Multiple Fixed Effects in Nonlinear Panel Data Models - Theory and Evidence
... However, econometric theory has mostly focused on single fixed effects. The present paper attempts to bridge part of this gap by looking at some specific nonlinear models. The empirical relevance is demonstrated using Monte Carlo simulations and an application to international trade data. This paper ...
... However, econometric theory has mostly focused on single fixed effects. The present paper attempts to bridge part of this gap by looking at some specific nonlinear models. The empirical relevance is demonstrated using Monte Carlo simulations and an application to international trade data. This paper ...
Vector Autoregressions with Parsimoniously Time
... In the models discussed above the parameters do vary at every point in time; another strand of literature investigates models with a finite number of changes in the parameters, or a finite number of possible values the parameters may take over time. One example of such models is regime switching mod ...
... In the models discussed above the parameters do vary at every point in time; another strand of literature investigates models with a finite number of changes in the parameters, or a finite number of possible values the parameters may take over time. One example of such models is regime switching mod ...
Vector Autoregressions with Parsimoniously Time Varying
... the path of the parameter vector in a non parametric way. In this paper we assume the probability αT for an increment to be different from zero to depend on the sample length T , specifically αT = k α T −a , where k α and a are positive constants. In the case of a single variable this leads to an e ...
... the path of the parameter vector in a non parametric way. In this paper we assume the probability αT for an increment to be different from zero to depend on the sample length T , specifically αT = k α T −a , where k α and a are positive constants. In the case of a single variable this leads to an e ...
Springer Series in Statistics
... where W is a standard Wiener process on [0, 1], the function f is an unknown function on [0, 1], and n is an integer. We assume that a sample path X = {Y (t), 0 ≤ t ≤ 1} of the process Y is observed. The statistical problem is to estimate the unknown function f . In the nonparametric case it is only ...
... where W is a standard Wiener process on [0, 1], the function f is an unknown function on [0, 1], and n is an integer. We assume that a sample path X = {Y (t), 0 ≤ t ≤ 1} of the process Y is observed. The statistical problem is to estimate the unknown function f . In the nonparametric case it is only ...
Journal of Applied Statistics Estimating utility functions using
... analysis with particular emphasis on the estimation of utility functions [1,6,7]. In addition, we explain how our work differs from the papers that use ME to estimate utility functions. As described in the previous subsection, the ME principle applies to estimation of probability distributions. Howe ...
... analysis with particular emphasis on the estimation of utility functions [1,6,7]. In addition, we explain how our work differs from the papers that use ME to estimate utility functions. As described in the previous subsection, the ME principle applies to estimation of probability distributions. Howe ...
1.14 Polynomial regression
... thus the mean is a d’th order polynomial in the covariate y. Let y1 , . . . , yn be given, real numbers – the covariates – and Xi = β 0 + β 1 y + β 2 y 2 + . . . + β d y d + ε i where the εi ’s are iid with the N (0, σ 2 )-distribution. Then we can estimate the d + 1 parameters β0 , . . . , βd by le ...
... thus the mean is a d’th order polynomial in the covariate y. Let y1 , . . . , yn be given, real numbers – the covariates – and Xi = β 0 + β 1 y + β 2 y 2 + . . . + β d y d + ε i where the εi ’s are iid with the N (0, σ 2 )-distribution. Then we can estimate the d + 1 parameters β0 , . . . , βd by le ...
Model selection for estimating the non zero components of a
... The following regression model is considered: X = m + τ ε, ε ∼ Nn (0, In ), where X = (X1 , . . . Xn )T is the vector of observations. The expectation of X, say m = (m1 , . . . , mn )T , and the variance τ 2 are unknown. Assuming that some of the components of m are equal to zero, our objective is t ...
... The following regression model is considered: X = m + τ ε, ε ∼ Nn (0, In ), where X = (X1 , . . . Xn )T is the vector of observations. The expectation of X, say m = (m1 , . . . , mn )T , and the variance τ 2 are unknown. Assuming that some of the components of m are equal to zero, our objective is t ...
full version
... ABSTRACT. Standard approaches to constructing nonparametric confidence bands for functions are frustrated by the impact of bias, which generally is not estimated consistently when using the bootstrap and conventionally smoothed function estimators. To overcome this problem it is common practice to e ...
... ABSTRACT. Standard approaches to constructing nonparametric confidence bands for functions are frustrated by the impact of bias, which generally is not estimated consistently when using the bootstrap and conventionally smoothed function estimators. To overcome this problem it is common practice to e ...
Propensity score adjusted method for missing data
... where π̂i = π(φ̂; xi , yi ). The estimator that is found by solving (1.3) for θ is called the propensity-score-adjusted (PSA) estimator. Before considering the PS model under nonignorable nonresponse, we will first examine the PS adjustment under ignorable nonresponse. Much research has been conduct ...
... where π̂i = π(φ̂; xi , yi ). The estimator that is found by solving (1.3) for θ is called the propensity-score-adjusted (PSA) estimator. Before considering the PS model under nonignorable nonresponse, we will first examine the PS adjustment under ignorable nonresponse. Much research has been conduct ...
statistical theory - Statistical Laboratory
... These lecture notes try to give a mathematical introduction to some key aspects of statistical theory. An attempt is made to be mathematically as self-contained as possible without loosing focus over excessive technicalities. An emphasis is given to develop an understanding of the interplay of prob ...
... These lecture notes try to give a mathematical introduction to some key aspects of statistical theory. An attempt is made to be mathematically as self-contained as possible without loosing focus over excessive technicalities. An emphasis is given to develop an understanding of the interplay of prob ...
Risk of Bayesian Inference in Misspecified Models
... sampling variance only, the approach of this paper is also related to the literature that constructs robust, “limited information” likelihoods from a statistic, such as a GMM estimator. The Gaussianity of the posterior can then be motivated by the approximately Gaussian sampling distribution of the ...
... sampling variance only, the approach of this paper is also related to the literature that constructs robust, “limited information” likelihoods from a statistic, such as a GMM estimator. The Gaussianity of the posterior can then be motivated by the approximately Gaussian sampling distribution of the ...
Model Selection and Adaptation of Hyperparameters
... practical applications, it may not be easy to specify all aspects of the covariance function with confidence. While some properties such as stationarity of the covariance function may be easy to determine from the context, we typically have only rather vague information about other properties, such ...
... practical applications, it may not be easy to specify all aspects of the covariance function with confidence. While some properties such as stationarity of the covariance function may be easy to determine from the context, we typically have only rather vague information about other properties, such ...
Extending Powell's Semiparametric Censored Estimator to Include Non-Linear Functional Forms and Extending Buchinsky's Estimation Technique
... as the degree of censoring is reduced and as the sample size is increased. A recent estimator that is similar to Powell is by Buchinsky and Hahn (1998) who estimate censored quantile regressions (censored LAD is the 50th quantile) by first estimating nonparametric quantiles and conditional distribut ...
... as the degree of censoring is reduced and as the sample size is increased. A recent estimator that is similar to Powell is by Buchinsky and Hahn (1998) who estimate censored quantile regressions (censored LAD is the 50th quantile) by first estimating nonparametric quantiles and conditional distribut ...
Nonconcave Penalized Likelihood With NP
... The above class of penalty functions has been considered by penalty is a convex function Lv and Fan (2009). Clearly the that falls at the boundary of the class of penalty functions satisfying Condition 1. Fan and Li (2001) advocate penalty functions that give estimators with three desired properties ...
... The above class of penalty functions has been considered by penalty is a convex function Lv and Fan (2009). Clearly the that falls at the boundary of the class of penalty functions satisfying Condition 1. Fan and Li (2001) advocate penalty functions that give estimators with three desired properties ...
Y - staff.city.ac.uk
... If q^ is unbiased, that is, if E(q ^)- q = 0. then we have, MSE q^ ≡ Var(q^) An unbiased estimator q^ of a parameter q is efficient if and only if it has the smallest variance of all unbiased estimators. That is, for any other unbiased estimator p of q, ...
... If q^ is unbiased, that is, if E(q ^)- q = 0. then we have, MSE q^ ≡ Var(q^) An unbiased estimator q^ of a parameter q is efficient if and only if it has the smallest variance of all unbiased estimators. That is, for any other unbiased estimator p of q, ...
Chapter 13 Estimation and Evidence in Forensic Anthropology
... Markov chain Monte Carlo simulation that successively samples from full conditional distributions in order to obtain the posterior distributions for all parameters in a particular model (19). As the model here has a single parameter, there is no conditioning on other parameters, so WinBUGS will samp ...
... Markov chain Monte Carlo simulation that successively samples from full conditional distributions in order to obtain the posterior distributions for all parameters in a particular model (19). As the model here has a single parameter, there is no conditioning on other parameters, so WinBUGS will samp ...
Binary Dependent Variables
... – Here, the subject-specific effects account only for the intercepts and do not include other variables. – We assume that {i} are fixed effects in this section. • In this chapter, we assume that responses are serially uncorrelated. • Important point: Panel data with dummy variables provide inconsis ...
... – Here, the subject-specific effects account only for the intercepts and do not include other variables. – We assume that {i} are fixed effects in this section. • In this chapter, we assume that responses are serially uncorrelated. • Important point: Panel data with dummy variables provide inconsis ...
Binary Dependent Variables
... – Here, the subject-specific effects account only for the intercepts and do not include other variables. – We assume that {i} are fixed effects in this section. • In this chapter, we assume that responses are serially uncorrelated. • Important point: Panel data with dummy variables provide inconsis ...
... – Here, the subject-specific effects account only for the intercepts and do not include other variables. – We assume that {i} are fixed effects in this section. • In this chapter, we assume that responses are serially uncorrelated. • Important point: Panel data with dummy variables provide inconsis ...
A Simple Estimator for Binary Choice Models With
... addition, the latent error term " may be heteroskedastic (e.g., some regressors could have random coefficients) and has an unknown distribution. Let Z be a vector of instrumental variables that are uncorrelated with ". There are three common methods for estimating such models: maximum likelihood, co ...
... addition, the latent error term " may be heteroskedastic (e.g., some regressors could have random coefficients) and has an unknown distribution. Let Z be a vector of instrumental variables that are uncorrelated with ". There are three common methods for estimating such models: maximum likelihood, co ...
NBER WORKING PAPER SERIES COEFFICIENTS Patrick Bajari
... to estimating random coefficient models, where the objective functions can have multiple local optima and the econometrician is not guaranteed to find the global solution. We also note that our approach does not require a parametric specification for the distribution of the random coefficients. We ...
... to estimating random coefficient models, where the objective functions can have multiple local optima and the econometrician is not guaranteed to find the global solution. We also note that our approach does not require a parametric specification for the distribution of the random coefficients. We ...
Assumption 2 - AgEcon Search
... the slope coefficient is asymptotically random, so the true slope (zero) fails to be identified and the OLS estimator is inconsistent, and (ii) the t-statistic of the slope does not have a limiting distribution but diverges at a T rate as the sample size (T) goes to infinity; therefore, the null hyp ...
... the slope coefficient is asymptotically random, so the true slope (zero) fails to be identified and the OLS estimator is inconsistent, and (ii) the t-statistic of the slope does not have a limiting distribution but diverges at a T rate as the sample size (T) goes to infinity; therefore, the null hyp ...
Frees, Edward W.; (1986).Estimation Following a Robbins-Monro Designed Experiment."
... have better finite sample properties than Xn even when On converges to M(a)-l so that Xn is asymptotically efficient. As shown by Wu (1985), the Lai and Robbins adaptive procedure is not as successful in practice as its asymptotic properties would suggest. ...
... have better finite sample properties than Xn even when On converges to M(a)-l so that Xn is asymptotically efficient. As shown by Wu (1985), the Lai and Robbins adaptive procedure is not as successful in practice as its asymptotic properties would suggest. ...
GLM (Generalized Linear Model) #1 (version 9)
... restriction on the selection of the link function, because whatever happens in g−1 (ηi ) to translate ηi into µi has to be exactly undone by the translation from µi into θi . McCullagh and Nelder state that the canonical link should not be used if it contradicts the substantive ideas that motivate a ...
... restriction on the selection of the link function, because whatever happens in g−1 (ηi ) to translate ηi into µi has to be exactly undone by the translation from µi into θi . McCullagh and Nelder state that the canonical link should not be used if it contradicts the substantive ideas that motivate a ...
Stat 511 Outline Spring 2004 Steve Vardeman Iowa State University
... When X is not of full rank, the above expression doesn’t make sense. But even in such cases, for some c ∈ Rk the linear combination of parameters c0 β can be unambiguous in the sense that every β that produces a given EY = Xβ produces the same value of c0 β. As it turns out, the c’s that have this p ...
... When X is not of full rank, the above expression doesn’t make sense. But even in such cases, for some c ∈ Rk the linear combination of parameters c0 β can be unambiguous in the sense that every β that produces a given EY = Xβ produces the same value of c0 β. As it turns out, the c’s that have this p ...