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Books on the topic 'Generalized method of moments estimation'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Generalized method of moments. Oxford: Oxford University Press, 2005.

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2

Lee, Myoung-jae. Methods of moments and semiparametric econometrics for limited dependent and variable models. New York: Springer, 1996.

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3

McFadden, Daniel. A method of simulated moments for estimation of discrete response models without numerical integration. Cambridge, Mass: Dept. of Economics, Massachusetts Institute of Technology, 1987.

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4

László, Mátyás, ed. Generalized method of moments estimation. Cambridge [England]: Cambridge University Press, 1999.

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5

Matyas, Laszlo, ed. Generalized Method of Moments Estimation. Cambridge University Press, 1999. http://dx.doi.org/10.1017/cbo9780511625848.

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6

Karakoç, Ekrem. Cross-National Test of the Theory. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198826927.003.0003.

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The previous chapter posed the primary research question and offered a new theory that encompassed two interrelated arguments. This chapter produces three hypotheses derived from the new theory offered in Chapter 2. Chapter 3 tests these arguments in a large-N study using multivariate statistical analysis. The first section discusses the operationalization of our main dependent and independent variables. It will also briefly outline a set of control variables and what the literature predicts regarding their effect on spending and inequality. These factors range from economic factors (globalization, inflation, female labor participation, economic development), political factors (partisanship, electoral systems, election cycle), and demographic factors. To correct for problems associated with the nature of panel data models, such as endogeneity, heteroskedasticity, and autocorrelation, it uses the Arellano-Bond estimation, which uses the Generalized Method of Moments. The rest of the chapter presents the results and offers its interpretation and conclusion.

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7

Sauter, Roger M. A method for estimation of generalized linear models when explanatory variables contain meaurement error. 1989.

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8

Geological Survey (U.S.), ed. Adjusted maximum likelihood estimation of the moments of lognormal populations from type 1 censored samples. [Denver, Colo.?]: Dept. of the Interior, U.S. Geological Survey, 1988.

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9

Adjusted maximum likelihood estimation of the moments of lognormal populations from type 1 censored samples. [Denver, Colo.?]: Dept. of the Interior, U.S. Geological Survey, 1988.

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10

Geological Survey (U.S.), ed. Adjusted maximum likelihood estimation of the moments of lognormal populations from type 1 censored samples. [Denver, Colo.?]: Dept. of the Interior, U.S. Geological Survey, 1988.

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11

Application of model based parameter estimation for RCS frequency response calculations using method of moments. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1998.

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12

Lee, Seung-Chul. A new power flow and sensitivity analysis method using a generalized power flow algorithm, interactive computer graphics, and artificial intelligence. 1985.

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13

Inkmann, Joachim. Conditional Moment Estimation of Nonlinear Equation Systems: With an Application to an Oligopoly Model of Cooperative R&D (Lecture Notes in Economics and Mathematical Systems). Springer, 2000.

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14

Makatjane, Katleho, and Roscoe van Wyk. Identifying structural changes in the exchange rates of South Africa as a regime-switching process. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/919-8.

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Exchange rate volatility is said to exemplify the economic health of a country. Exchange rate break points (known as structural breaks) have a momentous impact on the macroeconomy of a country. Nonetheless, this country study makes use of both unsupervised and supervised machine learning algorithms to classify structural changes as regime shifts in real exchange rates in South Africa. Weekly data for the period January 2003–June 2020 are used. To these data we apply both non-linear principal component analysis and Markov-switching generalized autoregressive conditional heteroscedasticity. The former approach is used to reduce the dimensionality of the data using an orthogonal linear transformation by preserving the statistical variance of the data, with the proviso that a new trait is non-linearly independent, and it identifies the number of regime switches that are to be used in the Markov-switching model. The latter is used to partition the variance in each regime by allowing an estimation of multiple break transitions. The transition breakpoints estimates derived from this machine learning approach produce results that are comparable to other methods on similar system sizes. Application of these methods shows that the machine learning approach can also be employed to identify structural changes as a regime-switching process. During times of financial crisis, the growing concern over exchange rate volatility, including its adverse effects on employment and growth, broadens the debates on exchange rate policies. Our results should help the South African monetary policy committee to anticipate when exchange rates will pick up and be prepared for the effects of periods of high exchange rates.

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15

Golan, Amos. Info-Metrics and Statistical Inference. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199349524.003.0013.

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In this chapter I concentrate on continuous inferential problems: problems where the dependent variable is continuous, such as classical regression problems. As in the previous chapter, using duality theory, I show that the info-metrics framework is general enough to include the class of information-theoretic methods as a special case. The formulation is developed for the classical regression problem, but the results apply to many other problems. A detailed discussion of the benefits and costs of using the info-metrics framework is provided and contrasted with other approaches. I use theoretical examples and policy-relevant applications to demonstrate the method. The common problem of misspecification is also discussed and studied within the info-metrics framework. I show that a misspecified model and a correctly specified one can yield similar answers. The appendices provide detailed discussions of the generalized method of moments and the Bayesian method of moments. Both are connected to info-metrics.

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16

Mas, André, and Besnik Pumo. Linear Processes for Functional Data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.3.

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This article provides an overview of the basic theory and applications of linear processes for functional data, with particular emphasis on results published from 2000 to 2008. It first considers centered processes with values in a Hilbert space of functions before proposing some statistical models that mimic or adapt the scalar or finite-dimensional approaches for time series. It then discusses general linear processes, focusing on the invertibility and convergence of the estimated moments and a general method for proving asymptotic results for linear processes. It also describes autoregressive processes as well as two issues related to the general estimation problem, namely: identifiability and the inverse problem. Finally, it examines convergence results for the autocorrelation operator and the predictor, extensions for the autoregressive Hilbertian (ARH) model, and some numerical aspects of prediction when the data are curves observed at discrete points.

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