Relevant bibliographies by topics / The market methods

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'The market methods'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "The market methods"

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Waheeduzzaman, A. N. M. "Market Potential Estimation in International Markets: A Comparison of Methods." Journal of Global Marketing 21, no. 4 (September 19, 2008): 307–20. http://dx.doi.org/10.1080/08911760802206144.

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Ederington, Louis, Wei Guan, and Lisa (Zongfei) Yang. "Bond market event study methods." Journal of Banking & Finance 58 (September 2015): 281–93. http://dx.doi.org/10.1016/j.jbankfin.2015.03.013.

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Yerniyazov, T. N., and M. K. Myrzakhmet. "Market barometer. The methods of market analysis with the use of market indicators." Economic Series of the Bulletin of the L.N. Gumilyov ENU, no. 3 (2018): 19–23. http://dx.doi.org/10.32523/2079-620x-2018-3-19-23.

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Jull, Olav. "International Market Analysis: Theories And Methods." Journal of Euromarketing 18, no. 1 (2009): 065–66. http://dx.doi.org/10.9768/0018.01.065.

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Malyshev, E. A. "METHODS OF NATIONAL LABOR MARKET PROTECTION." Journal of Law and Administration, no. 2 (January 1, 2017): 54–60. http://dx.doi.org/10.24833/2073-8420-2017-2-43-54-60.

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Kim, Taehyun, and Hoon‐Young Lee. "External validity of market segmentation methods." European Journal of Marketing 45, no. 1/2 (February 15, 2011): 153–69. http://dx.doi.org/10.1108/03090561111095630.

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Sørensen, Olav Jull. "International Market Analysis: Theories And Methods." Journal of Euromarketing 18, no. 1 (January 2009): 65–66. http://dx.doi.org/10.1080/10496480902868441.

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Carroll, Donal. "Marketing Your Methods: Avoiding Market Farces." Journal of Further and Higher Education 18, no. 3 (September 1994): 3–11. http://dx.doi.org/10.1080/0309877940180301.

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Uva, Wen-fei L. "680 Innovative Methods to Market Locally Grown Products." HortScience 35, no. 3 (June 2000): 516A—516. http://dx.doi.org/10.21273/hortsci.35.3.516a.

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The development of industrialized production and global sourcing has changed the marketing structure of the horticulture industry dramatically. The inherent disadvantaged resource base (soils and climate) and high production costs in the northeast United States make it difficult for growers to compete in commodity markets. Exploiting niche and value-added markets are important for the survival of northeast agriculture. Moreover, an emphasis on quality of life has created a movement towards sustainable agriculture. As a result of this movement, many programs have been initiated to promote locally grown products and to support agricultural-based economic development. The common objectives of the “locally grown” programs are to promote agricultural products produced within the region, support the local economy, and develop agricultural markets. Keys to success of a “locally grown” program are a vision, seed funding, a champion, and community, political leadership and technical support. Many innovative regional food and agriculture development programs have been initiated in New York State to support local farmers, revitalize the rural economy, promote local identity and pride, develop agri-tourism, and capture the urban markets. Some examples include the “Finger Lakes Culinary Bounty” initiated by local chefs, “Uncork New York” sponsored by the wine industry, and “Hudson Valley Harvest” and a pilot ethnic market project targeting New York City markets.
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Garrett, Alexander, and Cara Wrigley. "Navigating market opportunity: traditional market research and deep customer insight methods." Qualitative Market Research: An International Journal 22, no. 3 (June 10, 2019): 456–71. http://dx.doi.org/10.1108/qmr-05-2017-0091.

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PurposeThis paper aims to explore the use of a design process of inquiry that incorporates both deep customer insight (DCI) and traditional market research (TMR) in an ill-defined, complex current market opportunity to generate new business opportunities for firm-based innovation.Design/methodology/approachThe paper reports on an empirical research case study conducted within a multi-national insurance agency looking at the shift in mobility in Australia. Data were collected across seven distinct research phases, all of which used TMR and DCI techniques for joint comparison.FindingsThe findings revealed that TMR and DCI methodologies developed both contradictory and complementary research outcomes. These outcomes saw rise to newly generated novel business model concepts for market entry opportunity from the case study firm.Research limitations/implicationsThe theoretical outcome of this study is the design thinking DCI framework providing guidance on appropriate implementation of research methods to respond to complex market opportunity.Practical implicationsDCI methods used in conjunction with TMR can provide early stage market opportunity assessment for firms seeking to innovate from a customer perspective.Originality/valueThis is the first paper to apply a design approach, combining TMR and DCI methods to a complex market opportunity rather than a tangible problem. In addition, it also contributes to the emerging field of DCI methodologies by providing a practical examination of their use in the field.
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Dissertations / Theses on the topic "The market methods"

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Sandholm, Thomas. "Statistical Methods for Computational Markets : Proportional Share Market Prediction and Admission Control." Doctoral thesis, KTH, Data- och systemvetenskap, DSV, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4738.

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We design, implement and evaluate statistical methods for managing uncertainty when consuming and provisioning resources in a federated computational market. To enable efficient allocation of resources in this environment, providers need to know consumers' risk preferences, and the expected future demand. The guarantee levels to offer thus depend on techniques to forecast future usage and to accurately capture and model uncertainties. Our main contribution in this thesis is threefold; first, we evaluate a set of techniques to forecast demand in computational markets; second, we design a scalable method which captures a succinct summary of usage statistics and allows consumers to express risk preferences; and finally we propose a method for providers to set resource prices and determine guarantee levels to offer. The methods employed are based on fundamental concepts in probability theory, and are thus easy to implement, as well as to analyze and evaluate. The key component of our solution is a predictor that dynamically constructs approximations of the price probability density and quantile functions for arbitrary resources in a computational market. Because highly fluctuating and skewed demand is common in these markets, it is difficult to accurately and automatically construct representations of arbitrary demand distributions. We discovered that a technique based on the Chebyshev inequality and empirical prediction bounds, which estimates worst case bounds on deviations from the mean given a variance, provided the most reliable forecasts for a set of representative high performance and shared cluster workload traces. We further show how these forecasts can help the consumers determine how much to spend given a risk preference and how providers can offer admission control services with different guarantee levels given a recent history of resource prices.
QC 20100909
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Bumgarner, Natalie R. "Methods of season extension for market gardeners." Morgantown, W. Va. : [West Virginia University Libraries], 2007. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=5493.

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Thesis (M.S.)--West Virginia University, 2007.
Title from document title page. Document formatted into pages; contains vi, 104 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 100-104).
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Corrado, Charles J. "Nonparametric statistical methods in financial market research." Diss., The University of Arizona, 1988. http://hdl.handle.net/10150/184608.

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This dissertation presents an exploration of the use of nonparametric statistical methods based on ranks for use in financial market research. Applications to event study methodology and the estimation of security systematic risk are analyzed using a simulation methodology with actual daily security return data. The results indicate that procedures based on ranks are more efficient than normal theory procedures currently in common use.
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Jonsson, Ewerbring Marcus. "Explainable Deep Learning Methods for Market Surveillance." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300156.

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Deep learning methods have the ability to accurately predict and interpret what data represents. However, the decision making of a deep learning model is not comprehensible for humans. This is a problem for sectors like market surveillance which needs clarity in the decision making of the used algorithms. This thesis aimed to investigate how a deep learning model can be constructed to make the decision making of the model humanly comprehensible, and to investigate the potential impact on classification performance. A literature study was performed and publicly available explanation methods were collected. The explanation methods LIME, SHAP, model distillation and SHAP TreeExplainer were implemented and evaluated on a ResNet trained on three different time-series datasets. A decision tree was used as the student model for model distillation, where it was trained with both soft and hard labels. A survey was conducted to evaluate if the explanation method could increase comprehensibility. The results were that all methods could improve comprehensibility for people with experience in machine learning. However, none of the methods could provide full comprehensibility and clarity of the decision making. The model distillation reduced the performance compared to the ResNet model and did not improve the performance of the student model.
Djupinlärningsmetoder har egenskapen att förutspå och tolka betydelsen av data. Däremot så är djupinlärningsmetoders beslut inte förståeliga för människor. Det är ett problem för sektorer som marknadsövervakning som behöver klarhet i beslutsprocessen för använda algoritmer. Målet för den här uppsatsen är att undersöka hur en djupinlärningsmodell kan bli konstruerad för att göra den begriplig för en människa, och att undersöka eventuella påverkan av klassificeringsprestandan. En litteraturstudie genomfördes och publikt tillgängliga förklaringsmetoder samlades. Förklaringsmetoderna LIME, SHAP, modelldestillering och SHAP TreeExplainer blev implementerade och utvärderade med en ResNet modell tränad med tre olika dataset. Ett beslutsträd användes som studentmodell för modelldestillering och den blev tränad på båda mjuka och hårda etiketter. En undersökning genomfördes för att utvärdera om förklaringsmodellerna kan förbättra förståelsen av modellens beslut. Resultatet var att alla metoder kan förbättra förståelsen för personer med förkunskaper inom maskininlärning. Däremot så kunde ingen av metoderna ge full förståelse och insyn på hur beslutsprocessen fungerade. Modelldestilleringen minskade prestandan jämfört med ResNet modellen och förbättrade inte prestandan för studentmodellen.
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Fu, Qi. "Numerical methods for pricing callable bonds." Thesis, University of Macau, 2011. http://umaclib3.umac.mo/record=b2493162.

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Ibrahim, Hany. "Evaluation methods for market models used in smart grids." Thesis, KTH, Elektriska energisystem, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-104541.

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Skillbäck, Mikael, and Hany Ibrahim. "Evaluation Methods for Market Models Used in Smart Grids." Thesis, KTH, Energiteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-104912.

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The European Union has set environmental targets on climate change in three areas: energy efficiency, renewable energy sources, and reduction of emissions. These targets are the main driver for the change in today’s power system. The defined targets do not only affect the production and distribution of electricity but also raise questions on how electricity is being consumed. An essential building block of an efficient power system is often referred to as the smart grid. One of the important components of a smart grid is dynamic market models that facilitate demand response. Residential customers account for a relatively large portion of the total electricity consumption in Europe but relatively little is known about dynamic market models used in the residential sector. Pilot projects concerning dynamic market models have been conducted, but there is a lack of common evaluation methods to assess them.    This report investigates how pilot projects concerning demand response can be evaluated and presents a compilation of 135 international pilot projects and their results. The evaluations methods and findings from the international compilation are then applied to assess the proposed dynamic market model for the Stockholm Royal Seaport. Four evaluation criterions have been identified. The first relates to the demand response resource that is being studied and its impact on the results of the pilot project. Secondly, design principles of the pilot project must be considered. Thirdly, the division of costs and benefits among stakeholders must be calculated. Lastly, the precision of these measures must be taken into consideration. The compilation of international pilot projects reveals that dynamic markets models are often combined with modern technology. Combinations of market models, feedback and technology have an impact on overall electricity conservation and peak reduction. The reductions also depend on what electrical appliances are being used by the household members and their willingness to change their behavior. Customer acceptance is generally large among participants in pilot projects concerning dynamic market models. The hypothesis for the Stockholm Royal Seaport, in which five to fifteen percent of the load can be shifted from peak hours to off-peak hours with the proposed market model for the Stockholm Royal Seaport, is estimated to be reasonable. Load shift would lead to savings in the range between 64 – 266 SEK per year, which accounts for 1 – 4 % in bill savings. If the proposed dynamic market model is compared to fixed one month contracts, which includes retail price and fixed network tariffs, the bill savings were estimated to have been 563-766 SEK in year 2010. This corresponds to bill savings in the range of 8 – 11 %. Keywords: Smart grid, demand response, market model, evaluation methods for pilot projects, Stockholm Royal Seaport
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MacDonald, Garry A. "Applied analysis of labour and financial markets using time series methods." Curtin University of Technology, School of Economics and Finance, 1997. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=10866.

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The development of time series techniques associated with non stationary data, such as the testing for unit roots and cointegration has presented the applied worker with new challenges in the applied analysis of economic problems.This thesis uses some of these methods to consider a number of questions in the area of labour and financial markets.In particular the thesis considers the application of these methods to two general questions, the specification of the aggregate wage equation in Australia and the efficiency of the Australian stock market. More specifically the thesis focuses on the time series properties of variables commonly used in specifications of the wage equation and then tests them for cointegration. In the financial economics area the thesis tests for the gains to portfolio diversification from the perspective of an Australian investor and the applicability of the present value model of stock prices to the Australian stock market.
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Malishka, Peter. "Measurement costs and pricing methods in the retail produce market." Thesis, Montana State University, 1999. http://etd.lib.montana.edu/etd/1999/malishka/MalishkaP1999.pdf.

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A persistent practice in the retail produce market is the mixed use of per unit and per pound pricing for bulk produce commodities. While per pound pricing explicitly prices the size dimension of the produce, per unit pricing (known in the industry as "by the each" pricing) is a form of average pricing whereby units differing in size and value are sold for the same price. When goods are average priced, opportunities exist for buyers to find units of exceptional value at the going price. Exploiting these opportunities requires buyers to measure and compare the values of individual units. Measurement of this kind often results in costly wealth transfers among buyers and between buyers and sellers. Profit maximization implies that sellers will avoid average pricing and its associated measurement costs whenever alternative pricing methods can be implemented at lower cost. This study examines the implications of measurement costs in the retail produce market, and develops predictions concerning the seller's decision to set an average price (price per each) or a price per pound. Logistic regression analysis is used to test the predictions on retail price data from major retailers in Bozeman, Montana. The results suggest that sellers choose between the two pricing methods in a manner that is consistent with the minimization of pre-sale measurement costs.
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Guo, Matilda, and Maria Lapenkova. "Numerical Methods for Pricing Swing Options in the Electricity Market." Thesis, Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-13931.

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Since the liberalisation of the energy market in Europe in the early 1990s, much opportunity to trade electricity as a commodity has arisen. One significant consequence of this movement is that market prices have become more volatile instead of its tradition constant rate of supply. Spot price markets have also been introduced, affecting the demand of electricity as companies now have the option to not only produce their own supply but also purchase this commodity from the market. Following the liberalisation of the energy market, hence creating a greater demand for trading of electricity and other types of energy, various types of options related to the sales, storage and transmission of electricity have consequently been introduced. Particularly, swing options are popular in the electricity market. As we know, swing-type derivatives are given in various forms and are mainly traded as over-the-counter (OTC) contracts at energy exchanges. These options offer flexibility with respect to timing and quantity. Traditionally, the Geometric Brownian Motion (GBM) model is a very popular and standard approach for modelling the risk neutral price dynamics of underlyings. However, a limitation of this model is that it has very few degrees of freedom, as it does not capture the complex behaviour of electricity prices. In short the GBM model is inefficient in the pricing of options involving electricity. Other models have subsequently been used to bridge this inadequacy, e.g. spot price models, futures price models, etc. To model risk-neutral commodity prices, there are basically two different methodologies, namely spot and futures or so-called term structure models. As swing options are usually written on spot prices, by which we mean the current price at which a particular commodity can be bought or sold at a specified time and place, it is important for us to examine these models in order to more accurately inculcate their effect on the pricing of swing options. Monte Carlo simulation is also a widely used approach for the pricing of swing options in the electricity market. Theoretically, Monte Carlo valuation relies on risk neutral valuation and the technique used is to simulate as many (random) price paths of the underlying(s) as possible, and then to average the calculated payoff for each path, discounted to today's prices, giving the value of the desired derivative. Monte Carlo methods are particularly useful in the valuation of derivatives with multiple sources of uncertainty or complicated features, like our electricity swing options in question. However, they are generally too slow to be considered a competitive form of valuation, if any analytical techniques of valuation exist. In other words, the Monte Carlo approach is, in a sense, a method of last resort. In this thesis, we aim to examine a numerical method involved in the pricing of swing options in the electricity market. We will consider an existing and widely accepted electricity price process model, use the finite volume method to formulate a numerical scheme in order to calibrate the prices of swing options and make a comparison with numerical solutions obtained using the theta-scheme. Further contributions of this thesis include a comparison of results and also a brief discussion of other possible methods.
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Books on the topic "The market methods"

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Paul, Jefferies, and Hui Pak Ming, eds. Financial market complexity. Oxford: Oxford University Press, 2003.

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Mendoza, Meyra Sebello. Pricing behavior in Philippine corn markets: Implications for market efficiency. Washington, D.C: International Food Policy Research Institute, 1995.

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Statistics in market research. London: Arnold, 2004.

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Desai, Philly. Methods beyond interviewing in qualitative market research. London: Sage, 2002.

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Judd, Kenneth L. Asymptotic methods for asset market equilibrium analysis. Cambridge, MA: National Bureau of Economic Research, 2001.

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Laurent, Allaz Blaise, ed. Financial securities: Market equilibrium and pricing methods. Cincinnati, Ohio: South-Western College Pub., 1996.

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Desai, Philly. Methods beyond interviewing in qualitative market research. London: SAGE, 2002.

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Lawson, Catherine Theresa. Survey methods for assessing freight industry opinions. Salem, OR: Oregon Dept. of Transportation, Research Group, 2002.

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Adrienne, Schmitz, Schmitz Adrienne, and Urban Land Institute, eds. Real estate market analysis: Methods and case studies. 2nd ed. Washington, D.C: Urban Land Institute, 2009.

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Brett, Deborah L. Real estate market analysis: Methods and case studies. 2nd ed. Washington, D.C: Urban Land Institute, 2009.

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Book chapters on the topic "The market methods"

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Shapiro, Eric, David Mackmin, and Gary Sams. "Market rent." In Modern Methods of Valuation, 93–108. Twelfth edition. | Abingdon, Oxon ; New York, NY : Routledge, 2019.: Estates Gazette, 2019. http://dx.doi.org/10.1201/9781315145419-6.

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Musiela, Marek, and Marek Rutkowski. "Market Imperfections." In Martingale Methods in Financial Modelling, 87–108. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-22132-7_4.

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Musiela, Marek, and Marek Rutkowski. "Foreign Market Derivatives." In Martingale Methods in Financial Modelling, 159–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-22132-7_7.

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Davidian, Ken. "Small Satellite Market Research Methods." In Handbook of Small Satellites, 1–12. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-20707-6_106-1.

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Davidian, Ken. "Small Satellite Market Research Methods." In Handbook of Small Satellites, 1–12. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-20707-6_106-2.

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Troilo, Gabriele. "Methods for Developing Market Knowledge." In Marketing in Creative Industries, 137–72. London: Macmillan Education UK, 2015. http://dx.doi.org/10.1007/978-0-230-38023-3_6.

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Davidian, Ken. "Small Satellite Market Research Methods." In Handbook of Small Satellites, 799–810. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36308-6_106.

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Bebenroth, Ralf. "Evaluation Methods and Market Concentration." In International Business Mergers and Acquisitions in Japan, 139–52. Tokyo: Springer Japan, 2015. http://dx.doi.org/10.1007/978-4-431-54989-5_10.

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Kariya, Takeaki. "Optimal MTV Market Portfolio." In Quantitative Methods for Portfolio Analysis, 195–204. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1721-0_10.

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Pascucci, Andrea. "Continuous market models." In PDE and Martingale Methods in Option Pricing, 329–87. Milano: Springer Milan, 2011. http://dx.doi.org/10.1007/978-88-470-1781-8_10.

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Conference papers on the topic "The market methods"

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Horvat, J., S. Markovic, and M. Perencevic. "CADAC methods in market research." In Proceedings 23rd International Conference Information Technology Interfaces. ITI 2001. IEEE, 2001. http://dx.doi.org/10.1109/iti.2001.938022.

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Xing Yan and N. A. Chowdhury. "Electricity market clearing price forecasting in a deregulated electricity market." In 2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). IEEE, 2010. http://dx.doi.org/10.1109/pmaps.2010.5528949.

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Liu, Dun-nan, Heng Xie, and Hai-chao Wang. "Quantitative Analysis Methods for Electricity Market." In 2009 International Conference on Management and Service Science (MASS). IEEE, 2009. http://dx.doi.org/10.1109/icmss.2009.5304212.

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Siddiqi, Shams N. "Innovative Market Design and Mitigation Methods in the ERCOT Nodal Market." In 2007 IEEE Power Engineering Society General Meeting. IEEE, 2007. http://dx.doi.org/10.1109/pes.2007.385910.

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Herrmann, Markus, and Laura Hoyden. "Applied Webscraping in Market Research." In CARMA 2016 - 1st International Conference on Advanced Research Methods and Analytics. Valencia: Universitat Politècnica València, 2016. http://dx.doi.org/10.4995/carma2016.2016.3131.

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Modern Webscraping tools and APIs facilitate the extraction of information from the Internet significantly, especially if the data is not offered for download in a structured format. In this abstract we outline, that Webscraping, as a common practice to load, prepare and statistically analyze specific structured or unstructured data from the Internet, has become an essential application in Marketing and Data Science. Furthermore, we emphasize the importance of Open Data and social media data as a scraping target and illustrate examples of Open Data and social media data integration, Sentiment Analysis and website content classification as a utilization of Webscraping in a Market Research environment. While we argue that Webscraping of internet data is an enabler and driver of product innovation in Market Research it should also be noted that there are some legal restrictions involved.
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SAVANI, V. "OPTIMAL ESTIMATION OF PARAMETERS IN MARKET RESEARCH MODELS." In Computer Aided Methods in Optimal Design and Operations. WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812772954_0009.

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French, Nick, and Laura Gabrielli. "Pricing to Market - Property Valuation Methods revisited." In 22nd Annual European Real Estate Society Conference. European Real Estate Society, 2015. http://dx.doi.org/10.15396/eres2015_75.

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Kornilova, I. "Sociological research methods of real estate market." In ТЕНДЕНЦИИ РАЗВИТИЯ НАУКИ И ОБРАЗОВАНИЯ. НИЦ «Л-Журнал», 2017. http://dx.doi.org/10.18411/lj-31-03-2017-1-02.

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Landis, Warren, Sangwhan Cha, and Majid Shaalan. "On Optimization of Stock Market Prediction Methods." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9005612.

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Trnka, Andrej. "Market Basket Analysis with Data Mining methods." In 2010 International Conference on Networking and Information Technology (ICNIT 2010). IEEE, 2010. http://dx.doi.org/10.1109/icnit.2010.5508476.

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Reports on the topic "The market methods"

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Judd, Kenneth, and Sy-Ming Guu. Asymptotic Methods for Asset Market Equilibrium Analysis. Cambridge, MA: National Bureau of Economic Research, February 2001. http://dx.doi.org/10.3386/w8135.

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Gilshannon, S. T., and D. R. Brown. Review of methods for forecasting the market penetration of new technologies. Office of Scientific and Technical Information (OSTI), December 1996. http://dx.doi.org/10.2172/432867.

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Ji, Yi, and Ariel Yépez-García. Market Power in Electricity Generation sector: A Review of Methods and Applications. Inter-American Development Bank, August 2017. http://dx.doi.org/10.18235/0000894.

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Hall, George, and John Rust. Econometric Methods for Endogenously Sampled Time Series: The Case of Commodity Price Speculation in the Steel Market. Cambridge, MA: National Bureau of Economic Research, August 2002. http://dx.doi.org/10.3386/t0278.

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Peri, Giovanni, and Vasil Yasenov. The Labor Market Effects of a Refugee Wave: Applying the Synthetic Control Method to the Mariel Boatlift. Cambridge, MA: National Bureau of Economic Research, December 2015. http://dx.doi.org/10.3386/w21801.

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Grogger, Jeffrey. Markov Forecasting Methods for Welfare Caseloads. Cambridge, MA: National Bureau of Economic Research, October 2005. http://dx.doi.org/10.3386/w11682.

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Foerster, Andrew, Juan Rubio-Ramírez, Daniel Waggoner, and Tao Zha. Perturbation Methods for Markov-Switching DSGE Models. Cambridge, MA: National Bureau of Economic Research, August 2014. http://dx.doi.org/10.3386/w20390.

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Frick, Natalie, Tom Eckman, Greg Leventis, and Alan Sanstad. Methods to Incorporate Energy Efficiency in Electricity System Planning and Markets. Office of Scientific and Technical Information (OSTI), January 2021. http://dx.doi.org/10.2172/1762253.

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Schraml, Stephen. Evaluation of the Lagrangian Marker Method in CTH: Taylor Impact. Fort Belvoir, VA: Defense Technical Information Center, March 2015. http://dx.doi.org/10.21236/ada614160.

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de Groot, Oliver, Ceyhun Bora Durdu, and Enrique Mendoza. Approximately Right?: Global v. Local Methods for Open-Economy Models with Incomplete Markets. Cambridge, MA: National Bureau of Economic Research, November 2019. http://dx.doi.org/10.3386/w26426.

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