and out of poverty with the latter group tending to be strikingly large such movements in and out of poverty are apparent when looking at poverty in either absolute or relative terms changes in returns to endowments can be a potent source of increased incomes finally seemingly transitory shocks can have long term consequences the study concludes by drawing out the policy implications of these regularities

intragenerational income mobility in latin america economic mobility has not been widely studied in developing countries until very recently owing to the lack of suitable data studying mobility requires longitudinal data tracking economic units that is individuals households or firms over time collecting this type of data is expensive and historically few latin american countries carried it out now however such data sets are available for a number of latin american and caribbean countries table a 1 in the appendix provides a list of available panel data sets that can be used for income mobility studies for these countries in this paper we discuss how the knowledge gleaned from mobility studies differs from comparable cross sectional analysis the structure of the paper is as follows the next section discusses what mobility is how it can be measured and how it differs from inequality the subsequent section reviews previous mobility studies in latin american countries the paper then summarizes the contributions of our own recent work and the final section discusses what lies ahead in mobility research for latin american economies

why has u s inflation become harder to forecast we examine whether the u s rate of price inflation has become harder to forecast and to the extent that it has what changes in the inflation process have made it so the main finding is that the univariate inflation process is well described by an unobserved component trend cycle model with stochastic volatility or equivalently an integrated moving average process with time varying parameters this model explains a variety of recent univariate inflation forecasting puzzles and begins to explain some multivariate inflation forecasting puzzles as well

sky islands squirrels and scopes the political economy of an environmental conflict siting conflicts involving endangered species create economic and political pressures on existing property rules a welfare model of institu tional change with endogenous transaction costs is used to analytically describe the environmental con flict surrounding the mount graham international observatory project emphasis is placed on the evolution of property rights under conditions of imperfect information this case study illustrates a the nature of scientific and economic incentives for institutional change due to technological inno vation and b the process of institutional choice in a policy environment characterized by biological uncertainty and economic power jel q2

does joint modelling of the world economy pay off evaluating global forecasts from a bayesian gvar we analyze how modeling international dependencies improves forecasts for the global economy based on a bayesian gvar with ssvs prior and stochastic volatility to analyze the source of performance gains we decompose the predictive joint density into its marginals and a copula term capturing the dependence structure across countries the gvar outperforms forecasts based on country specific models this performance is solely driven by superior predictions for the dependence structure across countries whereas the gvar does not yield better predictive marginal densities the relative performance gains of the gvar model are particularly pronounced during volatile periods and for emerging economies

forming priors for dsge models and how it affects the assessment of nominal rigidities abstract we discuss prior elicitation for the parameters of dynamic stochastic general equilibrium dsge models and provide a method for constructing prior distributions for a subset of these parameters from beliefs about steady state relationships and second moments of the endogenous variables the empirical application documents how the specification of the prior distribution affects our assessment of the relative importance of price and wage rigidities in a new keynesian dsge model

the welfare analytics of transaction costs externalities and institutional choice a natural result of the economist s participation in externality literature has been a strong emphasis upon market and price guided policies available theoretical foundations are insufficient for supporting this ideology absent is a conceptual framework which integrates institutional options and their attendant resource costs with what we know of resource and technology constraints this paper addresses this omission by incorporating an explicit role for transaction costs into traditional welfare diagrammatics

why does the yield curve predict output and inflation the slope of the yield curve has been shown empirically to be a significant predictor of inflation and real economic activity but there is no standard theory as to why the relationship exists this article constructs an analytical rational expectations model to investigate the reasons for the empirical results the model suggests that the relationships are not structural but are instead influenced by the monetary policy regime however the yield curve should have predictive power for output and inflation in most circumstances various implications of the theoretical model are tested and confirmed empirically

estimation of poverty transition matrices with noisy data summary this paper investigates measurement error biases in estimated poverty transition matrices we compare transition matrices based on survey expenditure data to transition matrices based on measurement error free simulated expenditure the simulation model uses estimates that correct for measurement error in expenditure we find that time varying measurement error in expenditure data magnifies economic mobility roughly 45 of households initially in poverty at time t 1 are found to be out of poverty at time t using data from the korean labor and income panel study when measurement error is removed this drops to between 26 and 31 of households initially in poverty copyright 2016 john wiley sons ltd

was vietnam s economic growth in the 1990s pro poor an analysis of panel data from vietnam abstractinternational aid agencies and almost all economists agree that economic growth is necessary for reducing poverty yet some economists question whether it is sufficient for poverty reduction vietnam enjoyed rapid economic growth in the 1990s but a modest increase in inequality during that decade raises the possibility that the poor in vietnam benefited little from that growth this article examines the extent to which vietnam s economic growth has been pro poor giving particular attention to two issues the first is the appropriate comparison group when comparing the poorest x of the population at two points in time should the poorest x in the first time period be compared to the poorest x in the second time period some of whom were not the poorest x in the first time period or to the same people in the second time period some of whom are no longer among the poorest x the second is measurement error estimates of growth among the poorest x of the population are likely to be biase

national pro poor spending programmes and their effect on income inequality and poverty evidence from vietnam abstractthis article examines the causal effects of the national pro poor targeted programmes ntps on both poverty incidence and inequality in vietnam over the period 2002 2010 while the links between ntps and poverty alleviation and income inequality have previously been analysed independently this study is the first to offer a comprehensive analysis of ntps expenditure on poverty and inequality simultaneously applying the system generalized method of moments estimator to a panel of vietnamese regional data we are unable to establish that ntps have significantly mitigated poverty incidence however we estimate that ntps have significantly increased inequality we offer possible explanations why the ntps have resulted in these unintended outcomes and discuss potential policies which can reduce both poverty and inequality

measuring uncertainty and its impact on the economy we propose a new framework for measuring uncertainty and its effects on the economy based on a large var model with errors whose stochastic volatility is driven by two common unobservable factors representing aggregate macroeconomic and financial uncertainty the uncertainty measures can also influence the levels of the variables so that contrary to most existing measures ours reflect changes in both the conditional mean and volatility of the variables and their impact on the economy can be assessed within the same framework moreover identification of the uncertainty shocks is simplified with respect to standard var based analysis in line with the favar approach and with heteroskedasticity based identification finally the model which is also applicable in other contexts is estimated with a new bayesian algorithm which is computationally efficient and allows for jointly modeling many variables while previous var models with stochastic volatility could only handle a handful of variables empirically we apply the method to estimate uncertainty and its effects using us data finding that there is indeed substantial commonality in uncertainty sizable effects of uncertainty on key macroeconomic and financial variables with responses in line with economic theory

tests of predictive ability for vector autoregressions used for conditional forecasting summary many forecasts are conditional in nature for example a number of central banks routinely report forecasts conditional on particular paths of policy instruments even though conditional forecasting is common there has been little work on methods for evaluating conditional forecasts this paper provides analytical monte carlo and empirical evidence on tests of predictive ability for conditional forecasts from estimated models in the empirical analysis we examine conditional forecasts obtained with a var in the variables included in the dsge model of smets and wouters american economic review 2007 97 586 606 throughout the analysis we focus on tests of bias efficiency and equal accuracy applied to conditional forecasts from var models copyright 2016 john wiley sons ltd

evaluating real time var forecasts with an informative democratic prior this paper proposes bayesian forecasting in a vector autoregression using a democratic prior this prior is chosen to match the predictions of survey respondents in particular the unconditional mean for each series in the vector autoregression is centered around long horizon survey forecasts heavy shrinkage toward the democratic prior is found to give good real time predictions of a range of macroeconomic variables as these survey projections are good at quickly capturing endpoint shifts

a generalized valuation model for fixed rate residential mortgages this paper uses option pricing techniques to rationally price mortgage instruments subject to both default and prepayment risk attention is given to terminations due to purely financial consideration of the mortgage contract itself as well as to personally induced terminations explicit inclusion of default in the mortgage valuation procedure also permits the valuation of insurance against such default qualitatively it is found that default differs significantly in the behavior from either prepayment or nonfinancial termination quantitatively however there is significant substitution between prepayment and default so that the addition of a default feature to the contract has only a modest impact on mortgage values unless there is substantial price volatility in the housing market or a high loan to value ratio copyright 1992 by ohio state university press

how much of observed economic mobility is measurement error iv methods to reduce measurement error bias with an application to vietnam research on economic growth and inequality inevitably raise issues concerning economic mobility because the relationship between long run inequality and short run inequality is mediated by income mobility for a given level of short run inequality greater mobility implies lower long run inequality yet empirical measures of both inequality and mobility are biased upward due to measurement error in income and expenditure data collected from household surveys this paper presents a straightforward method to remove this bias using instrumental variable estimates the method is applied to panel data from vietnam the results imply that at least 15 and perhaps as much as 42 of measured mobility is upward bias due to measurement error the results also suggest that measurement error accounts for at least 12 of measured inequality

capital constraints and the performance of entrepreneurial firms in vietnam entrepreneurship has been among the key driving forces of the emergence of a dynamic private sector during the recent decades in vietnam this article addresses for vietnam the questions how capital constraints affect the performance of family firms and how entrepreneurs human and social capital interact with capital constraints to leverage entrepreneurial income a panel of 1721 firms in 4 years is used results are consistent with the resource dependency approach indicating an adverse effect of capital constraints on firm performance firms suffering capital constraints perform substantially better suggesting that they need more capital simply to finance newly recognized profit opportunities human capital plays a vital role in relaxing capital constraints and improves the entrepreneurial performance whereas the effect of social capital stemming from strong ties and weak ties is limited strong ties bring emotional support and weak ties give nonfinancial benefits from regular and useful business contacts advanced econometric analysis tools to take into account the endogeneity of capital constraints are used to establish relationships among relevant variables

dynamic effects of credit shocks in a data rich environment we examine the dynamic effects of credit shocks using a large dataset of u s economic and financial indicators in a structural factor model an identified credit shock resulting in an unanticipate

asymmetric volatility spillovers between uk regional worker flows and vacancies this paper investigates volatility spillovers between uk regional job finding job separation and vacancy rates employing a large bayesian logistic smooth transition vector autoregression var model we find high volatility spillovers between uk regional labour markets analyses of net spillovers show that in general shocks to job separation rates tend to spread into job finding and vacancy rates while vacancy rates are usually at the receiving end of shocks transmitted from the job separations and job findings to shed further light on the shock propagation mechanism we also look into more detailed matters such as the differences in spillovers between regions within the same regime and that of the same region but in different regimes

reconciling output gaps unobserved components model and hodrick prescott filter abstract this paper reconciles two widely used trend cycle decompositions of gdp that give markedly different estimates the correlated unobserved components model yields output gaps that are small in amplitude whereas the hodrick prescott hp filter generates large and persistent cycles by embedding the hp filter in an unobserved components model we show that this difference arises due to differences in the way the stochastic trend is modeled moreover the hp filter implies that the cyclical components are serially independent an assumption that is decidedly rejected by the data by relaxing this restrictive assumption the augmented hp filter provides comparable model fit relative to the standard correlated unobserved components model

testing macroprudential stress tests the risk of regulatory risk weights we compare the capital shortfall measured by regulatory stress tests to that o fa benchmark methodology the v lab stress test that employs only publicly available market data we find that when capital shortfalls are measured relative to risk weighted assets the ranking of financial institutions is not well correlated to the ranking of the v lab stress test whereas rank correlations increase when required capitalization is a function of total assets we show that the risk measures used in risk weighted assets are cross sectionally uncorrelated with market measures of risk as they do not account for the risk that risk will change furthermore the banks that appeared to be best capitalized relative to risk weighted assets were no better than the rest when the european economy deteriorated into the sovereign debt crisis in 2011

assessing financial stability the capital and loss assessment under stress scenarios class model abstract the class model is a top down capital stress testing framework that uses public data simple econometric models and auxiliary assumptions to project the effect of macroeconomic scenarios on u s banking firms through the lens of the model we find that the total banking system capital shortfall under stressful macroeconomic conditions began to rise 4 years before the financial crisis peaking in the fourth quarter of 2008 the capital gap has since fallen sharply and is now significantly below pre crisis levels in the cross section banking firms estimated to be most sensitive to macroeconomic conditions also have higher capital ratios consistent with a precautionary view of bank capital though this behavior is evident only since the crisis we interpret our results as evidence that the resiliency of the u s banking system has improved since the financial crisis and also as an illustration of the value of stress testing as a macroprudential policy tool

an empirical investigation of direct and iterated multistep conditional forecasts when constructing unconditional point forecasts both direct and iterated multistep dms and ims approaches are common however in the context of producing conditional forecasts ims approaches based on vector autoregressions var are far more common than simpler dms models this is despite the fact that there are theoretical reasons to believe that dms models are more robust to misspecification than are ims models in the context of unconditional forecasts marcellino stock and watson msw 2006 investigate the empirical relevance of these theories in this paper we extend that work to conditional forecasts we do so based on linear bivariate and trivariate models estimated using a large dataset of macroeconomic time series over comparable samples our results reinforce those in msw the ims approach is typically a bit better than dms with significant improvements only at longer horizons in contrast when we focus on the great moderation sample we find a marked improvement in the dms approach relative to ims the distinction is particularly clear when we forecast nominal rather than real variables where the relative gains can be substantial

is india s economic growth leaving the poor behind there has been much debate about how much india s poor have shared in the economic growth unleashed by economic reforms in the 1990s the authors argue that india has probably maintained its 1980s rate of poverty reduction in the 1990s however there is considerable diversity in performance across states this holds some important clues for understanding why economic growth has not done more for india s poor india s economic growth in the 1990s has not been occurring in the states where it would have the most impact on poverty nationally if not for the sectoral and geographic imbalance of growth the national rate of growth would have generated a rate of poverty reduction that was double india s historical trend rate states with relatively low levels of initial rural development and human capital development were not well suited to reduce poverty in response to economic growth the study s results are consistent with the view that achieving higher aggregate economic growth is only one element of an effective strategy for poverty reduction in india the sectoral and geographic composition of growth is also important as is the need to redress existing inequalities in human resource development and between rural and urban areas

regional output growth in the united kingdom more timely and higher frequency estimates from 1970 output growth estimates for the regions of the uk are currently published at the annual frequency only and are released with a long delay regional economists and policymakers would benefit from having higher frequency and more timely estimates in this paper we develop a mixed frequency vector autoregressive mf var model and use it to produce estimates of quarterly regional output growth temporal and cross sectional restrictions are imposed in the model to ensure that our quarterly regional estimates are consistent with the annual regional observations and the observed quarterly uk totals we use a machine learning method based on the hierarchical dirichlet laplace prior to ensure optimal shrinkage and parsimony in our over parameterised mf var thus this paper presents a new regional quarterly database of nominal and real gross value added dating back to 1970 we describe how we update and evaluate these estimates on an ongoing quarterly basis to publish online at www escoe ac uk regionalnowcasting more timely estimates of regional economic growth we illustrate how the new quarterly data can be used to contribute to our historical understanding of business cycle dynamics and connectedness between regions

the persistence of inflation in the united states abstract has the persistence of inflation in the united states changed since 1965 we estimate the persistence of inflation over time using different measures and estimation procedures and we produce confidence intervals for our estimates as well as formal tests of unchanged persistence we find that inflation persistence has been high and approximately unchanged in the u s over our sample period we reconcile our results with other studies that reached different conclusions

have standard vars remained stable since the crisis small or medium scale vars are commonly used in applied macroeconomics for forecasting and evaluating the shock transmission mechanism this requires the var parameters to be stable over the evaluation and forecast sample or to explicitly consider parameter time variation the earlier literature focused on whether there were sizable parameter changes in the early 1980s in either the conditional mean or variance parameters and in the subsequent period till the beginning of the new century in this paper we conduct a similar analysis but focus on the effects of the recent crisis using a range of techniques we provide substantial evidence against parameter stability the evolution of the unemployment rate seems particularly different relative to its past behavior we then discuss and evaluate alternative methods to handle parameter instability in a forecasting context while none of the methods clearly emerges as best some techniques turn out to be useful to improve the forecasting performance

capital shortfall a new approach to ranking and regulating systemic risks the financial crisis of 2007 2009 has given way to the sovereign debt crisis of 2010 2012 yet many of the banking issues remain the same we discuss a method to estimate the capital that a financial firm would need to raise if we have another financial crisis this measure of capital shortfall is based on publicly available information but is conceptually similar to the stress tests conducted by us and european regulators we argue that this measure summarizes the major characteristics of systemic risk and provides a reliable interpretation of the past and current financial crises

common drifting volatility in large bayesian vars the general pattern of estimated volatilities of macroeconomic and financial variables is often broadly similar we propose two models in which conditional volatilities feature comovement and study them using u s macroeconomic data the first model specifies the conditional volatilities as driven by a single common unobserved factor plus an idiosyncratic component we label this model bvar with general factor stochastic volatility bvar gfsv and we show that the loss in terms of marginal likelihood from assuming a common factor for volatility is moderate the second model which we label bvar with common stochastic volatility bvar csv is a special case of the bvar gfsv in which the idiosyncratic component is eliminated and the loadings to the factor are set to 1 for all the conditional volatilities such restrictions permit a convenient kronecker structure for the posterior variance of the var coefficients which in turn permits estimating the model even with large datasets while perhaps misspecifi

estimating and accounting for the output gap with large bayesian vector autoregressions we consider how to estimate the trend and cycle of a time series such as real gross domestic product given a large information set our approach makes use of the beveridge nelson decomposition based on a vector autoregression but with two practical considerations first we show how to determine which conditioning variables span the relevant information by directly accounting for the beveridge nelson trend and cycle in terms of contributions from different forecast errors second we employ bayesian shrinkage to avoid overfitting in finite samples when estimating models that are large enough to include many possible sources of information an empirical application with up to 138 variables covering various aspects of the us economy reveals that the unemployment rate inflation and to a lesser extent housing starts aggregate consumption stock prices real money balances and the federal funds rate contain relevant information beyond that in output growth for estimating the output gap with estimates largely robust to substituting some of these variables or incorporating additional variables

forecasting with medium and large bayesian vars this paper is motivated by the recent interest in the use of bayesian vars for forecasting even in cases where the number of dependent variables is large in such cases factor methods have been traditionally used but recent work using a particular prior suggests that bayesian var methods can forecast better in this paper we consider a range of alternative priors which have been used with small vars discuss the issues which arise when they are used with medium and large vars and examine their forecast performance using a us macroeconomic dataset containing 168 variables we find that bayesian vars do tend to forecast better than factor methods and provide an extensive comparison of the strengths and weaknesses of various approaches typically we find that the simple minnesota prior forecasts well in medium and large vars which makes this prior attractive relative to computationally more demanding alternatives our empirical results show the importance of using forecast metrics based on the entire predictive density instead of relying solely on those based on point forecasts

forecasting core inflation the case of south africa forecasting and estimating core inflation has recently gained attention especially for inflation targeting countries following research showing that targeting headline inflation may not be optimal a central bank can miss the signal due to the noise despite its importance there is sparse literature on estimating and forecasting core inflation in south africa with the focus still on measuring core inflation this paper emphasises predicting core inflation using large time varying parameter vector autoregressive models tvp vars factor augmented var and structural break models using quarterly data from 1981q1 to 2013q4 we use mean squared forecast errors msfe and predictive likelihoods to evaluate the forecasts in general we find that i small tvp vars consistently outperform all other models ii models where the errors are heteroscedastic do better than models with homoscedastic errors iii models assuming that the forgetting factor remains 0 99 throughout the forecast period outperforms models that allow for the forgetting factors to change with time and iv allowing for structural break does not improve the predictability of core inflation overall our results imply that additional information on the growth rate of the economy and interest rate is sufficient to forecast core inflation accurately but the relationship between these three variables needs to be modelled in a time varying nonlinear fashion

asymmetric responses of consumer spending to energy prices a threshold var approach abstract we document asymmetric responses of consumer spending to energy price shocks using a threshold vector autoregressive model estimated with bayesian methods on u s data with high and low real energy inflation regimes positive energy price shocks have a greater negative effect on consumption compared with the increase in consumption that arises from negative energy price shocks for large shocks the cumulative consumption responses are three to five times greater for positive than negative shocks in disaggregated spending data the asymmetric responses are strongest for durable goods consumption but asymmetries are also present in the responses of nondurables and services consumption

identification issues in forward looking models estimated by gmm with an application to the phillips curve limited information methods are commonly used to estimate forward looking models with rational expectations such as the new keynesian phillips curve of gal and gertler 1999 in this paper we address issues of identiflcation that have been overlooked due to the incompleteness of the single equation formulation we show that problems of weak instruments may arise depending on the properties of the exogenous variables and that they are empirically relevant we also uncover a link between identiflcation and dynamic mis speciflcation and examine the lack of power of hansen s 1982 j test to detect invalid over identifying restrictions with regards to the new phillips curve we flnd that problems of identiflcation cannot be ruled out and they deserve further attention

oil shocks and stock volatility new evidence via a bayesian graph based var approach abstractthis article examines the temporal dependence between three oil shocks and realized volatility in the stock markets of g20 countries between 1994 and 2019 by applying a novel graphical b

assessing the effects of unconventional monetary policy and low interest rates on pension fund risk incentives this study quantifies the effects of persistently low interest rates near to the zero lower bound and unconventional monetary policy on pension fund risk incentives in the united states using two structural vector autoregressive var models and a counterfactual scenario analysis the results show that monetary policy shocks as identified by changes in treasury yields following changes in the central bank s target interest rates lead to a substantial increase in pension funds allocation to equity assets notably the shift from bonds to equity securities is greater during the period where the us federal reserve launched unconventional monetary policy measures additional findings show a positive correlation between pension fund risk taking low interest rates and the decline in treasury yields across both well funded and underfunded public pension plans which is thus consistent with a structural risk shifting incentive

large time varying parameter vars a nonparametric approach in this paper we introduce a non parametric estimation method for a large vector autoregression var with time varying parameters the estimators and their asymptotic distributions are available in closed form this makes the method computationally efficient and capable of handling information sets as large as those typically handled by factor models and factor augmented vars favar when applied to the problem of forecasting key macroeconomic variables the method outperforms constant parameter benchmarks and large parametric bayesian vars with time varying parameters the tool can also be used for structural analysis as an example we study the time varying effects of oil price innovations on sectoral u s industrial output we find that durable consumer goods and durable materials which together account for slightly more than one fifth of total industrial output play a key role in explaining the changing interaction between unexpected oil price increases and u s business cycle fluctuations

monetary policy shocks and foreign investment income evidence from a large bayesian var abstract this paper assesses the transmission of monetary policy in a large bayesian vector autoregression based on the approach proposed by banbura et al 2010 the paper analyzes the impact of monetary policy shocks in the united states and canada not only on a range of domestic aggregates trade flows and exchange rates but also foreign investment income the analysis provides three main results first a surprise monetary policy contraction has a statistically important impact on both gross and net foreign investment income flows in both countries against the background of growing foreign wealth and investment income this result provides preliminary evidence that foreign balance sheet channels might play an increasingly significant role for monetary transmission second the impact of monetary policy on foreign investment income flows differs considerably across asset categories and over time suggesting that the investment instruments and the currency denomination of a country s foreign assets and liabilities are potentially relevant for the way in which monetary policy affects the domestic economy finally the results support existing evidence on the effectiveness of large vector autoregressions and the bayesian shrinkage approach in eliminating price and exchange rate puzzles

measurement error bias in estimates of income and income growth among the poor analytical results and a correction formula abstract in any population income growth among the poor may be higher or lower than overall income growth estimates of income growth among the poor are almost always based on household surveys but income and expenditure data in those surveys are almost always measured with error this article uses the assumption that income follows a lognormal distribution to demonstrate that such measurement error can lead to biased estimates of the mean income of the poor and the growth of that mean income over time in particular when both income and the measurement error are lognormally distributed i measurement error leads to underestimation of the mean income among the poor at any point in time ii increases decreases in measurement error over time for a given level of inequality lead to underestimation overestimation of income growth among the poor and iii increases decreases in inequality over time for a given level of measurement error lead to overestimation underestimation of income growt

hierarchical markov normal mixture models with applications to financial asset returns motivated by the common problem of constructing predictive distributions for daily asset returns over horizons of one to several trading days this article introduces a new model for time series this model is a generalization of the markov normal mixture model in which the mixture components are themselves normal mixtures and it is a specific case of an artificial neural network model with two hidden layers the article uses the model to construct predictive distributions of daily s p 500 returns 1971 2005 and one year maturity bond returns 1987 2007 for these time series the model compares favorably with arch and stochastic volatility models the article concludes by using the model to form predictive distributions of one to ten day returns during volatile episodes for the s p 500 and bond return series copyright 2010 john wiley sons ltd

real time forecasting with a mixed frequency var this article develops a vector autoregression var for time series which are observed at mixed frequencies quarterly and monthly the model is cast in state space form and estimated with bayesian methods under a minnesota style prior we show how to evaluate the marginal data density to implement a data driven hyperparameter selection using a real time dataset we evaluate forecasts from the mixed frequency var and compare them to standard quarterly frequency var and to forecasts from midas regressions we document the extent to which information that becomes available within the quarter improves the forecasts in real time this article has online supplementary materials

modeling effort and lobbying in a heterogeneous common pool resource setting extraction from a common pool resource cpr such as a fishery can lead to socially inefficient and undesirable outcomes as a result of appropriation problems through regulation of the cpr users may achieve a more profitable and socially efficient outcome feeny et al land econ 72 2 187 205 1996 delve into some of the assumptions made by traditional cpr models and how use of those assumptions provides an incomplete framework to guide fishery policy in the theoretical models we develop we relax two of those assumptions cpr users are homogeneous and are unable to create or influence management of the resource we note a case where regulation of the individual user can induce others to harvest more and relate this to the slippage effect concept regarding resource sustainability we document situations where incentives exist for cpr users to lobby for tighter regulations and thus increase profits by limiting the tragedy of the commons influence

do financial variables help forecasting inflation and real activity in the euro area abstract this paper uses a large data set consisting of 447 monthly macroeconomic time series concerning the main countries of the euro area to simulate out of sample predictions of the euro area industrial production and the harmonized inflation index and to evaluate the role of financial variables in forecasting we considered two models which allow forecasting based on large panels of time series forni et al rev econom statist 82 2000 540 mimeo 2001b and stock and watson mimeo 1999 performance of both models were compared to that of a simple univariate ar model results show that multivariate methods outperform univariate methods for forecasting inflation at one three six and twelve months and industrial production at one and three months we find that financial variables do help forecasting inflation but do not help forecasting industrial production

priors from general equilibrium models for vars abstract this paper uses a simple new keynesian dynamic stochastic general equilibrium model as a prior for a vector autoregression shows that the resulting model is competitive with standard benchmarks in terms of forecasting and can be used for policy analysis jel classification c11 c32 c53 key words bayesian analysis dsge models forecasting vector autoregressions 1 introduction dynamic stochastic general equilibrium dsge models are popular nowadays in macroeconomics they are taught in virtually every economics ph d program and represent a predominant share of publications in the field yet when it comes to policy making these models are scarcely used at least from a quantitative point of view the main quantitative workhorse for policy making at the federal reserve system is frb us a macro econometric model built in the cowles foundation tradition a style of macroeconomics that is no longer taught in many ph d programs 1 in their decision process fed policy makers rely heavily on forecasting they want to know the expected path of inflation in the next few quarters and by how much a 50 basis point increase in the federal funds rate would affect that path frb us offers answers to these questions answers that many macroeconomists would regard with suspicion given both the lucas 1976 critique and the fact that in general the restrictions imposed by cowles foundation models are at odds with dynamic general equilibrium macroeconomics sims 1980 general equilibrium models on the other hand have a hard time providing alternative answers the fact that these models are perceived to do badly in terms of forecasting as they are scarcely parameterized is perhaps one of the reasons why they are not at the forefront of policy making while progress is being made in the development of dsge models that deliver acceptable forecasts e g smets and wouters 2003 this paper proposes an approach that combines a stylized general equilibrium model with a vector autoregression var to obtain a specification that both forecasts well and is usable for policy analysis specifically the approach involves using prior information coming from a dsge model in the estimation of a vector autoregression loosely speaking this prior can be thought of as the result of the following exercise i simulate time series data from the dsge model ii fit a var to these data in practice we replace the sample moments of the simulated data by population moments computed from the dsge model solution since the dsge model depends on unknown structural parameters we use a hierarchical prior in our analysis by placing a distribution on the dsge model parameters a tightness parameter controls the weight of the dsge model prior relative to the weight of the actual sample markov chain monte carlo methods are used to generate draws from the joint posterior distribution of the var and dsge model parameters the paper shows that the approach makes even a fairly stylized new keynesian dsge model competitive with standard benchmarks in terms of forecasting real output growth inflation and the nominal interest rate the three variables that are of most interest to policy makers 2 up to this point our procedure borrows from the work of dejong ingram and whiteman 1993 and ingram and whiteman 1994 who are the first to use priors from dsge models for vars ingram and whiteman showed that prior information from the bare bones stochastic growth model of king plosser and rebelo 1988 is helpful in forecasting real economic activity such as output consumption investment and hours worked in addition to documenting the forecasting performance of a trivariate var with a prior derived from a monetary dsge model this paper makes two contributions that significantly extend the earlier work first we show formally how posterior inference for the var parameters can be translated into posterior inference for dsge model parameters

bank funding costs in a rising interest rate environment one key risk to the banking system is how funding costs will change as monetary policy is normalized and interest rates rise after almost a decade of near zero rates our contribution is to develop a model that jointly estimates banks balance sheets and retail interest rates to arrive at a consistent estimate of the change in bank funding costs as market rates change our estimates imply a 100 basis point shock to the federal funds rate would increase overall deposit funding costs by about 40 billion which is roughly equal to 25 of aggregate annual net income for commercial banks and savings institutions we also find that deposit rate responses are largely symmetric in contrast to some previous research showing deposit rates are less responsive to upward movements in reference rates we introduce unique and confidential data on bank deposit betas to anchor our results

has the development gap between the ethnic minority and majority groups narrowed in vietnam evidence from household surveys using household data for rural northern vietnam between 1993 and 2014 we find that the ethnic minority group continued to lag behind the majority group in various development indicators despite the overall improvement in living standards our regression and decomposition analyses show that the structural differences between the two groups are an important cause of persistent development gap however the nature of structural differences changed over time and no single source of structural difference explains the persistent gap we argue that more minority appropriate policies are needed to lift poor minority households out of poverty further and reduce the development gap

forecasting inflation in a small open developing economy abstractthe transition to a model based forecasting environment is encountered by hurdles in a small open developing economy an attempt is made to validate the benefits of model based inflation fo

model uncertainty in panel vector autoregressive models we develop methods for bayesian model averaging bma or selection bms in panel vector autoregressions pvars our approach allows us to select between or average over all possible combinations of restricted pvars where the restrictions involve interdependencies between and heterogeneities across cross sectional units the resulting bma framework can find a parsimonious pvar specification thus dealing with overparameterization concerns we use these methods in an application involving the euro area sovereign debt crisis and show that our methods perform better than alternatives our findings contradict a simple view of the sovereign debt crisis which divides the euro zone into groups of core and peripheral countries and worries about financial contagion within the latter group

measurement error and earnings dynamics some estimates from the psid validation study previous empirical work on measurement error in survey earnings data has shown that the variance of the measurement error is roughly constant over time it is negatively correlated with true earnings and it is autocorrelated with previous measurement errors this article proposes a simple model in which the measurement error stems from underreporting of transitory earnings fluctuations and a white noise component the model fits well to data from the panel study of income dynamics validation study the results imply that autocorrelations in the changes of earnings can be estimated relatively accurately despite the presence of measurement error

macroeconomic forecasting using diffusion indexes this article studies forecasting a macroeconomic time series variable using a large number of predictors the predictors are summarized using a small number of indexes constructed by principal component analysis an approximate dynamic factor model serves as the statistical framework for the estimation of the indexes and construction of the forecasts the method is used to construct 6 12 and 24 monthahead forecasts for eight monthly u s macroeconomic time series using 215 predictors in simulated real time from 1970 through 1998 during this sample period these new forecasts outperformed univariate autoregressions small vector autoregressions and leading indicator models

short term interest rates as predictors of inflation on testing the hypothesis that the real rate of interest is constant in an innovative and provocative paper in this review eugene fama presents evidence which appears to be consistent with the joint hypothesis that the real rate of interest ignoring taxes is a constant and that the market for u s treasury bills is efficient in the sense of embodying rational expectations those findings are strikingly at variance with a long list of previous studies which seemed to support the view that the real rate of interest varies over time and can be related to economic variables such as real output monetary policy and so forth 2 the methodologies which lead to these contradictory conclusions are quite different previous studies had followed the lead of irving fisher by relating nominal interest rates to distributed lags on past rates of inflation 3 these distributed lags are generally interpreted as approximations to the market s expected rate of inflation and thus any remaining variation is attributed to variation in the real rate 4 fama s methodology on the other hand draws on the fact that the difference between the market interest rate and the subsequently observed rate of inflation the ex post real interest rate consists by definition of the ex ante real interest rate plus a pure forecasting error 5 the hypothesis of market efficiency implies that these forecasting errors must be serially random thus observing ex post real rates is equivalent to observing ex ante real rates with random measurement error these errors of course confound the problem of identifying variation in the ex ante real rate in this paper we will argue that the relative magnitude of these measurement errors is such that the tests carried out by fama were not powerful enough to reject the joint hypothesis that the ex ante real rate is a constant and expectations are rational more powerful tests which are presented in this paper using the same data do lead to rejection of that hypothesis of course rejection