Output Fluctuations in the G-7: An Unobserved Components Approach

This paper proposes a multivariate unobserved components model to simultaneously decompose the real GDP for each of the G-7 countries into their respective trend and cycle components. In contrast to previous literature, our model allows for explicit correlation between all the contemporaneous trend and cycle shocks. This approach thus allows us to distinguish cross-country correlation driven by shared trend shocks from correlation between the cycle shocks. We find that fluctuations in output are primarily due to permanent shocks for all of the G-7 countries. We also find that common restrictions on the correlations between trend and cycle shocks are rejected by the data. With regards to cross-country relationships, we find some countries share more transitory shocks, such as Canada and the US, whereas others, such as Germany and France, share more permanent shocks.

Examining the Quality of Early GDP Component Estimates

In this paper we examine the quality of the initial estimates of headline GDP and 10 major components of both real and nominal U.S. GDP. We ask a number of questions about various characteristics of the differences between the initial estimates available one month after the end of the quarter to the estimates available three months after the end of the quarter. Do the first estimates have the same directional signs as the later numbers? Are the original numbers unbiased estimates of the later figures? Are any observed biases related to the state of the economy? Finally, we determine whether there is a significant difference between the vector of the 30 day estimates of the 10 major components and the vector of the 90 day estimates of the same components. We conclude that, despite the existence of some bias, under most circumstances, an analyst could use the early data to obtain a realistic picture of what had happened in the economy in the previous quarter.Flash Estimates, Data Revisions, GDP Components, Statistical Tests, Business Cycles

Differences in Early GDP Component Estimates Between Recession and Expansion

In this paper we examine the quality of the initial estimates of the components of both real and nominal U.S. GDP. We introduce a number of new statistics for measuring the magnitude of changes in the components from the initial estimates available one month after the end of the quarter to the estimates available 3 months after the end of the quarter. We further specifically investigate the potential role of changes in the state of the economy for these changes. Our analysis shows that the early data generally reflected the composition of the changes in GDP that was observed in the later data. Thus, under most circumstances, an analyst could use the early data to obtain a realistic picture of what had happened in the economy in the previous quarter. However, the differences in the composition of the vectors of the two vintages were larger during recessions than in expansions. Unfortunately, it is in those periods when accurate information is most vital for forecasting.Flash Estimates, Data Revisions, GDP Components, Statistical Tests, Business Cycles

A Likelihood Ratio Test of Stationarity Based on a Correlated Unobserved Components Model

We propose a likelihood ratio (LR) test of stationarity based on a widely-used correlated unobserved components model. We verify the asymptotic distribution and consistency of the LR test, while a bootstrap version of the test is at least first-order accurate. Given empirically-relevant processes estimated from macroeconomic data, Monte Carlo analysis reveals that the bootstrap version of the LR test has better small-sample size control and higher power than commonly used bootstrap Lagrange multiplier (LM) tests, even when the correct parametric structure is specified for the LM test. A key feature of our proposed LR test is its allowance for correlation between permanent and transitory movements in the time series under consideration, which increases the power of the test given the apparent presence of non-zero correlations for many macroeconomic variables. Based on the bootstrap LR test, and in some cases contrary to the bootstrap LM tests, we can reject trend stationarity for U.S. real GDP, the unemployment rate, consumer prices, and payroll employment in favor of nonstationary processes with volatile stochastic trends.Stationarity Test, Likelihood Ratio, Unobserved Components, Parametric Bootstrap, Monte Carlo Simulation, Small-Sample Inference

Searching for better prospects: endogenizing falling job tenure and private pension coverage

Recent declines in job tenure have coincided with a shift away from traditional defined benefit (DB) pensions, which reward long tenure. New evidence also points to an increase in job-to-job movements by workers, and we document gains in relative wages of job-to-job movers over a similar period. We develop a search model in which firms may offer tenure-based contracts like DB pensions to reduce the incidence of costly on-the-job search by workers. Either reduced search costs or an increase in the probability of job matches can, under fairly general conditions, lower the value of deterring search and the use of DB pensions.Pensions ; Unemployment

Searching for Better Prospects: Endogenizing Falling Job Tenure and Private Pension Coverage

Recent declines in job tenure have coincided with a shift away from traditional defined benefit (DB) pensions, which reward long tenure. Recent evidence also points to an increase in job-to-job movements by workers, and we document gains in relative wages of job-to-job movers over a similar period. We develop a search model in which firms may offer tenure-based contracts like DB pensions to reduce the incidence of costly on-the-job search by workers. Reduced search costs can, under fairly general conditions, lower the value of deterring search and the use of DB pensions.

Are 'unbiased' forecasts really unbiased? Another look at the Fed forecasts

This paper reconciles contradictory findings obtained from forecast evaluations: the existence of systematic errors and the failure to reject rationality in the presence of such errors. Systematic errors in one economic state may offset the opposite types of errors in the other state such that the null of rationality is not rejected. A modified test applied to the Fed forecasts shows that the forecasts were ex post biased.Greenbook Forecasts, forecast evaluation, systematic errors

Can the Fed Predict the State of the Economy?

Recent research has documented that the Federal Reserve produces systematic errors in forecasting inflation, real GDP growth, and the unemployment rate, even though these forecasts are unbiased. We show that these systematic errors reveal that the Fed is “surprised” by real and inflationary cycles. Using a modified Mincer-Zarnowitz regression, we show that the Fed knows the state of the economy for the current quarter, but cannot predict it one quarter ahead.Forecast Evaluation; Federal Reserve; Systematic Errors; Recessions

How Well Does "Core" CPI Capture Permanent Price Changes?

We decompose core CPI and the food and energy CPI measures into permanent and transitory components using a correlated unobserved components model, to examine the behavior of core CPI when subject to shocks and to examine the claim that core CPI captures the persistent part of headline CPI. We find that the permanent component of core CPI is more volatile than core CPI, or that the permanent and transitory components are highly correlated. We find that the excluded food and energy components have important permanent components, and that core CPI has an important transitory component. We examine impulse response functions and find that headline CPI inflation responds more sharply to shocks than core CPI inflation, and after the first year the impact of shocks on headline inflation is less than the impact on core inflation.unobserved components, CPI, price indices, inflation, core

Jointly Evaluating the Federal Reserve’s Forecasts of GDP Growth and Inflation

In this paper we jointly evaluate the Federal Reserve staff forecasts of U.S. real output growth and the inflation rate assuming the forecasts are to be used as inputs for the Taylor rule. Our simple methodology generates “policy forecast errors” which have a direct interpretation for the impact of forecast errors on the target interest rate given by the Taylor rule. Without interest rate smoothing, we find that, on average, the Taylor rule target interest rate would have been approximately a full percentage point away from the intended target because of errors in forecasting output growth and inflation. Our results are robust to changes in the forecast horizon and to changes in the weights on the variables in the policy rule.Evaluating Forecasts, Macroeconomic Forecasts, Loss Function,Inflation Forecasting, GDP Growth Forecasting, Monetary Policy