Econometric Theory Research Papers - Academia.edu (original) (raw)

Time series merupakan salah satu jenis data yang sering kita jumpai. Karakteristik data time series melibatkan lebih dari satu titik waktu dengan unit analisis dapat berupa kota, kabupaten, perusahaan, negara dan sebagainya. Sedangkan... more

Time series merupakan salah satu jenis data yang sering kita jumpai. Karakteristik data time series melibatkan lebih dari satu titik waktu dengan unit analisis dapat berupa kota, kabupaten, perusahaan, negara dan sebagainya. Sedangkan periode analisa dapat berupa harian, mingguan, bulanan, triwulanan, atau tahunan. Contoh data time series adalah data pendapatan nasional, inflasi, suku bunga, dan sebagainya.
Handout ini membahas empat topik yaitu data non stasioner, stasioneritas, akar unit, dan kointegrasi.

Unit root tests for time series with level shifts of general form are considered when the timing of the shift is unknown. It is proposed to estimate the nuisance parameters of the data generation process including the shift date in a... more

Unit root tests for time series with level shifts of general form are considered when the timing of the shift is unknown. It is proposed to estimate the nuisance parameters of the data generation process including the shift date in a first step and apply standard unit root tests to the residuals. The estimation of the nuisance parameters is done in such a way that the unit root tests on the residuals have the same limiting distributions as for the case of a known break date. Simulations are performed to investigate the small sample properties of the tests, and empirical examples are discussed to illustrate the procedure.

This paper extends the Integrated Conditional Moment (ICM) test for the functional form of nonlinear regression models to tests for para- metric conditional distributions. This test is formed on the basis of the integrated squared... more

This paper extends the Integrated Conditional Moment (ICM) test for the functional form of nonlinear regression models to tests for para- metric conditional distributions. This test is formed on the basis of the integrated squared difference between the empirical characteristic function of the actual data and the characteristic function implied by the model. This test is consistent, and has nontrivial

Bu çalışmada çok kategorili, nitel değişkenlerin sırasız bir şekilde kullanıldığı ve bağımlı değişkenin ikiden fazla değer aldığı Multinomial Logit Modeli üzerinde durulmuştur. Kavramsal açıdan çok değişkenli modellerin arasındaki farklar... more

Bu çalışmada çok kategorili, nitel değişkenlerin sırasız bir şekilde kullanıldığı ve bağımlı değişkenin ikiden fazla değer aldığı Multinomial Logit Modeli üzerinde durulmuştur. Kavramsal açıdan çok değişkenli modellerin arasındaki farklar ortaya konulduktan sonra, Multinomial Logit Modelinin çeşitleri ve varsayımları ile modelin tahmini ve yorumlanması teorik çerçevede ve uygulamalı örneklerle açıklanmıştır.

This paper shows that if the errors in a multiple regression model are heavy-tailed, the ordinary least squares (OLS) estimators for the regression coefficients are tail-dependent. The tail dependence arises, because the OLS estimators... more

This paper shows that if the errors in a multiple regression model are heavy-tailed, the ordinary least squares (OLS) estimators for the regression coefficients are tail-dependent. The tail dependence arises, because the OLS estimators are stochastic linear combinations of heavy-tailed random variables. Moreover, tail dependence also exists between the fitted sum of squares (FSS) and the residual sum of squares (RSS), because they are stochastic quadratic combinations of heavy-tailed random variables.

The authors thank the two referees and the co-editor for their valuable suggestions. Thanks to Professor Qiwei Yao for helpful discussion on programming of bootstrap method. Oliver Linton was supported by the ESRC (UK), and Jiazhu Pan was... more

The authors thank the two referees and the co-editor for their valuable suggestions. Thanks to Professor Qiwei Yao for helpful discussion on programming of bootstrap method. Oliver Linton was supported by the ESRC (UK), and Jiazhu Pan was partially supported by the starter ...

This Working Paper is brought to you for free and open access by the School of Economics at Institutional Knowledge at Singapore Management University. It has been accepted for inclusion in Research Collection School of Economics by an... more

This Working Paper is brought to you for free and open access by the School of Economics at Institutional Knowledge at Singapore Management University. It has been accepted for inclusion in Research Collection School of Economics by an authorized administrator of ...

In this paper nearly unstable AR(p) processes (in other words, models with characteristic roots near the unit circle) are studied. Our main aim is to describe the asymptotic behaviour of the least squares estimators of the coecients. A... more

In this paper nearly unstable AR(p) processes (in other words, models with characteristic roots near the unit circle) are studied. Our main aim is to describe the asymptotic behaviour of the least squares estimators of the coecients. A convergence result is presented for the general complex-valued case. The limit distribution is given by the help of some continuous time AR processes. We apply the results for real-valued nearly unstable AR(p) models. In this case the limit distribution can be identied with the maximum likelihood estimator of the coecients of the corresponding continuous time AR processes.

In this paper we introduce a linear programming estimator (LPE) for the slope parameter in a constrained linear regression model with a single regressor. The LPE is interesting because it can be superconsistent in the presence of an... more

In this paper we introduce a linear programming estimator (LPE) for the slope parameter in a constrained linear regression model with a single regressor. The LPE is interesting because it can be superconsistent in the presence of an endogenous regressor and, hence, preferable to the ordinary least squares estimator (LSE). Two different cases are considered as we investigate the statistical properties of the LPE. In the first case, the regressor is assumed to be fixed in repeated samples. In the second, the regressor is stochastic and potentially endogenous. For both cases the strong consistency and exact finite-sample distribution of the LPE is established. Conditions under which the LPE is consistent in the presence of serially correlated, heteroskedastic errors are also given. Finally, we describe how the LPE can be extended to the case with multiple regressors and conjecture that the extended estimator is consistent under conditions analogous to the ones given herein. Finite-sample properties of the LPE and extended LPE in comparison to the LSE and instrumental variable estimator (IVE) are investigated in a simulation study. One advantage of the LPE is that it does not require an instrument.

We consider a model Yt=sigmatetatY\_t=\sigma\_t\eta\_tYt=sigmatetat in which (sigmat)(\sigma\_t)(sigmat) is not independent of the noise process (etat)(\eta\_t)(etat), but sigmat\sigma\_tsigmat is independent of etat\eta\_tetat for each ttt. We assume that (sigmat)(\sigma\_t)(sigmat) is stationary and we propose an... more

We consider a model Yt=sigmatetatY\_t=\sigma\_t\eta\_tYt=sigmatetat in which (sigmat)(\sigma\_t)(sigmat) is not independent of the noise process (etat)(\eta\_t)(etat), but sigmat\sigma\_tsigmat is independent of etat\eta\_tetat for each ttt. We assume that (sigmat)(\sigma\_t)(sigmat) is stationary and we propose an adaptive estimator of the density of ln(sigma2t)\ln(\sigma^2\_t)ln(sigma2t) based on the observations YtY\_tYt. Under various dependence structures, the rates of this nonparametric estimator coincide with the

The hat matrix maps the vector of response values in a regression to its predicted counterpart. The trace of this hat matrix is the workhorse for calculating the effective number of parameters in both parametric and nonparametric... more

The hat matrix maps the vector of response values in a regression to its predicted counterpart. The trace of this hat matrix is the workhorse for calculating the effective number of parameters in both parametric and nonparametric regression settings. Drawing on the regression literature, the standard kernel density estimate is transformed to mimic a regression estimate thus allowing extraction of a usable hat matrix for calculating the effective number of parameters of the kernel density estimate. Asymptotic expressions for the trace of this hat matrix are derived under standard regularity conditions for mixed, continuous, and discrete densities. Simulations validate the theoretical contributions. Several empirical examples demonstrate the usefulness of the method suggesting that calculating the effective number of parameters of a kernel density estimator may be useful in interpreting differences across estimators.

The matrix that transforms the response variable in a regression to its predicted value is commonly referred to as the hat matrix. The trace of the hat matrix is a standard metric for calculating degrees of freedom. The two prominent... more

The matrix that transforms the response variable in a regression to its predicted value is commonly referred to as the hat matrix. The trace of the hat matrix is a standard metric for calculating degrees of freedom. The two prominent theoretical frameworks for studying hat matrices to calculate degrees of freedom in local polynomial regressions-ANOVA and non-ANOVA-abstract from both mixed data and the potential presence of irrelevant covariates, both of which dominate empirical applications. In the multivariate local polynomial setup with a mix of continuous and discrete covariates, which include some irrelevant covariates, we formulate asymptotic expressions for the trace of both the non-ANOVA and ANOVA-based hat matrices from the estimator of the unknown conditional mean. The asymptotic expression of the trace of the non-ANOVA hat matrix associated with the conditional mean estimator is equal up to a linear combination of kernel-dependent constants to that of the ANOVA-based hat matrix. Additionally, we document that the trace of the ANOVA-based hat matrix converges to 0 in any setting where the bandwidths diverge. This attrition outcome can occur in the presence of irrelevant continuous covariates or it can arise when the underlying data generating process is in fact of polynomial order.

I provide a systematic treatment of the asymptotic properties of weighted M-estimators under variable probability stratified sampling. The characterization of the sampling scheme and representation of the objective function allow for a... more

I provide a systematic treatment of the asymptotic properties of weighted M-estimators under variable probability stratified sampling. The characterization of the sampling scheme and representation of the objective function allow for a straightforward analysis. Simple, consistent asymptotic variance matrix estimators are proposed for a large class of problems. When stratification is based on exogenous variables, I show that the unweighted M-estimator is more efficient than the weighted estimator under a generalized conditional information matrix equality. When population frequencies are known, a more efficient weighting is possible. I also show how the results carry over to multinomial sampling.

This paper shows how large-dimensional dynamic factor models are suitable for structural analysis. We argue that all identification schemes employed in structural vector autoregression (SVAR) analysis can be easily adapted in dynamic... more

This paper shows how large-dimensional dynamic factor models are suitable for structural analysis. We argue that all identification schemes employed in structural vector autoregression (SVAR) analysis can be easily adapted in dynamic factor models. Moreover, the “problem of fundamentalness,” which is intractable in SVARs, can be solved, provided that the impulse-response functions are sufficiently heterogeneous. We provide consistent estimators for the impulse-response functions and for (n, T) rates of convergence. An exercise with U.S. macroeconomic data shows that our solution of the fundamentalness problem may have important empirical consequences.