Maximum likelihood in the frequency domain: a time to build example

A well known result is that the Gaussian log-likelihood can be expressed as the sum over different frequency components. This implies that the likelihood ratio statistic has a similar linear decomposition. We exploit these observations to devise diagnostic methods that are useful for interpreting maximum likelihood ratio tests. We apply the methods to the estimation and testing of two real business cycle models. The standard real business cycle model is rejected in favor of an alternative in which capital investment requires a planning period.Business cycles ; Investments

Maximum likelihood in the frequency domain: a time to build example

The Gaussian log-likelihood can be expressed as the sum over different frequency components. This implies that the likelihood ratio statistic has a similar linear decomposition. Exploiting these observations, the authors devise diagnostic methods that are useful for interpreting maximum-likelihood parameter estimates and likelihood ratio tests. They apply the methods to estimating and testing two real business-cycle models and reject the standard model in favor of an alternative in which capital investment requires a planning period.Business cycles ; Econometric models

Maximum likelihood in the frequency domain: the importance of time-to-plan

The authors illustrate the use of various frequency-domain tools for estimating and testing dynamic, stochastic, general-equilibrium models. Their substantive results confirm other findings that suggest that time-to-plan in investment technology has a potentially useful role to play in business-cycle models.Business cycles

What Happens After a Technology Shock?

We provide empirical evidence that a positive shock to technology drives per capita hours worked, consumption, investment, average productivity and output up. This evidence contrasts sharply with the results reported in a large and growing literature that argues, on the basis of aggregate data, that per capita hours worked fall after a positive technology shock. We argue that the difference in results primarily reflects specification error in the way that the literature models the low-frequency component of hours worked.

Entry dynamics and the decline in exchange-rate pass-through

The degree of exchange-rate pass-through to import prices is low. An average passthrough estimate for the 1980s would be roughly 50 percent for the United States implying that, following a 10 percent depreciation of the dollar, a foreign exporter selling to the U.S. market would raise its price in the United States by 5 percent. Moreover, substantial evidence indicates that the degree of pass-through has since declined to about 30 percent. ; Gust, Leduc, and Vigfusson (2010) demonstrate that, in the presence of pricing complementarity, trade integration spurred by lower costs for importers can account for a significant portion of the decline in pass-through. In our framework, pass-through declines solely because of markup adjustments along the intensive margin. ; In this paper, we model how the entry and exit decisions of exporting firms affect passthrough. This is particularly important since the decline in pass-through has occurred as a greater concentration of foreign firms are exporting to the United States. ; We find that the effect of entry on pass-through is quantitatively small and is more than offset by the adjustment of markups that arise only along the intensive margin. Even though entry has a relatively small impact on pass-through, it nevertheless plays an important role in accounting for the secular rise in imports relative to GDP. In particular, our model suggests that over 3/4 of the rise in the U.S. import share since the early 1980s is due to trade in new goods. ; Thus, a key insight of this paper is that adjustment of markups that occur along the intensive margin are quantitatively more important in accounting for secular changes in pass-through than adjustments that occur along the extensive margin.

Forecasting the Price of Oil

We address some of the key questions that arise in forecasting the price of crude oil. What do applied forecasters need to know about the choice of sample period and about the tradeoffs between alternative oil price series and model specifications? Are real or nominal oil prices predictable based on macroeconomic aggregates? Does this predictability translate into gains in out-of-sample forecast accuracy compared with conventional no-change forecasts? How useful are oil futures markets in forecasting the price of oil? How useful are survey forecasts? How does one evaluate the sensitivity of a baseline oil price forecast to alternative assumptions about future demand and supply conditions? How does one quantify risks associated with oil price forecasts? Can joint forecasts of the price of oil and of U.S. real GDP growth be improved upon by allowing for asymmetries?Econometric and statistical methods; International topics

The Response of Hours to a Technology Shock: Evidence Based on Direct Measures of Technology

We investigate what happens to hours worked after a positive shock to technology, using the aggregate technology series computed in Basu, Fernald and Kimball (1999). We conclude that hours worked rise after such a shock.

Maximum Likelihood in the Frequency Domain: A Time to Build Example

A well known result is that the Gaussian log-likelihood can be expressed as the sum over different frequency components. This implies that the likelihood ratio statistic has a similar linear decomposition. We exploit these observations to devise diagnostic methods that are useful for interpreting maximum likelihood parameter estimates and likelihood ratio tests. We apply the methods to the estimation and testing of two real business cycle models. The standard real business cycle model is rejected in favor of an alternative in which capital investment requires a planning period