The gravity model has been widely used to infer substantial trade flow effects of institutions such as customs unions and exchange rate mechanisms. McCallum [1995] found that the US-Canada border led to trade between provinces that is a factor 22 (2,200%) times trade between states and provinces, a spectacular puzzle in light of the low formal barriers on this border. We show that the gravity model usually estimated does not correspond to the theory behind it. We solve the 'border puzzle' by applying the theory seriously. We find that national borders reduce trade between the US and Canada by about 44%, while reducing trade among other industrialized countries by about 30%. McCallum's spectacular headline number is the result of a combination of omitted variables bias and the small size of the Canadian economy. Within-Canada trade rises by a factor 6 due to the border. In contrast, within-US trade rises 25%.

https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=256086

Gravity with Gravitas: A Solution to the Border Puzzle

Gravity equations have been widely used to infer trade flow effects of various institutional arrangements. We show that estimated gravity equations do not have a theoretical foundation. This implies both that estimation suffers from omitted variables bias and that comparative statics analysis is unfounded. We develop a method that (i) consistently and efficiently estimates a theoretical gravity equation and (ii) correctly calculates the comparative statics of trade frictions. We apply the method to solve the famous McCallum border puzzle. Applying our method, we find that national borders reduce trade between industrialized countries by moderate amounts of 20-50 percent.

/pdf/gravity-with-gravitas-a-solution-to-the-border-puzzle-4dn7us6msp.pdf

In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

Using a Bayesian likelihood approach, we estimate a dynamic stochastic general equilibrium model for the US economy using seven macro-economic time series. The model incorporates many types of real and nominal frictions and seven types of structural shocks. We show that this model is able to compete with Bayesian Vector Autoregression models in out-of-sample prediction. We investigate the relative empirical importance of the various frictions. Finally, using the estimated model we address a number of key issues in business cycle analysis: What are the sources of business cycle fluctuations? Can the model explain the cross-correlation between output and inflation? What are the effects of productivity on hours worked? What are the sources of the "Great Moderation"?

/pdf/shocks-and-frictions-in-us-business-cycles-a-bayesian-dsge-d1f6ynd58q.pdf

Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach

The Real Business Cycle (RBC) research program has grown spectacularly over the last decade, as its concepts and methods have diffused into mainstream macroeconomics. Yet, there is increasing skepticism that technology shocks are a major source of business fluctuations. This chapter exposits the basic RBC model and shows that it requires large technology shocks to produce realistic business cycles. While Solow residuals are sufficiently volatile, these imply frequent technological regress. Productivity studies permitting unobserved factor variation find much smaller technology shocks, suggesting the imminent demise of real business cycles. However, we show that greater factor variation also dramatically amplifies shocks: a RBC model with varying capital utilization yields realistic business cycles from small, nonnegative changes in technology.

/pdf/resuscitating-real-business-cycles-3jhtvvsksh.pdf

Resuscitating Real Business Cycles

How much does real gross domestic product (GDP) respond to unanticipated changes in the real price of oil? Commonly used censored oil price vector autoregressive models suggest a substantial decline in real GDP in response to unexpected increases in the real price of oil, yet no response to unexpected declines. We show that these estimates are invalid. Based on a structural model that encompasses both symmetric and asymmetric models as special cases, correctly computed impulse responses are of roughly the same magnitude in either direction, consistent with formal tests for symmetric responses. We discuss implications for theoretical models and for policy responses to energy price shocks.

/pdf/are-the-responses-of-the-u-s-economy-asymmetric-in-energy-4ectkoa1mz.pdf

Are the responses of the U.S. economy asymmetric in energy price increases and decreases

GAUSS code for the Hodrick-Prescott filter

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 and 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 prices in forecasting the spot 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 oil demand and oil 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?

/pdf/forecasting-the-price-of-oil-4z7be0vnej.pdf

Forecasting the Price of Oil

We provide empirical evidence that a positive shock to technology drives up per capita hours worked, consumption, investment, average productivity and output . 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.

What happens after a technology shock

It is customary to suggest that the asymmetry in the transmission of oil price shocks to real output is well established. Much of the empirical work cited as being in support of asymmetry, however, has not directly tested the hypothesis of an asymmetric transmission of oil price innovations. Moreover, many of the papers quantifying these asymmetric responses are based on censored oil price VAR models that have recently been shown to be invalid. Other studies are based on dynamic correlations in the data and do not distinguish between cause and effect. Recently, several new methods of testing and quantifying asymmetric responses of U.S. real economic activity to positive and negative oil price innovations have been developed. We put this literature into perspective, contrast it with more traditional approaches, highlight directions for further research, and reconcile some seemingly conflicting results reported in the literature.

/pdf/nonlinearities-in-the-oil-price-output-relationship-3v3tfp2z8q.pdf

Robert J. Vigfusson

Papers

Are the responses of the U.S. economy asymmetric in energy price increases and decreases

GAUSS code for the Hodrick-Prescott filter

Forecasting the Price of Oil

What happens after a technology shock

Nonlinearities in the oil price–output relationship