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Journal articles on the topic 'Unobserved components models'

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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1

Harvey, Andrew. "Testing in unobserved components models." Journal of Forecasting 20, no. 1 (2001): 1–19. http://dx.doi.org/10.1002/1099-131x(200101)20:1<1::aid-for764>3.0.co;2-3.

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2

Hotta, Luiz Koodi. "IDENTIFICATION OF UNOBSERVED COMPONENTS MODELS." Journal of Time Series Analysis 10, no. 3 (1989): 259–70. http://dx.doi.org/10.1111/j.1467-9892.1989.tb00027.x.

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3

Morley, James, Irina B. Panovska, and Tara M. Sinclair. "TESTING STATIONARITY WITH UNOBSERVED-COMPONENTS MODELS." Macroeconomic Dynamics 21, no. 1 (2016): 160–82. http://dx.doi.org/10.1017/s1365100515000437.

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In the aftermath of the global financial crisis, competing measures of the trend in macroeconomic variables such as U.S. real GDP have featured prominently in policy debates. A key question is whether large shocks to macroeconomic variables will have permanent effects—i.e., in econometric terms, do the data contain stochastic trends? Unobserved-components models provide a convenient way to estimate stochastic trends for time series data, with their existence typically motivated by stationarity tests that allow at most a deterministic trend under the null hypothesis. However, given the small sa

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4

Proietti, Tommaso. "Unobserved components models with correlated disturbances." Statistical Methods and Applications 12, no. 3 (2004): 277–92. http://dx.doi.org/10.1007/s10260-003-0074-y.

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5

Harvey, Andrew C., and Siem Jan Koopman. "Diagnostic Checking of Unobserved-Components Time Series Models." Journal of Business & Economic Statistics 10, no. 4 (1992): 377. http://dx.doi.org/10.2307/1391813.

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6

Harvey, Andrew C., and Siem Jan Koopman. "Diagnostic Checking of Unobserved-Components Time Series Models." Journal of Business & Economic Statistics 10, no. 4 (1992): 377–89. http://dx.doi.org/10.1080/07350015.1992.10509913.

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7

Diebold, Francis X., and Marc Nerlove. "Prediction, Extraction, and Estimation in Unobserved Components Models." Econometric Theory 3, no. 2 (1987): 305. http://dx.doi.org/10.1017/s0266466600010367.

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8

Hooda, Ekta, and Urmil Verma. "Unobserved Components Model for Forecasting Sugarcane Yield in Haryana." Journal of Applied and Natural Science 11, no. 3 (2019): 661–65. http://dx.doi.org/10.31018/jans.v11i3.2144.

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Unlike classical regression analysis, the state space models have time-dependent parameters and provide a flexible class of dynamic and structural time series models. The unobserved component model (UCM) is a special type of state space models widely used to analyze and forecast time series. The present investigation has been carried out to study the trend of sugarcane(gur) yield in five districts (Ambala, Karnal, Panipat, Yamunanagar and Kurukshetra) of Haryana state using the unobserved component models with level, trend and irregular components. For this purpose, the time series data on sug

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9

Harvey, Andrew, and Siem Jan Koopman. "Signal extraction and the formulation of unobserved components models." Econometrics Journal 3, no. 1 (2000): 84–107. http://dx.doi.org/10.1111/1368-423x.00040.

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10

Trenkler, Carsten, and Enzo Weber. "On the identification of multivariate correlated unobserved components models." Economics Letters 138 (January 2016): 15–18. http://dx.doi.org/10.1016/j.econlet.2015.11.009.

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11

Fiorentini, Gabriele, and Agustín Maravall. "Unobserved components in ARCH models: An application to seasonal adjustment." Journal of Forecasting 15, no. 3 (1996): 175–201. http://dx.doi.org/10.1002/(sici)1099-131x(199604)15:3<175::aid-for618>3.0.co;2-r.

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12

Koopman, Siem Jan, and Kai Ming Lee. "Seasonality with trend and cycle interactions in unobserved components models." Journal of the Royal Statistical Society: Series C (Applied Statistics) 58, no. 4 (2009): 427–48. http://dx.doi.org/10.1111/j.1467-9876.2009.00661.x.

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13

Villegas, Marco A., and Diego J. Pedregal. "Automatic selection of unobserved components models for supply chain forecasting." International Journal of Forecasting 35, no. 1 (2019): 157–69. http://dx.doi.org/10.1016/j.ijforecast.2017.11.001.

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14

Pedregal, Diego J., Fausto P. Garcı́a, and Felix Schmid. "RCM2 predictive maintenance of railway systems based on unobserved components models." Reliability Engineering & System Safety 83, no. 1 (2004): 103–10. http://dx.doi.org/10.1016/j.ress.2003.09.020.

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15

Nagakura, Daisuke. "A note on the two assumptions of standard unobserved components models." Economics Letters 100, no. 1 (2008): 123–25. http://dx.doi.org/10.1016/j.econlet.2007.12.015.

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16

Koopman, Siem Jan, and Marius Ooms. "Forecasting daily time series using periodic unobserved components time series models." Computational Statistics & Data Analysis 51, no. 2 (2006): 885–903. http://dx.doi.org/10.1016/j.csda.2005.09.009.

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17

Basistha, Arabinda. "Hours per capita and productivity: evidence from correlated unobserved components models." Journal of Applied Econometrics 24, no. 1 (2008): 187–206. http://dx.doi.org/10.1002/jae.1013.

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18

Kloudová, Dana. "UNOBSERVED COMPONENTS MODELS FOR ESTIMATING THE OUTPUT GAP OF THE CZECH ECONOMY." Acta academica karviniensia 13, no. 3 (2013): 95–105. http://dx.doi.org/10.25142/aak.2013.046.

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19

Ercolani, Joanne S. "CYCLICAL TRENDS IN CONTINUOUS TIME MODELS." Econometric Theory 25, no. 4 (2009): 1112–19. http://dx.doi.org/10.1017/s0266466608090440.

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It is undoubtedly desirable that econometric models capture the dynamic behavior, like trends and cycles, observed in many economic processes. Building models with such capabilities has been an important objective in the continuous time econometrics literature, for instance, the cyclical growth models of Bergstrom (1966); the economy-wide macroeconometric models of, for example, Bergstrom and Wymer (1976); unobserved stochastic trends of Harvey and Stock (1988 and 1993) and Bergstrom (1997); and differential-difference equations of Chambers and McGarry (2002). This paper considers continuous t

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20

Çatık, A. Nazif, and Mehmet Karaçuka. "A COMPARATIVE ANALYSIS OF ALTERNATIVE UNIVARIATE TIME SERIES MODELS IN FORECASTING TURKISH INFLATION." Journal of Business Economics and Management 13, no. 2 (2012): 275–93. http://dx.doi.org/10.3846/16111699.2011.620135.

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This paper analyses inflation forecasting power of artificial neural networks with alternative univariate time series models for Turkey. The forecasting accuracy of the models is compared in terms of both static and dynamic forecasts for the period between 1982:1 and 2009:12. We find that at earlier forecast horizons conventional models, especially ARFIMA and ARIMA, provide better one-step ahead forecasting performance. However, unobserved components model turns out to be the best performer in terms of dynamic forecasts. The superiority of the unobserved components model suggests that inflatio

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21

Garcı́a-Ferrer, Antonio, and Marcos Bujosa-Brun. "Forecasting OECD industrial turning points using unobserved components models with business survey data." International Journal of Forecasting 16, no. 2 (2000): 207–27. http://dx.doi.org/10.1016/s0169-2070(99)00049-7.

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22

Nonejad, Nima. "Flexible model comparison of unobserved components models using particle Gibbs with ancestor sampling." Economics Letters 133 (August 2015): 35–39. http://dx.doi.org/10.1016/j.econlet.2015.04.034.

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23

Hu, Yingyao, and Yuya Sasaki. "CLOSED-FORM IDENTIFICATION OF DYNAMIC DISCRETE CHOICE MODELS WITH PROXIES FOR UNOBSERVED STATE VARIABLES." Econometric Theory 34, no. 1 (2017): 166–85. http://dx.doi.org/10.1017/s0266466617000081.

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Proxies for unobserved skills and technologies are increasingly available in empirical data. For dynamic discrete choice models of forward-looking agents where a continuous state variable is unobserved but its proxy is available, we derive closed-form identification of the structure by explicitly solving integral equations. In the first step, we derive closed-form identification of Markov components, including the conditional choice probabilities and the law of state transition. In the second step, we plug in these first-step identifying formulas to obtain primitive structural parameters of dy

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24

Saint-Cyr, Legrand D. F., and Laurent Piet. "mixmcm: A community-contributed command for fitting mixtures of Markov chain models using maximum likelihood and the EM algorithm." Stata Journal: Promoting communications on statistics and Stata 19, no. 2 (2019): 294–334. http://dx.doi.org/10.1177/1536867x19854015.

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Markov chain models and finite mixture models have been widely applied in various strands of the academic literature. Several studies analyzing dynamic processes have combined both modeling approaches to account for unobserved heterogeneity within a population. In this article, we describe mixmcm, a community-contributed command that fits the general class of mixed Markov chain models, accounting for the possibility of both entries into and exits from the population. To account for the possibility of incomplete information within the data (that is, unobserved heterogeneity), the model is fit w

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25

Kellermann, Magnus A. "Total Factor Productivity Decomposition and Unobserved Heterogeneity in Stochastic Frontier Models." Agricultural and Resource Economics Review 44, no. 1 (2015): 124–48. http://dx.doi.org/10.1017/s1068280500004664.

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This study examines in an empirical comparison how different econometric specifications of stochastic frontier models affect the decomposition of total factor productivity growth. We estimate nine stochastic frontier models, which have been widely used in empirical investigations of sources of productivity growth. Our results show that the relative contribution of components to total factor productivity growth is quite sensitive to the choice of econometric model, which points to the need to select the “right” model. We apply various statistical tests to narrow the range of applicable models a

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26

Hannonen, Marko. "AN ANALYSIS OF LAND PRICES: A STRUCTURAL TIME‐SERIES APPROACH." International Journal of Strategic Property Management 9, no. 3 (2005): 145–72. http://dx.doi.org/10.3846/1648715x.2005.9637534.

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This paper analyses spatio‐temporal variation of land prices in two single localities by means of structural time series modelling formalism that combines the flexibility of a time series model with that of the interpretation of a regression analysis. The extension of conventional hedonic models by introducing unobserved components for trend and cycle resulted to significant improvements in their post‐sample predictive accuracy. In predictive testing, for most model formulations the unobserved component approach generated only a marginal average prediction error when compared to the orthodox h

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27

Harvey, Andrew C., and Davide Delle Monache. "Computing the mean square error of unobserved components extracted by misspecified time series models." Journal of Economic Dynamics and Control 33, no. 2 (2009): 283–95. http://dx.doi.org/10.1016/j.jedc.2008.05.007.

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28

Wolff, Christian C. P. "Measuring the forward foreign exchange risk premium: multi-country evidence from unobserved components models." Journal of International Financial Markets, Institutions and Money 10, no. 1 (2000): 1–8. http://dx.doi.org/10.1016/s1042-4431(99)00021-9.

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29

Fu, Bowen. "Is the slope of the Phillips curve time-varying? Evidence from unobserved components models." Economic Modelling 88 (June 2020): 320–40. http://dx.doi.org/10.1016/j.econmod.2019.09.045.

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30

McElroy, Tucker S., and Agustin Maravall. "Optimal Signal Extraction with Correlated Components." Journal of Time Series Econometrics 6, no. 2 (2014): 237–73. http://dx.doi.org/10.1515/jtse-2013-0016.

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AbstractWhile it is typical in the econometric signal extraction literature to assume that the unobserved signal and noise components are uncorrelated, there is nevertheless an interest among econometricians in the hypothesis of hysteresis, i.e. that major movements in the economy are fundamentally linked. While specific models involving correlated signal and noise innovation sequences have been developed and applied using state space methods, there is no systematic treatment of optimal signal extraction with correlated components. This paper provides the mean square error optimal formulas for

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31

Trabelsi Mnif, Afef. "Political uncertainty and behavior of Tunisian stock market cycles: Structural unobserved components time series models." Research in International Business and Finance 39 (January 2017): 206–14. http://dx.doi.org/10.1016/j.ribaf.2016.07.029.

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32

Zivot, Eric. "A Bayesian Analysis Of The Unit Root Hypothesis Within An Unobserved Components Model." Econometric Theory 10, no. 3-4 (1994): 552–78. http://dx.doi.org/10.1017/s0266466600008665.

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In this paper we extend some of Phillips's [4] results to nonlinear unobserved components models and develop a posterior odds ratio test of the unit root hypothesis based on flat and Jeffreys priors. In contrast to the analysis presented by Schotman and van Dijk [9], we utilize a nondegenerate structural representation of the components model that allows us to determine well-behaved Jeffreys priors, posterior densities under flat priors and Jeffreys priors, and posterior odds ratios for the unit root hypothesis without a proper prior for the level parameter. The analysis highlights the importa

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33

Herger, Nils. "Panel Data Models and the Uncovered Interest Parity Condition: The Role of Two-Way Unobserved Components." International Journal of Finance & Economics 21, no. 3 (2016): 294–310. http://dx.doi.org/10.1002/ijfe.1552.

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34

Allegret, Jean-Pierre, and Alain Sand-Zantman. "Disentangling Business Cycles and Macroeconomic policy in Mercosur: a VAR and an Unobserved Components Models Approaches." Journal of Economic Integration 22, no. 3 (2007): 482–514. http://dx.doi.org/10.11130/jei.2007.22.3.482.

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35

Wooldridge, Jeffrey M. "Multiplicative Panel Data Models Without the Strict Exogeneity Assumption." Econometric Theory 13, no. 5 (1997): 667–78. http://dx.doi.org/10.1017/s0266466600006125.

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This paper considers estimation of multiplicative, unobserved components panel data models without imposing a strict exogeneity assumption on the conditioning variables. The method of moments estimators proposed have significant robustness properties. They require only a conditional mean assumption and apply to models with lagged dependent variables and to finite distributed lag models with arbitrary feedback from the explained to future values of the explanatory variables. The model is particularly suited to nonnegative explained variables, including count variables, continuously distributed

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36

Liu, Yuntong, Yu Wei, Yi Liu, and Wenjuan Li. "Forecasting Oil Price by Hierarchical Shrinkage in Dynamic Parameter Models." Discrete Dynamics in Nature and Society 2020 (December 2, 2020): 1–12. http://dx.doi.org/10.1155/2020/6640180.

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The aim of this paper is to forecast monthly crude oil price with a hierarchical shrinkage approach, which utilizes not only LASSO for predictor selection, but a hierarchical Bayesian method to determine whether constant coefficient (CC) or time-varying parameter (TVP) predictive regression should be employed in each out-of-sample forecasting step. This newly developed method has the advantages of both model shrinkage and automatic switch between CC and TVP forecasting models; thus, this may produce more accurate predictions of crude oil prices. The empirical results show that this hierarchica

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37

Dungey, Mardi, Jan P. A. M. Jacobs, Jing Tian, and Simon van Norden. "TREND IN CYCLE OR CYCLE IN TREND? NEW STRUCTURAL IDENTIFICATIONS FOR UNOBSERVED-COMPONENTS MODELS OF U.S. REAL GDP." Macroeconomic Dynamics 19, no. 4 (2014): 776–90. http://dx.doi.org/10.1017/s1365100513000606.

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A well-documented property of the Beveridge–Nelson trend–cycle decomposition is the perfect negative correlation between trend and cycle innovations. We show how this may be consistent with a structural model where permanent innovations enter the cycle or transitory innovations enter the trend, and that identification restrictions are necessary to make this structural distinction. A reduced-form unrestricted version is compatible with either option, but cannot distinguish which is relevant. We discuss economic interpretations and implications using U.S. real GDP data.

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38

Bahcall, John N. "Dark Matter in the Galactic Disk." Symposium - International Astronomical Union 117 (1987): 17–31. http://dx.doi.org/10.1017/s0074180900149794.

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The Poisson and Vlasov equations are solved self-consistently for realistic Galaxy models which include multiple disk components, a Population II spheroid, and an unseen massive halo. The total amount of matter in the vicinity of the Sun is determined by comparing the observed distributions of tracer stars, samples of F dwarfs and of K giants, with the predictions of the Galaxy models. Results are obtained for a number of different assumed distributions of the unseen disk mass. The major uncertainties, observational and theoretical, are estimated. For all the observed samples, typical models i

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39

Bratčikovienė, Nomeda. "Adapted SETAR model for lithuanian HCPI time series." Nonlinear Analysis: Modelling and Control 17, no. 1 (2012): 27–46. http://dx.doi.org/10.15388/na.17.1.14076.

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We present adapted SETAR (self-exciting threshold autoregressive) model, which enables simultaneous estimation of nonlinearity and unobserved time series components. This model was tested on real Lithuanian harmonised consumer price index (HCPI) time series, covering the period from January 1996 to December 2009. The results show that adapted SETAR model is able to capture features of the real time series with complex nature. ARIMA model has also been used for the same time series for the comparison. Evaluated models and results of the comparison are presented in this work.

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40

Hossain, Kazi Abrar, Syed Abul Basher, and A. K. Enamul Haque. "Quantifying the impact of Ramadan on global raw sugar prices." International Journal of Islamic and Middle Eastern Finance and Management 11, no. 4 (2018): 510–28. http://dx.doi.org/10.1108/imefm-05-2017-0132.

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Purpose The purpose of this study is to quantify the impact of Ramadan on both the level and the growth of global raw sugar price. Design/methodology/approach The study uses a dummy and a fractional variable to capture Ramadan to overcome the asynchronicity of time between Ramadan fasting (which is based on the Islamic lunar calendar) and the movement in prices (which follows the Gregorian solar calendar). To capture the seasonality of sugar production, the data on sugar price span 34 years so that the Islamic calendar makes a complete cycle of the Gregorian calendar. The empirical model is es

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41

Krane, Spencer D. "Professional Forecasters' Views of Permanent and Transitory Shocks to GDP." American Economic Journal: Macroeconomics 3, no. 1 (2011): 184–211. http://dx.doi.org/10.1257/mac.3.1.184.

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This paper examines how the professional forecasters comprising the Blue Chip Economic Consensus view shocks to GDP. I use an unobserved components model of the forecast revisions to identify forecasters' perceptions of permanent and transitory shocks to GDP. The model indicates forecasters: attribute about two-thirds of the variance in current-period revisions to permanent shocks; view the relative importance of permanent shocks similar to the estimates of some simple univariate econometric models; see high-frequency indicators of economic activity as being informative about both permanent an

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42

Makeienko, Maryna. "Symbolic Analysis Applied to the Specification of Spatial Trends and Spatial Dependence." Entropy 22, no. 4 (2020): 466. http://dx.doi.org/10.3390/e22040466.

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This article provides symbolic analysis tools for specifying spatial econometric models. It firstly considers testing spatial dependence in the presence of potential leading deterministic spatial components (similar to time-series tests for unit roots in the presence of temporal drift and/or time-trend) and secondly considers how to econometrically model spatial economic relations that might contain unobserved spatial structure of unknown form. Hypothesis testing is conducted with a symbolic-entropy based non-parametric statistical procedure, recently proposed by Garcia-Cordoba, Matilla-Garcia

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43

Giordani, Paolo, Roberto Rocci, and Giuseppe Bove. "Factor Uniqueness of the Structural Parafac Model." Psychometrika 85, no. 3 (2020): 555–74. http://dx.doi.org/10.1007/s11336-020-09715-4.

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Abstract Factor analysis is a well-known method for describing the covariance structure among a set of manifest variables through a limited number of unobserved factors. When the observed variables are collected at various occasions on the same statistical units, the data have a three-way structure and standard factor analysis may fail. To overcome these limitations, three-way models, such as the Parafac model, can be adopted. It is often seen as an extension of principal component analysis able to discover unique latent components. The structural version, i.e., as a reparameterization of the

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44

KLEMM, RICHARD A., GERALD ARNOLD, ANDREAS BILLE, CARSTEN T. RIECK та KURT SCHARNBERG. "c-AXIS TWIST Bi2Sr2CaCu2O8+δ JOSEPHSON JUNCTIONS: A NEW PHASE-SENSITIVE TEST OF ORDER PARAMETER SYMMETRY". International Journal of Modern Physics B 13, № 29n31 (1999): 3449–54. http://dx.doi.org/10.1142/s0217979299003192.

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Li et al. found that the critical current density [Formula: see text] across atomically clean c-axis twist junctions of Bi 2 Sr 2 CaCu 2 O 8+δ is the same as that of the constituent single crystal, [Formula: see text], independent of the twist angle ϕ0, even at and below Tc. We investigated theoretically if a dx2-y2-wave order parameter might twist by mixing in dxy-wave components, but found that such mixing cannot possibly explain the data near to Tc. Combined with group theoretical arguments, we then conclude that the order parameter contains at least a substantial s-wave component, but does

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45

Donayre, Luiggi. "ESTIMATED THRESHOLDS IN THE RESPONSE OF OUTPUT TO MONETARY POLICY: ARE LARGE POLICY CHANGES LESS EFFECTIVE?" Macroeconomic Dynamics 18, no. 1 (2013): 41–64. http://dx.doi.org/10.1017/s1365100513000278.

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This paper investigates the potential sources of the mixed evidence found in the empirical literature studying asymmetries in the response of output to monetary policy shocks of different magnitudes. Further, it argues that such mixed evidence is a consequence of the exogenous imposition of the threshold that classifies monetary shocks as small or large. To address this issue, I propose an unobserved-components model of output, augmented by a monetary policy variable, which allows the threshold to be endogenously estimated. The results show strong statistical evidence that the effect of moneta

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46

Njuki, Eric, Boris E. Bravo-Ureta, and Víctor E. Cabrera. "Climatic effects and total factor productivity: econometric evidence for Wisconsin dairy farms." European Review of Agricultural Economics 47, no. 3 (2020): 1276–301. http://dx.doi.org/10.1093/erae/jbz046.

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Abstract This study exploits temporal and cross-sectional variation in weather and long-run climate trends to investigate their effects on farm-level productivity. Using panel data for a sample of Wisconsin dairy producers, three stochastic production frontier models are estimated and a random parameters approach is chosen as the most desirable, which accounts for stochastic observed and unobserved environmental factors. The estimated coefficients are used to decompose a multiplicative total factor productivity index that accounts for different sources of productivity growth. Annual productivi

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47

Phoong, Seuk Yen, and Seuk Wai Phoong. "Finite Mixture Model: A Comparison of Maximum Likelihood Estimation and Bayesian Analysis." 12th GLOBAL CONFERENCE ON BUSINESS AND SOCIAL SCIENCES 12, no. 1 (2021): 63. http://dx.doi.org/10.35609/gcbssproceeding.2021.12(63).

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The mixture model is known as model-based clustering that is used to model a mixture of unknown distributions. The clustering of mixture model is based on four important criteria, including the number of components in the mixture model, clustering kernel (such as Gaussian mixture models, Dirichlet, etc.), estimation methods, and dimensionality (Lai et al., 2019). Finite mixture model is a finite dimensional of a hierarchical model. It is useful in modeling the data with outliers, non-normal distributed or heavy tails. Furthermore, finite mixture model is flexible when fitted with the models th

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48

Yang, Shihao, Samuel W. K. Wong, and S. C. Kou. "Inference of dynamic systems from noisy and sparse data via manifold-constrained Gaussian processes." Proceedings of the National Academy of Sciences 118, no. 15 (2021): e2020397118. http://dx.doi.org/10.1073/pnas.2020397118.

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Parameter estimation for nonlinear dynamic system models, represented by ordinary differential equations (ODEs), using noisy and sparse data, is a vital task in many fields. We propose a fast and accurate method, manifold-constrained Gaussian process inference (MAGI), for this task. MAGI uses a Gaussian process model over time series data, explicitly conditioned on the manifold constraint that derivatives of the Gaussian process must satisfy the ODE system. By doing so, we completely bypass the need for numerical integration and achieve substantial savings in computational time. MAGI is also s

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49

Jing, Renzhi, and Ning Lin. "Tropical Cyclone Intensity Evolution Modeled as a Dependent Hidden Markov Process." Journal of Climate 32, no. 22 (2019): 7837–55. http://dx.doi.org/10.1175/jcli-d-19-0027.1.

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Abstract A hidden Markov model is developed to simulate tropical cyclone intensity evolution dependent on the surrounding large-scale environment. The model considers three unobserved (hidden) discrete states of storm intensity change and associates each state with a probability distribution of intensity change. The storm’s transit from one state to another is described as a Markov chain. Both the intensity change and state transit components of the model are dependent on environmental variables including potential intensity, vertical wind shear, relative humidity, and ocean feedback. This Mar

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Bai, Jushan, and Serena Ng. "INSTRUMENTAL VARIABLE ESTIMATION IN A DATA RICH ENVIRONMENT." Econometric Theory 26, no. 6 (2010): 1577–606. http://dx.doi.org/10.1017/s0266466609990727.

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We consider estimation of parameters in a regression model with endogenous regressors. The endogenous regressors along with a large number of other endogenous variables are driven by a small number of unobservable exogenous common factors. We show that the estimated common factors can be used as instrumental variables and they are more efficient than the observed variables in our framework. Whereas standard optimal generalized method of moments estimator using a large number of instruments is biased and can be inconsistent, the factor instrumental variable estimator (FIV) is shown to be consis

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