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    Systematic peak-load pricing, congestion premia and demand diverting: Empirical evidence

    Economics Letters, 103 (1), April 2009, 59-61.

    This paper finds empirical support to systematic peak-load pricing in airlines — higher fares in ex-ante known congested periods. It estimates a congestion premia and supports the main empirical prediction in Gale and Holmes [Gale, I., Holmes, T., 1993. Advance-purchase discounts and monopoly allocation of capacity. American Economic Review 83, 135–146] — less discount seats on peak flights.

  • Friends2

    Dynamic pricing, advance sales, and aggregate demand learning in airlines

    Journal of Industrial Economics, 60 (4), December 2012, 697-724.

    This paper uses a unique U.S. airlines panel data set to empirically study the dynamic pricing of inventories with uncertain demand over a finite horizon. I estimate a dynamic pricing equation and a dynamic demand equation that jointly characterize the adjustment process between prices and sales as the flight date nears. I find that the price increases as the inventory decreases, and decreases as there is less time to sell. Consistent with aggregate demand learning and price adjustment, demand shocks have a positive and much larger effect on prices than the positive effect of anticipated sales.

  • 0024

    Estimating dynamic demand for airlines

    Economics Letters, 124 (1), July 2014, 26-29.

    This paper uses an original panel dataset with posted prices and sales to estimate a dynamic demand. We find that consumers become more price sensitive as time to departure nears which is consistent with having lower valuations. This result provides empirical support to a key theoretical implication in Deneckere and Peck (2012)—high-valuation consumers purchase earlier. We also find that the number of active consumers increases closer to departure.

  • 0024

    Price discrimination through refund contracts in airlines (with Paan Jindapon)

    International Journal of Industrial Organization, 34 (3), May 2014, 1-8.

    This paper shows how an airline monopoly uses refundable and non-refundable tickets to screen consumers who are uncertain about their travel. Our theoretical model predicts that the difference between these two fares diminishes as individual demand uncertainty is resolved. Using an original data set from U.S. airline markets, we find strong evidence supporting our model. Price discrimination opportunities through refund contracts decline as the departure date nears and individuals learn about their demand.

  • 0024

    Airport, airline and departure time choice and substitution patterns: An empirical analysis

    Transportation Research Part A, 103, September 2017, 198-210.

    This paper uses the random-coefficients logit methodology that controls for potential endogeneity of prices and allows for general substitution patterns to estimate various demand systems. The estimation takes advantage of an original ticket-level revealed preference data set on travels from the New York City area to Toronto that contains prices and characteristics of not only flight choices but also of all non-booked alternative flights. Consistent with having higher valuations, our results show that travelers buying closer to departure have a higher utility of flying. Moreover, consumers’ heterogeneity decreases as the flight date nears. At the carrier level, we identify which carriers have more pricesensitive consumers and which carriers face greater competition. In addition, the results suggest that our multi-airport metropolitan area can be considered as a single market and that JFK and Newark are relatively closer substitutes. Overall, consumers are more willing to switch to alternative carriers than between airports or departure times.

  • 0024

    Separating between unobserved consumer types: Evidence from airlines (with Manuel A. Hernandez)

    Economic Inquiry

    We propose an alternative approach to identify unobserved consumer types and assess whether firms price discriminate. Unlike other screening schemes that rely on quantity discounts or product differentiation, in our finite mixture structure individuals have unit demands and the product is homogeneous. We implement the model using an original U.S. airlines data set. The results support the existence of two demand types. The high-type “business” traveler is less price sensitive, has a higher valuation, and pays a higher price than the low type “tourist.” The proportion of high types also increases as the departure date nears.

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    Identifying price bubble periods in the energy sector (with Shahil Sharma)

    Energy Economics, 69, January 2018, 418-429.

    In this paperwe test for the existence of single andmultiple episodes of explosive behavior in three energy sector indices (crude oil, heating oil, and natural gas) and five energy sector spot prices (West Texas Intermediate (WTI), Brent, heating oil, natural gas, and jet fuel). The results from the Supremum Augmented Dickey-Fuller (SADF) and the Generalized SADF tests provide strong statistical evidence of explosive behavior in all of our energy series. A simple theoretical framework of commodity pricing allows us to understand the assumptions to interpret explosive behavior as bubbles. By constructing implied convenience yields using futures prices we test the key assumption and we are able to identify the beginning and the end of bubble periods for the WTI, Brent, heating oil, and natural gas spot prices.

  • 0024

    An analysis of dynamic price discrimination in airlines (with Nick Rupp and Joe Meskey)

    Southern Economic Journal, 85 (3), January 2019, 639-662.

    Prices for the same flight change substantially depending on the time of purchase. This article uses a unique data set with round-the-clock posted fares to document significant within-day price variation. Labeling time-variation as discriminatory is difficult because the cost of an unsold airline seat changes with inventory, days before departure, and aggregate demand expectations. After controlling for these factors and aggregating hourly fares to have a framework with two consumer types, we are able to identify a component that is largely consistent with dynamic price discrimination. We find higher prices during office hours (when business travelers are likely to buy) and lower prices in the evening (when leisure travelers are more likely to purchase). As the proportion of business travelers increases closer to departure, both price dispersion and price discrimination become larger. We provide an alternative explanation for the observed within-day price differentials which is related to Edgeworth price cycles.

  • 0024

    Long-run equilibrium shift and short-run dynamics of U.S. home price tiers during the housing bubble (with Damian Damianov)

    Journal of Real Estate Finance and Economics, 53 (1), July 2016, 1-28.

    We use VECM to examine the interdependence between the high and the low price tiers during the latest housing market boom and bust. For 118 of the 364 US MSAs analyzed, the tiered price indexes are bound by a long-run relationship. In general, low tier homes appreciated more than high tier homes in the past two decades. In contrast to previous periods of high volatility, however, low tier homes appreciated more during the boom and lost more value during the bust of the market. We find a shift in the long-run equilibrium during the bubble —the cointegration parameter that ties the tiers together is greater in absolute value during the bubble period compared to the periods of more moderate appreciation and depreciation rates. Moreover, the shift in the long-run equilibrium can be explained by differences in subprime originations across housing markets. We also find that short run price dynamics is driven by momentum in both segments of the market.

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    Demand uncertainty and capacity utilization in airlines (with Jim Lee)

    Empirical Economics, 47 (1), August 2014, 1-19.

    This paper studies the relationship between demand uncertainty—the key source of excess capacity—and capacity utilization in the US airline industry. We present a simple theoretical model that predicts that lower demand realizations are associated with higher demand volatility. This prediction is strongly supported by the results of estimating a panel GARCH framework that pools unique data on capacity utilization across different flights and over various departure dates. A one unit increase in the standard deviation of unexpected demand decreases capacity utilization by 21 percentage points. The estimation controls for unobserved time-invariant specific characteristics as well as for systematic demand fluctuations.