Relevant bibliographies by topics / Economics estimation

Contents

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

Academic literature on the topic 'Economics estimation'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Economics estimation"

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Chen, Liqiong, Antonio F. Galvao, and Suyong Song. "Quantile Regression with Generated Regressors." Econometrics 9, no. 2 (April 12, 2021): 16. http://dx.doi.org/10.3390/econometrics9020016.

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This paper studies estimation and inference for linear quantile regression models with generated regressors. We suggest a practical two-step estimation procedure, where the generated regressors are computed in the first step. The asymptotic properties of the two-step estimator, namely, consistency and asymptotic normality are established. We show that the asymptotic variance-covariance matrix needs to be adjusted to account for the first-step estimation error. We propose a general estimator for the asymptotic variance-covariance, establish its consistency, and develop testing procedures for linear hypotheses in these models. Monte Carlo simulations to evaluate the finite-sample performance of the estimation and inference procedures are provided. Finally, we apply the proposed methods to study Engel curves for various commodities using data from the UK Family Expenditure Survey. We document strong heterogeneity in the estimated Engel curves along the conditional distribution of the budget share of each commodity. The empirical application also emphasizes that correctly estimating confidence intervals for the estimated Engel curves by the proposed estimator is of importance for inference.
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Xu, Ke-Li. "REWEIGHTED FUNCTIONAL ESTIMATION OF DIFFUSION MODELS." Econometric Theory 26, no. 2 (September 30, 2009): 541–63. http://dx.doi.org/10.1017/s0266466609100087.

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The local linear method is popular in estimating nonparametric continuous-time diffusion models, but it may produce negative results for the diffusion (or volatility) functions and thus lead to insensible inference. We demonstrate this using U.S. interest rate data. We propose a new functional estimation method of the diffusion coefficient based on reweighting the conventional Nadaraya–Watson estimator. It preserves the appealing bias properties of the local linear estimator and is guaranteed to be nonnegative in finite samples. A limit theory is developed under mild requirements (recurrence) of the data generating mechanism without assuming stationarity or ergodicity.
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Rolling, Craig A., Yuhong Yang, and Dagmar Velez. "COMBINING ESTIMATES OF CONDITIONAL TREATMENT EFFECTS." Econometric Theory 35, no. 6 (November 6, 2018): 1089–110. http://dx.doi.org/10.1017/s0266466618000397.

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Estimating a treatment’s effect on an outcome conditional on covariates is a primary goal of many empirical investigations. Accurate estimation of the treatment effect given covariates can enable the optimal treatment to be applied to each unit or guide the deployment of limited treatment resources for maximum program benefit. Applications of conditional treatment effect estimation are found in direct marketing, economic policy, and personalized medicine. When estimating conditional treatment effects, the typical practice is to select a statistical model or procedure based on sample data. However, combining estimates from the candidate procedures often provides a more accurate estimate than the selection of a single procedure. This article proposes a method of model combination that targets accurate estimation of the treatment effect conditional on covariates. We provide a risk bound for the resulting estimator under squared error loss and illustrate the method using data from a labor skills training program.
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Newey, Whitney K. "Nonparametric Instrumental Variables Estimation." American Economic Review 103, no. 3 (May 1, 2013): 550–56. http://dx.doi.org/10.1257/aer.103.3.550.

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In many economic models, objects of interest are functions which satisfy conditional moment restrictions. Economics does not restrict the functional form of these models, motivating nonparametric methods. In this paper we review identification results and describe a simple nonparametric instrumental variables (NPIV) estimator. We also consider a simple method of inference. In addition we show how the ability to uncover nonlinearities with conditional moment restrictions is related to the strength of the instruments. We point to applications where important nonlinearities can be found with NPIV and applications where they cannot.
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Chen, Songnian, and Shakeeb Khan. "SEMIPARAMETRIC ESTIMATION OF NONSTATIONARY CENSORED PANEL DATA MODELS WITH TIME VARYING FACTOR LOADS." Econometric Theory 24, no. 5 (May 14, 2008): 1149–73. http://dx.doi.org/10.1017/s0266466608080468.

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We propose an estimation procedure for a semiparametric panel data censored regression model in which the error terms may be subject to general forms of nonstationarity. Specifically, we allow for heteroskedasticity over time and a time varying factor load on the individual specific effect. Empirically, estimation of this model would be of interest to explore how returns to unobserved skills change over time—see, e.g., Chay (1995, manuscript, Princeton University) and Chay and Honoré (1998, Journal of Human Resources 33, 4–38). We adopt a two-stage procedure based on nonparametric median regression, and the proposed estimator is shown to be $\sqrt{n}$ -consistent and asymptotically normal. The estimation procedure is also useful in the group effect setting, where estimation of the factor load would be empirically relevant in the study of the intergenerational correlation in income, explored in Solon (1992, American Economic Review 82, 393–408; 1999, Handbook of Labor Economics, vol. 3, 1761–1800) and Zimmerman (1992, American Economic Review 82, 409–429).
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ALAN, SULE, and MARTIN BROWNING. "Estimating Intertemporal Allocation Parameters using Synthetic Residual Estimation." Review of Economic Studies 77, no. 4 (August 13, 2010): 1231–61. http://dx.doi.org/10.1111/j.1467-937x.2010.00607.x.

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Shimizu, Chihiro, Koji Karato, and Kiyohiko Nishimura. "Nonlinearity of housing price structure." International Journal of Housing Markets and Analysis 7, no. 4 (September 30, 2014): 459–88. http://dx.doi.org/10.1108/ijhma-10-2013-0055.

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Purpose – The purpose of this article, starting from linear regression, was to estimate a switching regression model, nonparametric model and generalized additive model as a semi-parametric model, perform function estimation with multiple nonlinear estimation methods and conduct comparative analysis of their predictive accuracy. The theoretical importance of estimating hedonic functions using a nonlinear function form has been pointed out in ample previous research (e.g. Heckman et al. (2010). Design/methodology/approach – The distinctive features of this study include not only our estimation of multiple nonlinear model function forms but also the method of verifying predictive accuracy. Using out-of-sample testing, we predicted and verified predictive accuracy by performing random sampling 500 times without replacement for 9,682 data items (the same number used in model estimation), based on data for the years before and after the year used for model estimation. Findings – As a result of estimating multiple models, we believe that when it comes to hedonic function estimation, nonlinear models are superior based on the strength of predictive accuracy viewed in statistical terms and on graphic comparisons. However, when we examined predictive accuracy using out-of-sample testing, we found that the predictive accuracy was inferior to linear models for all nonlinear models. Research limitations/implications – In terms of the reason why the predictive accuracy was inferior, it is possible that there was an overfitting in the function estimation. Because this research was conducted for a specific period of time, it needs to be developed by expanding it to multiple periods over which the market fluctuates dynamically and conducting further analysis. Practical implications – Many studies compare predictive accuracy by separating the estimation model and verification model using data at the same point in time. However, when attempting practical application for auto-appraisal systems and the like, it is necessary to estimate a model using past data and make predictions with respect to current transactions. It is possible to apply this study to auto-appraisal systems. Social implications – It is recognized that housing price fluctuations caused by the subprime crisis had a massive impact on the financial system. The findings of this study are expected to serve as a tool for measuring housing price fluctuation risks in the financial system. Originality/value – While the importance of nonlinear estimation when estimating hedonic functions has been pointed out in theoretical terms, there is a noticeable lag when it comes to testing based on actual data. Given this, we believe that our verification of nonlinear estimation’s validity using multiple nonlinear models is significant not just from an academic perspective – it may also have practical applications.
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Fernández-Val, Iván, and Martin Weidner. "Fixed Effects Estimation of Large-TPanel Data Models." Annual Review of Economics 10, no. 1 (August 2, 2018): 109–38. http://dx.doi.org/10.1146/annurev-economics-080217-053542.

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This article reviews recent advances in fixed effects estimation of panel data models for long panels, where the number of time periods is relatively large. We focus on semiparametric models with unobserved individual and time effects, where the distribution of the outcome variable, conditional on covariates and unobserved effects, is specified parametrically while the distribution of the unobserved effects is left unrestricted. In contrast to existing reviews on long panels, we discuss models with both individual and time effects, split-panel jackknife bias corrections, unbalanced panels, distribution and quantile effects, and other extensions. Understanding and correcting the incidental parameter bias caused by the estimation of many fixed effects are our main focuses, and the unifying theme is that the order of this bias is given by the simple formula p/ n for all models discussed, with p being the number of estimated parameters and n the total sample size.
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Hubner, Stefan, and Pavel Čížek. "Quantile-based smooth transition value at risk estimation." Econometrics Journal 22, no. 3 (June 6, 2019): 241–61. http://dx.doi.org/10.1093/ectj/utz009.

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Summary Value at risk models are concerned with the estimation of conditional quantiles of a time series. Formally, these quantities are a function of conditional volatility and the respective quantile of the innovation distribution. The former is often subject to asymmetric dynamic behaviour, e.g., with respect to past shocks. In this paper, we propose a model in which conditional quantiles follow a generalised autoregressive process governed by two parameter regimes with their weights determined by a smooth transition function. We develop a two-step estimation procedure based on a sieve estimator, approximating conditional volatility by using composite quantile regression, which is then used in the generalised autoregressive conditional quantile estimation. We show that the estimator is consistent and asymptotically normal, and we complement the results with a simulation study. In our empirical application, we consider daily returns of the German equity index (DAX) and the USD/GBP exchange rate. Although only the latter follows a two-regime model, we find that our model performs well in terms of out-of-sample prediction in both cases.
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Ubesie, Cyril Madubuko, Amalachukwu Ananwude, Ezechi Nwanekpe Cyracus, and Ebe Emmanuel. "Does Fiscal Policy Tools have the Potential to Stimulate Performance of Manufacturing Sector in Nigeria?" Finance & Economics Review 2, no. 3 (October 1, 2020): 33–51. http://dx.doi.org/10.38157/finance-economics-review.v2i3.163.

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Purpose: There is no denying the fact that the Nigerian manufacturing sector is not performing up to the expectation. The poor performance of the manufacturing sector is attributed largely to the poor state of basic infrastructures, especially power supply, and good road networks. To this end, this study examined the potential of fiscal policy to stimulate manufacturing sector performance in Nigeria. Methods: The model estimation employed the Ordinary Least Square (OLS) estimation technique, while the effect of estimation was carried out using the Granger causality test based on the data from the Central Bank of Nigeria (CBN) and Federal Inland Revenue Service (FIRS) for the period of 1986 to 2019. Results: The result of the analysis revealed that recurrent expenditure has no significant effect on manufacturing sector performance. However, capital expenditure, fiscal deficit, and the company’s income tax significantly affect manufacturing sector performance. Implications: The Federal, State, and Local governments should stop wasteful expenditure on unnecessary entertainment on meetings, seminars, workshops, foreign trips, etc. to increase spending on basic industrial infrastructures, most importantly on the power supply and road network to stimulate the manufacturing sector performance.
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Dissertations / Theses on the topic "Economics estimation"

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Busetti, Fabio. "Testing and estimation of models with stochastic trends." Thesis, London School of Economics and Political Science (University of London), 2001. http://etheses.lse.ac.uk/2257/.

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The thesis considers time series and econometric models with stochastic trend components. Locally Best Invariant tests for the presence of stochastic trends are constructed and their asymptotic distributions derived. Particular attention is paid to models with structural breaks, as the tests have high power also against alternative hypotheses in which the trends of the series contain a small number of breaks but are otherwise deterministic. Asymptotic critical values of the tests are tabulated for series with a single breakpoint. A modification of the LBI statistic is then proposed, for which the asymptotic distribution depends only on the number of the breaks and not on their location. Common stochastic trends imply cointegration and thus testing the number of common trends can also be regarded as testing the dimension of the cointegration space. A test for common trends recently proposed in the literature is extended to series which contain structural breaks. Testing for the presence of a nonstationary seasonal component is then examined. The LBI test, adjusted for serial correlation by means of a nonparametric correction, is extended in various directions and its performance is compared with that of a parametric test. Representation, estimation and tests of cointegrated structural time series models form the subject of one chapter, where numerous links with the literature on vector autoregressions are established. Panel data regression models where the individual effects take the form of individual specific random walks are considered in the last chapter. Imposing the constraint of a common signal-to-noise ratio across individuals makes the maximum likelihood estimator computationally feasible also when the number of units in the cross section is large. For these models an average LBI test for stationarity and for the presence of fixed effects is proposed.
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Venetis, Ioannis A. "Essays on the estimation of fractionally cointegrated systems." Thesis, University of Essex, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364513.

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Sherrell, Neill. "The estimation and specification of spatial econometric models." Thesis, University of Bristol, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.281861.

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Hu, Wanhong. "Estimation of dynamic heterogeneous panel data models." Connect to resource, 1996. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=osu1266934002.

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Ichimura, Hidehiko. "Estimation of single index models." Thesis, Massachusetts Institute of Technology, 1987. http://hdl.handle.net/1721.1/14733.

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Demirer, Mert. "Essays on production function estimation." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/127028.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 193-201).
This first chapter develops a new method for estimating production functions with factor-augmenting technology and assesses its economic implications. The method does not impose parametric restrictions and generalizes prior approaches that rely on the CES production function. I first extend the canonical Olley-Pakes framework to accommodate factor-augmenting technology. Then, I show how to identify output elasticities based on a novel control variable approach and the optimality of input expenditures. I use this method to estimate output elasticities and markups in manufacturing industries in the US and four developing countries. Neglecting labor-augmenting productivity and imposing parametric restrictions mismeasures output elasticities and heterogeneity in the production function. My estimates suggest that standard models (i) underestimate capital elasticity by up to 70 percent (ii) overestimate labor elasticity by up to 80 percent.
These biases propagate into markup estimates inferred from output elasticities: markups are overestimated by 20 percentage points. Finally, heterogeneity in output elasticities also affects estimated trends in markups: my estimates point to a much more muted markup growth (about half) in the US manufacturing sector than recent estimates. The second chapter develops partial identification results that are robust to deviations from the commonly used control function approach assumptions and measurement errors in inputs. In particular, the model (i) allows for multi-dimensional unobserved heterogeneity,(ii) relaxes strict monotonicity to weak monotonicity, (iii) accommodates a more flexible timing assumption for capital. I show that under these assumptions production function parameters are partially identified by an 'imperfect proxy' variable via moment inequalities. Using these moment inequalities, I derive bounds on the parameters and propose an estimator.
An empirical application is presented to quantify the informativeness of the identified set. The third chapter develops an approach in which endogenous networks is a source of identification in estimations with network data. In particular, I study a linear model where network data can be used to control for unobserved heterogeneity and partially identify the parameters of the linear model. My method does not rely on a parametric model of network formation. Instead, identification is achieved by assuming that the network satisfies latent homophily - the tendency of individuals to be linked with others who are similar to themselves. I first provide two definitions of homophily: weak and strong homophily. Then, based on these definitions, I characterize the identified sets and show that they are bounded under weak conditions.
Finally, to illustrate the method in an empirical setting, I estimate the effects of education on risk preferences and peer effects using social network data from 150 Chinese villages.
by Mert Demirer.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Economics
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Henry, Marc. "Long memory in time series : semiparametric estimation and conditional heteroscedasticity." Thesis, London School of Economics and Political Science (University of London), 1999. http://etheses.lse.ac.uk/1581/.

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This dissertation considers semiparametric spectral estimates of temporal dependence in time series. Semiparametric frequency domain methods rely on a local parametric specification of the spectral density in a neighbourhood of the frequency of interest. Therefore, such methods can be applied to the analysis of singularities in the spectral density at frequency zero to identify long memory. They can also serve as the basis for the estimation of regular parts of the spectrum. One thereby avoids inconsistency that might arise from misspecification of dynamics at frequencies other than the frequency under focus. In case of long financial time series, the loss of efficiency with respect to fully parametric methods (or full band estimates) may be offset by the greater robustness properties. However, if semiparametric frequency domain methods are to be valid tools for inference on financial time series, they need to allow for conditional heteroscedasticity which is now recognized as a dominant feature of asset returns. This thesis provides a general specification which allows the time series under investigation to exhibit this type of behaviour. Two statistics are considered. The weighted periodogram statistic provides asymptotically normal point estimates of the spectral density at zero frequency for weakly dependent processes. The local Whittle (or local frequency domain maximum likelihood) estimate provides asymptotically normal estimates of long memory in possibly strongly dependent processes. The asymptotic results hold irrespective of the behaviour of the spectral density at non zero frequencies. The asymptotic variances are identical to those that obtain under conditional homogeneity in the distribution of the innovations to the observed process. In semiparametric frequency domain estimation, the choice of bandwidth is crucial. Indeed, it determines the asymptotic efficiency of the procedure. Optimal choices of bandwidth are derived, balancing asymptotic bias and asymptotic variance. Feasible versions of these optimal band-widths are proposed, and their performance is assessed in an extensive Monte Carlo study where the innovations to the observed process are simulated under numerous parametric submodels of the general specification, covering a wide range of persistence properties both in the levels and in the squares of the observed process. The techniques described above are applied to the analysis of temporal dependence and persistence in intra-day foreign exchange rate returns and their volatilities. While no strong indication of returns predictability is found in the former, a clear pattern arises in the latter, indicating that intra-day exchange rate returns are well described as martingale differences with weakly stationary and fractionally cointegrated long memory volatilities.
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McCrorie, James Roderick. "Some topics in the estimation of continuous time econometric models." Thesis, University of Essex, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.388615.

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Arellano, Gomez Manuel. "Estimation and testing of dynamic econometric models from panel data." Thesis, London School of Economics and Political Science (University of London), 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.261293.

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Feinstein, Jonathan S. "Detection controlled estimation : theory and applications." Thesis, Massachusetts Institute of Technology, 1987. http://hdl.handle.net/1721.1/14868.

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Books on the topic "Economics estimation"

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Conniffe, Denis. Likelihood and estimation. Dublin: Economic and Social Research Institute, 1986.

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Koenker, Roger. M-Estimation of multivariate regressions. Champaign: University of Illinois at Urbana-Champaign, 1988.

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Dahlberg, Matz. Essays on estimation methods and local public economics. Uppsala: Dept. of Economics, Uppsala University, 1997.

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Conniffe, Denis. Expected maximum log liklihood estimation. Dublin: Economic and Social Research Institute, 1988.

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1952-, Frei Gertrud, and Schips Bernd, eds. Estimation of disequilibrium models. Berlin: Springer-Verlag, 1986.

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Bohrer, Robert. Preliminary test estimation for the second order autoregression. Champaign: University of Illinois at Urbana-Champaign, 1992.

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Melino, Angelo. Estimation of unit averaged diffusion processes. Toronto: University of Toronto, 1985.

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Systems life cycle costing: Economics analysis, estimation, and management. Boca Raton: Taylor & Francis, 2011.

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Pesendorfer, Martin. Identification and estimation of dynamic games. Cambridge, MA: National Bureau of Economic Research, 2003.

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Kolstad, Charles D. Dynamic specification error in cost function and factor demand estimation. Champaign: University of Illinois at Urbana-Champaign, 1992.

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Book chapters on the topic "Economics estimation"

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Bowers, David. "Estimation." In Statistics for Economics and Business, 120–36. London: Palgrave Macmillan UK, 1991. http://dx.doi.org/10.1007/978-1-349-21346-7_11.

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Ubøe, Jan. "Estimation." In Springer Texts in Business and Economics, 159–75. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70936-9_8.

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Nerlove, Marc, and Francis X. Diebold. "Estimation." In The New Palgrave Dictionary of Economics, 3899–905. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_627.

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Nerlove, Marc, and Francis X. Diebold. "Estimation." In The New Palgrave Dictionary of Economics, 1–6. London: Palgrave Macmillan UK, 1987. http://dx.doi.org/10.1057/978-1-349-95121-5_627-1.

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Hill, Stephen. "Demand theory and estimation." In Managerial Economics, 100–135. London: Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-19852-8_5.

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Hill, Stephen. "Cost theory and estimation." In Managerial Economics, 162–82. London: Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-19852-8_7.

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Ben-Haim, Yakov. "Estimation and Forecasting." In Info-Gap Economics, 179–210. London: Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230277328_6.

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Beckmann, Martin J. "Estimation." In Texts and Monographs in Economics and Mathematical Systems, 63–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-83273-4_7.

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Wright, Dennis. "Cost Estimation and Economics." In Basic Programs for Chemical Engineers, 136–80. Dordrecht: Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4121-2_5.

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Steigerwald, Douglas G. "Adaptive Estimation." In The New Palgrave Dictionary of Economics, 1–4. London: Palgrave Macmillan UK, 2008. http://dx.doi.org/10.1057/978-1-349-95121-5_2420-1.

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Conference papers on the topic "Economics estimation"

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Ross, J. G. "The Philosophy of Reserve Estimation." In SPE Hydrocarbon Economics and Evaluation Symposium. Society of Petroleum Engineers, 1997. http://dx.doi.org/10.2118/37960-ms.

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Babeshko, Ludmila. "Instrumental estimation of systems of simultaneous equations: interrelation of methods." In System analysis in economics – 2018. Prometheus publishing house, 2018. http://dx.doi.org/10.33278/sae-2018.eng.192-195.

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Hendrych, Radek, and Tomáš Cipra. "Recursive estimation of the multivariate EWMA process." In International Days of Statistics and Economics 2019. Libuše Macáková, MELANDRIUM, 2019. http://dx.doi.org/10.18267/pr.2019.los.186.46.

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Цвиль, Мария, Mariya Tsvil, Елена Гришина, and Elena Grishina. "ESTIMATION OF THE VOLUME OF NATURAL GAS EXPORTS RUSSIA WITH THE USE OF A MULTIPLICATIVE TIME SERIES MODEL." In Mathematics in Economics. AUS PUBLISHERS, 2018. http://dx.doi.org/10.26526/conferencearticle_5c24b1d0bac002.81258719.

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This article is devoted to the analysis of the export structure of Russia. The paper assesses the volume of natural gas exports as one of the main resources for the export of this country. A multiplicative time series model is constructed. The forecast of the expected volume of natural gas exports for the second half of 2018 is made, according to which the conclusions and forecasts of the state of the studied sphere of the Russian economy are drawn.
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Chen, Yiling, Nicole Immorlica, Brendan Lucier, Vasilis Syrgkanis, and Juba Ziani. "Optimal Data Acquisition for Statistical Estimation." In EC '18: ACM Conference on Economics and Computation. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3219166.3219195.

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Pushkarev, Andrey, Anna Sennikova, and Oleg Mariev. "ECONOMETRIC ESTIMATION OF MARKET SELECTION IN RUSSIA: DIFFERENT PERFORMANCE INDICATORS." In 13th Economics & Finance Virtual Conference, Prague. International Institute of Social and Economic Sciences, 2020. http://dx.doi.org/10.20472/efc.2020.013.013.

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Zlakomanova, E. N. "Estimation of the density of the institutional environment of a tourist-recreational cluster." In Scientific dialogue: Economics and Management. ЦНК МОАН, 2020. http://dx.doi.org/10.18411/sciencepublic-08-05-2020-01.

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Karacaer, Caner, and Mustafa Onur. "Analytical Probabilistic Reserve Estimation by Volumetric Method and Aggregation of Resources." In SPE Hydrocarbon Economics and Evaluation Symposium. Society of Petroleum Engineers, 2012. http://dx.doi.org/10.2118/162875-ms.

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Lima, Gabriel A. C., and Saul B. Suslick. "A Quantitative Method for Estimation of Volatility of Oil Production Projects." In SPE Hydrocarbon Economics and Evaluation Symposium. Society of Petroleum Engineers, 2005. http://dx.doi.org/10.2118/94661-ms.

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Chen, Yiling, and Shuran Zheng. "Prior-free Data Acquisition for Accurate Statistical Estimation." In EC '19: ACM Conference on Economics and Computation. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3328526.3329564.

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Reports on the topic "Economics estimation"

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Gamboa-Estrada, Fredy, and Jose Vicente Romero. Common and idiosyncratic movements in Latin-American Exchange Rates. Banco de la República, April 2021. http://dx.doi.org/10.32468/be.1158.

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We propose a simple theoretical and empirical approach to differentiate between common and idiosyncratic exchange rate movements in 5 Latin-American economies: Brazil, Chile, Colombia, Mexico, and Peru. Our approach allows us to distinguish the effects on exchange rates of a regional exchange rate common factor and macroeconomic fundamentals differentials. The methodology and estimation strategy are suitable for both low and high frequency settings. We provide evidence that the regional common factor has a significant effect on the dynamics of the Latin-American exchange rates. In our estimations the relation between exchange rates and the common factor is contemporaneous and stable during the studied period.
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2

Hall, Douglas G., Richard T. Hunt, Kelly S. Reeves, and Greg R. Carroll. Estimation of economic parameters of U.S. hydropower resources. Office of Scientific and Technical Information (OSTI), June 2003. http://dx.doi.org/10.2172/1218138.

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3

Chen, Xiaohong, Jack Favilukis, and Sydney Ludvigson. An Estimation of Economic Models with Recursive Preferences. Cambridge, MA: National Bureau of Economic Research, June 2011. http://dx.doi.org/10.3386/w17130.

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4

Chen, Xiaohong, Jack Favilukis, and Sydney Ludvigson. An estimation of economic models with recursive preferences. Cemmap, October 2012. http://dx.doi.org/10.1920/wp.cem.2012.3212.

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5

Humphreys, S. L., J. A. Rollstin, and J. N. Ridgely. SECPOP90: Sector population, land fraction, and economic estimation program. Office of Scientific and Technical Information (OSTI), September 1997. http://dx.doi.org/10.2172/534482.

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6

Simoni, Anna, Lars Nesheim, and Stefan Hoderlein. Semiparametric estimation of random coefficients in structural economic models. Institute for Fiscal Studies, April 2012. http://dx.doi.org/10.1920/wp.cem.2012.0912.

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7

Dime, Roselle, Juzhong Zhuang, and Edimon Ginting. Estimating Fiscal Multipliers in Selected Asian Economies. Asian Development Bank, August 2021. http://dx.doi.org/10.22617/wps210309-2.

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The surge of the coronavirus disease (COVID-19) pandemic has driven countries worldwide to launch substantial stimulus packages to support economic recovery. This paper estimates effects of fiscal measures on output using data from 2000 to 2019 for a panel of nine developing Asian economies and a vector autoregression model. Results show that (i) the 4-quarter and 8-quarter cumulative fiscal multipliers for general government spending range between 0.73 and 0.88 in baselines, in line with recent estimates for developed countries but larger than those for developing countries; (ii) government spending is more effective than tax cuts in boosting the economy; and (iii) an accommodative monetary policy regime can make fiscal measures more effective.
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8

Looney, Robert, and David Schrady. Estimating Economic Benefits of Naval Forward Presence. Fort Belvoir, VA: Defense Technical Information Center, October 2000. http://dx.doi.org/10.21236/ada389471.

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9

George, Daryl. ESP: Economics of Shipyard Painting, Bid Estimating Transfer Study. Fort Belvoir, VA: Defense Technical Information Center, November 1993. http://dx.doi.org/10.21236/ada454978.

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10

Nieves, L. A., and J. J. Tawil. The economic costs of radiation-induced health effects: Estimation and simulation. Office of Scientific and Technical Information (OSTI), August 1988. http://dx.doi.org/10.2172/6768759.

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