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Selection of estimation window in the presence of breaks
 Journal of Econometrics
, 2007
"... In situations where a regression model is subject to one or more breaks it is shown that it can be optimal to use prebreak data to estimate the parameters of the model used to compute outofsample forecasts. The issue of how best to exploit the tradeo that might exist between bias and forecast er ..."
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Cited by 32 (6 self)
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In situations where a regression model is subject to one or more breaks it is shown that it can be optimal to use prebreak data to estimate the parameters of the model used to compute outofsample forecasts. The issue of how best to exploit the tradeo that might exist between bias and forecast error variance is explored and illustrated for the multivariate regression model under the assumption of strictly exogenous regressors. In practice when this assumption cannot be maintained and both the time and size of the breaks are unknown the optimal choice of the observation window will be subject to further uncertainties that make exploiting the biasvariance tradeo di cult. To that end we propose a new set of crossvalidation methods for selection of a single estimation window and weighting or pooling methods for combination of forecasts based on estimation windows of di erent lengths. Monte Carlo simulations are used to show when these procedures work well compared with methods that ignore the presence of breaks. JEL Classi cations: C22, C53.
Crises and recoveries in an empirical model of consumption disasters. Working Paper 15920
, 2010
"... We estimate an empirical model of consumption disasters using a new panel data set on consumption for 24 countries and more than 100 years. The model allows for permanent and transitory effects of disasters that unfold over multiple years. It also allows the timing of disasters to be correlated acro ..."
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Cited by 28 (2 self)
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We estimate an empirical model of consumption disasters using a new panel data set on consumption for 24 countries and more than 100 years. The model allows for permanent and transitory effects of disasters that unfold over multiple years. It also allows the timing of disasters to be correlated across countries. We estimate the model using Bayesian methods. Our estimates imply that the average length of disasters is roughly 5 years and that more than half of the short run impact of disasters on consumption are reversed in the long run on average. We investigate the asset pricing implications of these rare disasters. In a model with power utility and standard values for risk aversion, stocks surge at the onset of a disaster due to agents ’ strong desire to save. This causes a low equity premium, especially in normal times. In contrast, a model with EpsteinZinWeil preferences and an intertemporal elasticity of substitution equal to 2 yields a sizeable equity premium in normal times for modest values of risk aversion.
Detecting and Predicting Forecast Breakdowns
, 2008
"... We propose a theoretical framework for assessing whether a forecast model estimated over one period can provide good forecasts over a subsequent period. We formalize this idea by defining a forecast breakdown as a situation in which the outofsample performance of the model, judged by some loss fun ..."
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Cited by 17 (1 self)
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We propose a theoretical framework for assessing whether a forecast model estimated over one period can provide good forecasts over a subsequent period. We formalize this idea by defining a forecast breakdown as a situation in which the outofsample performance of the model, judged by some loss function, is significantly worse than its insample performance. Our framework, which is valid under general conditions, can be used not only to detect past forecast breakdowns but also to predict future ones. We show that main causes of forecast breakdowns are instabilities in the data generating process and relate the properties of our forecast breakdown test to those of structural break tests. The empirical application finds evidence of a forecast breakdown in the Phillips’ curve forecasts of U.S. inflation, and links it to inflation volatility and to changes in the monetary policy reaction function of the Fed.
2007): “Efficient Bayesian Inference for Multiple ChangePoint and Mixture Innovation Models,” forthcoming
 Journal of Business and Economic Statistics
"... Time series subject to parameter shifts of random magnitude and timing are commonly modeled with a changepoint approach using Chib’s (1998) algorithm to draw the break dates. We outline some advantages of an alternative approach in which breaks come through mixture distributions in state innovation ..."
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Cited by 17 (1 self)
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Time series subject to parameter shifts of random magnitude and timing are commonly modeled with a changepoint approach using Chib’s (1998) algorithm to draw the break dates. We outline some advantages of an alternative approach in which breaks come through mixture distributions in state innovations, and for which the sampler of Gerlach, Carter and Kohn (2000) allows reliable and efficient inference. We show how the same sampler can be used to (i) model shifts in variance that occur independently of shifts in other parameters (ii) draw the break dates in O(n) rather than O(n 3) operations in the changepoint model of Koop and Potter (2004b), the most general to date. Finally, we introduce to the time series literature the concept of adaptive MetropolisHastings sampling for discrete latent variable models. We develop an easily implemented adaptive algorithm that improves on Gerlach et al. (2000) and promises to significantly reduce computing time in a variety of problems including mixture innovation, changepoint, regimeswitching, and outlier detection. The efficiency gains on two models for U.S. inflation and real interest rates are 257 % and 341%.
Forecasting and Estimating Multiple Changepoint Models with an Unknown Number of Changepoints
, 2006
"... This paper develops a new approach to changepoint modeling that allows the number of changepoints in the observed sample to be unknown. The model we develop assumes regime durations have a Poisson distribution. It approximately nests the two most common approaches: the time varying parameter model ..."
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Cited by 12 (1 self)
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This paper develops a new approach to changepoint modeling that allows the number of changepoints in the observed sample to be unknown. The model we develop assumes regime durations have a Poisson distribution. It approximately nests the two most common approaches: the time varying parameter model with a changepoint every period and the changepoint model with a small number of regimes. We focus considerable attention on the construction of reasonable hierarchical priors both for regime durations and for the parameters which characterize each regime. A Markov Chain Monte Carlo posterior sampler is constructed to estimate a version of our model which allows for change in conditional means and variances. We show how real time forecasting can be done in an efficient manner using sequential importance sampling. Our techniques are found to work well in an empirical exercise involving US GDP growth and in‡ation. Empirical results suggest that the number of changepoints is larger than previously estimated in these series and the implied model is similar to a time varying parameter (with stochastic volatility) model.
A Parallel CuttingPlane Algorithm for the Vehicle Routing Problem With Time Windows
, 1999
"... In the vehicle routing problem with time windows a number of identical vehicles must be routed to and from a depot to cover a given set of customers, each of whom has a specified time interval indicating when they are available for service. Each customer also has a known demand, and a vehicle may on ..."
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Cited by 11 (1 self)
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In the vehicle routing problem with time windows a number of identical vehicles must be routed to and from a depot to cover a given set of customers, each of whom has a specified time interval indicating when they are available for service. Each customer also has a known demand, and a vehicle may only serve the customers on a route if the total demand does not exceed the capacity of the vehicle. The most effective solution method proposed to date for this problem is due to Kohl, Desrosiers, Madsen, Solomon, and Soumis. Their algorithm uses a cuttingplane approach followed by a branchand bound search with column generation, where the columns of the LP relaxation represent routes of individual vehicles. We describe a new implementation of their method, using Karger's randomized minimumcut algorithm to generate cutting planes. The standard benchmark in this area is a set of 87 problem instances generated in 1984 by M. Solomon; making using of parallel processing in both the cuttingpla...
European Central Bank
, 2008
"... New Keynesian Phillips Curves (NKPC) have been extensively used in the analysis of monetary policy, but yet there are a number of issues of concern about how they are estimated and then related to the underlying macroeconomic theory. The first is whether such equations are identified. To check ident ..."
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Cited by 10 (0 self)
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New Keynesian Phillips Curves (NKPC) have been extensively used in the analysis of monetary policy, but yet there are a number of issues of concern about how they are estimated and then related to the underlying macroeconomic theory. The first is whether such equations are identified. To check identification requires specifying the process for the forcing variables (typically the output gap) and solving the model for inflationintermsoftheobservables. Inpractice,theequationisestimatedby GMM, relying on statistical criteria to choose instruments. This may result in failure of identification or weak instruments. Secondly, the NKPC is usually derived as a part of a DSGE model, solved by loglinearising around a steady state and the variables are then measured in terms of deviations from the steady state. In practice the steady states, e.g. for output, are usually estimated by some statistical procedure such as the HodrickPrescott (HP) filter that might not be appropriate. Thirdly, there are arguments that other variables, e.g. interest rates, foreign inflation and foreign output gaps should enter the Phillips curve. This paper examines these three issues and argues that all three benefit from a global perspective. The global perspective provides additional instruments to alleviate the weak instrument problem, yields a theoretically consistent measure of the steady state and provides a natural route for foreign inflation or output gap to enter the NKPC. Keywords: Global VAR (GVAR), identification, New Keynesian Phillips Curve, TrendCycle decomposition.
Prior elicitation in multiple changepoint models
"... This paper discusses Bayesian inference in changepoint models. The main existing approaches either attempt to be noninformative by using a Uniform prior over changepoints or use an informative hierarchical prior. Both these approaches assume a known number of changepoints. We show how they have s ..."
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Cited by 8 (1 self)
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This paper discusses Bayesian inference in changepoint models. The main existing approaches either attempt to be noninformative by using a Uniform prior over changepoints or use an informative hierarchical prior. Both these approaches assume a known number of changepoints. We show how they have some potentially undesirable properties and discuss how these properties relate to the imposition of a …xed number of changepoints. We develop a new Uniform prior which allows some of the changepoints to occur outof sample. This prior has desirable properties, can reasonably be interpreted as “noninformative”and handles the case where the number of changepoints We would like to thank Edward Leamer for useful conversations and also seminar participants at the Federal Reserve Bank of St. Louis and University of Kansas. The views expressed in this paper are those of the authors and do not necessarily re‡ect the views of the Federal Reserve Bank of New York or the Federal Reserve System. 1 is unknown. We show how the general ideas of our approach can be extended to informative hierarchical priors. With arti…cial data and two empirical illustrations, we show how these di¤erent priors can have a substantial impact on estimation and prediction even with moderately large data sets. 1
Size and Value Anomalies under Regime Shifts ∗
, 2005
"... The views expressed are those of the individual authors and do not necessarily reflect official positions of the Federal Reserve Bank of St. Louis, the Federal Reserve System, or the Board of Governors. Federal Reserve Bank of St. Louis Working Papers are preliminary materials circulated to stimulat ..."
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Cited by 8 (3 self)
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The views expressed are those of the individual authors and do not necessarily reflect official positions of the Federal Reserve Bank of St. Louis, the Federal Reserve System, or the Board of Governors. Federal Reserve Bank of St. Louis Working Papers are preliminary materials circulated to stimulate discussion and critical comment. References in publications to Federal Reserve Bank of St. Louis Working Papers (other than an acknowledgment that the writer has had access to unpublished material) should be cleared with the author or authors.