Statistica Sinica Preprint No: SS
|
|
- Griffin Eaton
- 6 years ago
- Views:
Transcription
1 Statistica Sinica Preprint No: SS Title A Bootstrap Method for Constructing Pointwise and Uniform Confidence Bands for Conditional Quantile Functions Manuscript ID SS URL DOI /ss Complete List of Authors Joel L Horowitz and Anand Krishnamurthy Corresponding Author Joel L Horowitz joel-horowitz@northwesternedu Notice: Accepted version subject to English editing
2 A BOOTSTRAP METHOD FOR CONSTRUCTING POINTWISE AND UNIFORM CONFIDENCE BANDS FOR CONDITIONAL QUANTILE FUNTIONS by Joel L Horowitz Department of Economics Northwestern University Evanston, IL 6001 joel-horowitz@northwesternedu Anand Krishnamurthy Cornerstone Research 599 Lexington Ave New York, NY 100 akrishnamurthy@cornerstonecom January 018 ABSTRACT This paper is concerned with inference about the conditional quantile function in a nonparametric quantile regression model Any method for constructing a confidence interval or band for this function must deal with the asymptotic bias of nonparametric estimators of the function In estimation methods such as local polynomial estimation, this is usually done through undersmoothing or explicit bias correction The latter usually requires oversmoothing However, there are no satisfactory empirical methods for selecting bandwidths that under- or oversmooth This paper extends the bootstrap method of Hall and Horowitz (013) for conditional mean functions to conditional quantile functions The paper also shows how the bootstrap method can be used to obtain uniform confidence bands The bootstrap method uses only bandwidths that are selected by standard methods such as cross validation and plug-in It does not use under- or oversmoothing The results of Monte Carlo experiments illustrate the numerical performance of the bootstrap method
3 A BOOTSTRAP METHOD FOR CONSTRUCTING POINTWISE AND UNIFORM CONFIDENCE BANDS FOR CONDITIONAL QUANTILE FUNTIONS 1 INTRODUCTION This paper is concerned with inference about the unknown function g in the nonparametric quantile regression model (11) Y = g( X) + ε; P( ε 0) = τ, where X is an observed continuously distributed explanatory variable and ε is an unobserved continuously distributed random variable that is independent of X and whose τ quantile ( 0< τ < 1) is 0 Hall and Horowitz (013) (hereinafter HH) describe a bootstrap method for constructing a pointwise confidence band for the unknown function mx ( ) = EY ( X= x) in a nonparametric mean regression This paper extends the bootstrap method of HH to g in the quantile regression model (11) The paper also shows how the bootstrap method can be used to construct a uniform confidence band for g The method for constructing a uniform confidence band for g can be used to construct a uniform confidence band for m, but this is not done here Any method for constructing a pointwise or uniform confidence band for g based on a nonparametric estimate must deal with the problem of asymptotic bias For example, a local polynomial estimate of g with a bandwidth chosen by cross-validation or plug-in methods is asymptotically biased Denote the estimate by ĝ The expected value of ĝ does not equal g, the asymptotic distribution of the scaled estimate is not centered at g, and the true coverage probability of an asymptotic confidence interval for g that is constructed from the normal distribution in the usual way is less than the nominal probability This problem is usually overcome by undersmoothing or explicit bias reduction Undersmoothing consists of making the bias asymptotically negligible by using a bandwidth whose rate of convergence is faster than the asymptotically optimal rate In explicit bias reduction, an estimate of the asymptotic bias is used to construct an asymptotically unbiased estimate of g Most explicit bias reduction methods involve some form of oversmoothing, that is using a bandwidth whose rate of convergence is slower than the asymptotically optimal rate Undersmoothing and explicit bias correction methods are also available for the conditional mean function m Methods based on undersmoothing or oversmoothing require a bandwidth whose rate of convergence is faster or slower than the asymptotically optimal rate As discussed by HH, there are no attractive, effective empirical ways to choose these bandwidths In addition, undersmoothing can produce very wiggly confidence bands, even for smooth conditional quantile or conditional mean functions 1
4 Explicit bias correction methods that rely on estimation of derivatives can also produce wiggly confidence bands The method presented in this paper, like the method of HH, uses bandwidths chosen by standard empirical methods such as cross validation or a plug-in rule It does not under- or oversmooth and does not use auxiliary or other non-standard bandwidths Instead, the method uses the bootstrap to estimate the bias of ĝ The bootstrap estimate of the bias has stochastic noise that is comparable in size to the bias itself However, combining a suitable quantile of the distribution of the bootstrap bias estimate with ĝ enables us to obtain a pointwise confidence band with an asymptotic coverage probability that equals or exceeds 1 α for any given α > 0 at all but a user specified fraction of the possible values of x The exceptional points are in regions where the function g has sharp peaks or troughs that cause the bias of ĝ to be unusually large These regions are typically visible in a plot of ĝ and can also be found through a theoretical analysis An asymptotic uniform confidence band that has no exceptional points is obtained by replacing the bootstrap bias estimate with an upper bound on the estimated bias This paper differs from HH in two important ways First, we obtain confidence bands for g that are uniform in x, whereas HH obtain only pointwise bands Second, our bootstrap method is different from that of HH To avoid complications caused by the non-smoothness of the quantile objective function, we apply the bootstrap to the leading term of the asymptotic bias of the quantile regression estimator We do not estimate the conditional quantile function from the bootstrap sample In contrast, the objective function of a mean-regression model is smooth This enables HH to estimate the conditional mean function and its bias directly from the bootstrap sample Methods that use undersmoothing have been described by Bjerve, Doksum, and Yandell (1985); Hall (199); Hall and Owen (1993); Neumann (1995); Chen (1996); Neumann and Polzehl (1998); Picard and Tribouley (000); Chen, Härdle, and Li (003); Claeskens and Van Keilegom (003); Härdle, Huet, Mammen, and Sperlich (004); and McMurry and Politis (008) Methods based on oversmoothing have been described by Härdle and Bowman (1988); Härdle and Marron (1991); Hall (199); Eubank and Speckman (1993); Sun and Loader (1994); Härdle, Huet, and Jolivet (1995); Xia (1998); and Schucany and Somers (1977) Calonico, Cattaneo, and Farrell (016) describe an explicit bias correction method for conditional mean functions that does not require oversmoothing or an auxiliary bandwidth It is not known whether this method can be extended to conditional quantile functions There is also a large literature on bootstrap methods for parametric quantile regression models See, for example, De Angelis, Hall, and Young (1993); Hahn (1995); Horowitz (1998); Feng, He, and Hu (011); Aguirre and Dominguez (013); Galvao and Montes-Rojas (015); and Hagemann (017)
5 Section of this paper presents an informal description of our method The method is similar in some respects to that of HH for conditional mean functions, but the non-smoothness of quantile estimators presents problems that are different from those involved in estimating conditional mean functions These require a separate treatment and modifications of parts of the method of HH Section also outlines the extension of our method to a heteroskedastic version of model (11) Section 3 presents formal theoretical results Section 4 presents simulation results that illustrate the numerical performance of the method Conclusions are presented in Section 5 The proofs of theorems are in the online supplementary appendix INFORMAL DESCRIPTION OF THE METHOD Let { Y, X : i = 1,, n} denote an independent random sample of observations from the i i distribution of ( Y, X ) in model (11) Let gx ( ) denote a local polynomial nonparametric estimator of gx ( ) based on bandwidth h Denote the scaled asymptotic bias and variance of gx, ( ) respectively, by 1/ β ( x) = lim Enh ( ) [ gx ( ) gx ( )] and n g σ ( x) = lim ( nh) Var[ g ( x)] Assume that n { gx ( ) Egx [ ( )]}/ σ ( x) N(0,1) as n To minimize the complexity of the discussion in the g d remainder of this paper, we assume that X is a scalar random variable and ĝ is a local linear quantile regression estimator The main results of the paper continue to apply if X is a vector or ĝ is a local polynomial estimator of odd degree different from 1 This paper does not treat series estimators The local linear quantile estimation procedure is described in Step 1 in Section 1 To avoid boundary effects we restrict attention to a compact set that is contained in an open subset of the support of X Let h denote the bandwidth used in local polynomial estimation of g If β ( x) were known, an asymptotic 1 α0 confidence interval for gx ( ) would be 1/ 1/ 1 α/σg 1 α/σg gx ( ) z ( x)/( nh) gx ( ) gx ( ) + z ( x)/( nh) where z1 α / is the 1 α / quantile of the standard normal distribution, α = α( x, α0) satisfies (1) Φ[ z β( x)/ σ ( x)] Φ[ z β( x)/ σ ( x)] = 1 α, 1 α/ g 1 α/ g 0 and Φ is the normal distribution function In applications, β ( x) and σ ( x ) are unknown Let σ ( x ) be the estimate of σ ( x ) that is described in Section 1 Let ( λ x) denote the bootstrap estimate of g β( x)/ σg ( x) that is obtained in Step 5 of the procedure described in Section 1, and let x 0 the solution in α to Φ[ z λ( x)] Φ[ z λ( x)] = 1 α 1 α/ 1 α/ 0 3 g g ( α, α ) denote
6 For ξ [0,1], let αξ ( α 0) be the ξ quantile of points in the set { α( x, α0): x } Define z( ) = Construct the pointwise confidence band α0 z1 α ( α )/ ξ 0 1/ 1/ { x y g x z x nh y g x z x nh } [ α ( α )] = (, ): ( ) ( α ) σ ( )/( ) ( ) + ( α ) σ ( )/( ) n x 0 0 g 0 g It is shown in Section 33 that n has asymptotic coverage probability equal to or greater than 0 except for a proportion ξ of points x An argument like that in Section 6 of HH shows that the exceptional points occur in regions where g ( x) is large These points typically occur near peaks and troughs of gx ( ) and can be identified from a graph of gx ( ) 1 α The critical value z( ) = z is greater than α0 1 α ( α )/ ξ 0 z1 α / 0 Therefore, the confidence band [ α ( α )] is wider than a band that uses the critical value n ξ 0 This enables n[ αξ ( α0)] to z1 α / accommodate the asymptotic bias of gx ( ) The parameter ξ is selected by the user and controls the fraction of points x at which the asymptotic coverage probability of the confidence band n[ αξ ( α0)] is at least 1 α0 This control is an important advantage of our method over undersmoothing and explicit bias correction As is illustrated in Section 4, the latter two methods have poor coverage accuracy but provide no information about the extent of this inaccuracy or ability to control it At the cost of a wider confidence band, the fraction of points at which our method undercovers can be reduced to zero asymptotically by constructing the uniform band described in Step 7 of Section 1 To construct a uniform confidence band for g, define 0 and λ λ max min = max λ( x) x = min λ( x) x Let W 1 be the mean-zero Gaussian process defined in Section 31, and let t satisfy Û x P tu λmin W1 tu λmax x = 1 a0 h, where h is the bandwidth used for local linear quantile estimation of g It is shown in Section 33 that ( ) {( y, x): g( x) t ( x)/( nh) y g( x) + t ( x)/( nh) ; x } 1/ 1/ U α0 U σg U σg is an asymptotic uniform confidence band for g whose coverage probability equals or exceeds 1 α0 4
7 1 The Estimation Procedure This section provides a step-by-step explanation of the method for constructing n and Steps 1-6 are broadly similar to those of HH, but their implementation details differ because of differences between the mean-regression model of HH and the quantile regression model considered here Step 7 constructs a uniform confidence band and is new Step 1: Local linear estimation of g and estimation of U σ ĝ Let K be a kernel function and h be a possibly random bandwidth For any real v, define K() v= Kv ( / h) Define the check function ρ ( v) = v[ τ Iv ( 0)], τ where 0< τ < 1 and I is the indicator function The local linear estimator of gx ( ) is gx ( ) = b 0, where n 0 1 r τ i 0 1 i h i, = 1 ( b, b ) = arg min [ Y b b ( X x)] K ( X x) b0 b1 i Fan, Hu, and Truong (1994) and Yu and Jones (1997) describe properties of this estimator h To obtain σ ( x ), let f ( x ) be a consistent kernel nonparametric estimator of f ( x ), the g X X probability density function of X at x Let ε = Y g( X ) be the residuals from estimating model (11), i i i and let f ε (0) be a consistent kernel nonparametric estimator of f ε (0), the probability density of ε at 0 Specifically, n (0) ( ) 1 fε nhε Kh ( ε ε i ) i= 1 =, where h ε is a bandwidth Define B = K () v dv K It is shown in Section 3 that the scaled variance of the asymptotic distribution of gx ( ) is σ τ(1 τ) B K g ( x) = ( nh) Var[ g( x)] = fx ( x)[ f ε (0)] The scaled variance can be estimated by replacing fx ( x ) and f ε (0) with their consistent estimators to obtain τ(1 τ) B K σ g ( x) = fx ( x)[ f ε (0)] Step : Compute centered residuals Let q n be the τ quantile of the residuals { ε i } That is 5
8 n 1 qn = inf q: n I( εi q) τ i= 1 The centered residuals are ε = ε q i i n The τ quantile of centered residuals is 0 and the Step 3 Construct the bootstrap resample The bootstrap resample is { Y, X : i= 1,, n}, where * * i = ( i) + i Y g X ε * εi s are obtained by sampling the i ε s randomly with replacement The * i i Xi s are not resampled Step 4: Compute the bootstrap estimate of the asymptotic bias of gx ( ) Let there be B bootstrap resamples that are indexed by b= 1,, B For resample b, define { τ 0 1 } n * 1 1 * nb i i= 1 i h i T ( x) = n 1 I Y b + b ( X x) K ( X x) T * ( x ) is a bootstrap analog of the first-order condition in equation (A8) of the supplementary appendix nb Let * * nb B ( ) 1 * Tn x B Tnb ( x ) b= 1 E ( T ) denote the bootstrap expectation of = * T nb conditional on the data Estimate * * nb E ( T ) by T ( x ) converges almost surely to n * * nb E [ T ( x )] and can be made arbitrarily close to * * nb E [ T ( x )] by making B sufficiently large As n, the bias of 1/ ( nh) [ gx ( ) gx ( )] converges to 1/ β ( x) = lim ( nh) Egx [ ( ) gx ( )] Equation (3) gives an analytic expression for β ( x) The n bootstrap estimate of β ( x) is 1/ 1 ( ) τ σ g x β ( x) = h T ( ) (1 ) 1/ n x τ BK [ f ( x)] X In contrast to HH, we do not form a bootstrap estimate of gx ( ) Instead, we form a bootstrap estimate of the asymptotic form of 1/ Enh ( ) [ gx ( ) gx ( )] from the analytic expression for this form This expression is given by equation (34) The variance of ( β x) converges to zero at the same rate as 6 β ( x) Therefore, ( β x) is not consistent for β ( x) in the sense that ( β x)/ β ( x) does not converge in probability to one The methods for finding pointwise and uniform confidence bands for g take account of this inconsistency See Steps 6 and 7 below
9 Step 5: Obtain the normalized estimate of the bias and effective significance level The normalized bias of gx ( ) is defined as λ( x) = β( x)/ σ ( x) and is estimated by λ( x) = β( x)/ σ ( x) The effective significance level at point x, ( α x, α 0), is defined as the solution in α to the equation Φ[ z λ( x)] Φ[ z λ( x)] = 1 α 1 α/ 1 α/ 0 g Step 6: Construct a pointwise confidence band for g Let ξ [0,1] Let αξ ( α 0) be the ξ g quantile of points in the set { α( x, α0): x } Define z( ) = Construct the pointwise α0 z1 α ( α )/ ξ 0 confidence band 1/ 1/ { x y g x z x nh y g x z x nh } [ α ( α )] = (, ): ( ) ( α ) σ ( )/( ) ( ) + ( α ) σ ( )/( ) n x 0 0 g 0 g It is shown in Section 33 that the pointwise band n[ αξ ( α0)] covers gx ( ) with probability at least 1 α 0 except for a proportion ξ of points x Specifically, for α( x, α 0) as in (1), let αξ ( α 0) denote the ξ quantile of the points { α( x, α0): x } Define the set () x( α0) = { x : α( x, α0) > αx( α0)} Then liminf P{[ xgx, ( )] [ αx ( α )} 1 α n n 0 0 for each x x ( α0) ξ ( α0) is the set of points x on which the pointwise confidence band has an asymptotic coverage probability of at least 1 α0 It contains a fraction 1 ξ of points x Specifically, let and, respectively, denote the Lebesgue measures of the sets and ξ ( α0) Then / = 1 ξ Step 7: Construct a uniform confidence band for g Define (3) λ max = max λ( x) x and (4) λ min = min λ( x) x Let W 1 denote the mean-zero Gaussian process defined in Theorem 31 in Section 31 Define t Û as the solution in t to (5) x P t λmin W1 t λmax x = 1 a0 h The asymptotic uniform confidence band is 7
10 ( ) {( y, x): g( x) t ( x)/( nh) y g( x) + t ( x)/( nh) ; x } 1/ 1/ U α0 U σg U σg The quantities max λ and min λ can be computed by replacing in (3) and (4) with a fine grid of equally spaced points The critical value t Û can be computed by replacing in (5) with the grid Heteroskedasticity A heteroskedastic version of (11) is (6) Y = g( X) + σ( X) ε; P( ε 0) = τ, where σ () is a scale function and ε is independent of X Identification of σ requires normalizing the scale of ε This is done by setting the interquartile range (IQR) of ε equal to 1 Then σ ( x) = IQR( Y X = x) Let gx ( ) be the local linear quantile regression estimate of gx ( ) and ( σ x) be a consistent nonparametric estimate of IQR( Y X = x) The residuals of model (5) are Yi g( Xi) εi = ( σ X ) i The centered residuals of (6) are as in Step after replacing ε i with εi The estimate of the scaled asymptotic variance of the estimate of gx ( ) in (6) is σ ( x) τ(1 τ) BK g x fx ( x)[ f ε (0)] σ ( ) =, where f ε is now based on the εi s Bootstrap sampling is done by setting * * i ( i) σ( i) εi Y = g X + X, where the ε i * s are sampled randomly with replacement from the centered εi s Steps 4-7 for construction of pointwise and uniform confidence bands remain as in Section 1 but with the foregoing modifications of g σ ( x ) and the bootstrap sampling procedure We conjecture that our method can be extended to the generalization of model (11) in which P( ε 0) = τ is replaced by P( ε 0 X) = τ, but we do not analyze this extension here 3 THEORETICAL RESULTS This section presents theorems giving conditions under which the pointwise and uniform confidence bands constructed in Steps 6-7 of Section 1 have the claimed coverage properties when ĝ is a local linear quantile regression estimator Theorem 31 shows that 1/ ( nh) [ gx ( ) gx ( )] is 8
11 approximated sufficiently accurately by the sum of its asymptotic bias and a mean-zero Gaussian process Theorem 3 shows that a similar approximation applies to the bootstrap bias estimator These two approximations are combined in Theorem 34 to show that the bootstrap procedure of Section 1 yields pointwise confidence intervals with the coverage probabilities explained in Step 6 of Section 1 Theorem 35 shows that the pointwise confidence intervals of Theorem 34 can be widened to construct a uniform confidence band We make the following assumptions: Assumption 1: (i) The data { Y, X : i = 1,, n} are an independent random sample from model i i (11); (ii) X in (11) has compact support; (iii) ε in (11) is independent of X and P( ε 0) = τ for some τ (0,1) Assumption : (i) The distribution of X is absolutely continuous with respect to Lebesgue measure with probability density function f X ; (ii) f X is bounded away from 0 on supp( X ) and twice continuously differentiable on the interior of supp( X ) Assumption 3: The distribution of ε is absolutely continuous with respect to Lebesgue measure with probability density function f ε ; (ii) f ε is twice continuously differentiable and f ε (0) > 0 Assumption 4: The function g in (11) is three times continuously differentiable on the interior of supp( X ) ; (ii) ĝ is a local linear quantile regression estimator of g (iii) There is a compact set such that [ gx ( ), g ( x)] for each x Assumption 5: The kernel K is a probability density function with support [ 1,1], symmetrical around 0, and twice continuously differentiable on ( 1,1) Assumption 6: The bandwidth h used to construct ĝ satisfies: (i) 1/5 h = dn, where d is a function of the data { Y, X : i= 1,, n} and p d d0 as n for some finite constant d 0 > 0 (ii) There exists a finite constant D 1 > 0 such that ( 1 0 ) P d d > n D 0 as n (iii) There are constants D and D 3 such that 0< D < D3 < 1 and ( D 3 D P n h n ) = 1 O( n C ) as n for all finite C > 0 Assumption 1 defines the data generation process Assumptions -4 are smoothness assumptions Assumption 5 specifies standard properties of K Assumption 6 is satisfied by standard bandwidth i i 9
12 choice methods such as cross-validation and plug-in methods Under assumptions 1-6, local linear estimates of g obtained using the random bandwidth h and the deterministic bandwidth asymptotically equivalent See Lemma A1 in the supplementary appendix h 1/5 = dn are Asymptotic Approximations to 1/ ( nh) [ gx ( ) gx ( )] and the Bootstrap Bias Estimate The asymptotic coverage probabilities of the pointwise and uniform confidence bands defined in Steps 6 and 7 of Section 1 depend on strong asymptotic approximations to 1/ ( nh) [ gx ( ) gx ( )] and the bootstrap estimate of 1/ Enh ( ) [ gx ( ) gx ( )] These approximations are given by the following theorems Theorem 31: Let assumptions 1-6 hold Define ψ K 0( x) 1/ X 1/ [ τ(1 τ) B ] =, f ( x) f ε (0) κ = v K() v dv, and h 1/5 = dn There exists a Gaussian process W1 ( x ) defined on the same probability space as the 0 0 data such that EW [ 1( x )] = 0 for all x, 1 EW [ ( x )] = 1 for all x, and for any η > 0 lim sup ( ) [ ( ) ( )] ( ) + ( ) > h = 0 5/ 1/ d0 κ x P nh gx gx g x ψ0 xw1 n x h 0 It follows from Theorem 31 that for each x, (31) 1/ ( nh) [ gx ( ) gx ( )] N[ µ, V( x)], d g g where (3) and 5/ d0 κ µ g = g ( x) (33) 0 Vg ( x ) = ψ ( x ) Moreover, asymptotically, (34) and (35) 0κ h Egx ( ) gx ( ) = g ( x) τ(1 τ) B ( nh ) Var[ g( x)] = ( ) = 0 K σ g x fx ( x)[ f ε (0)] Properties (31)-(35) were obtained previously by Fan, Hu, and Truong (1994) and Yu and Jones (1997) 10
13 The quantity ( nh0 ) Var[ g ( x )] can be estimated consistently by replacing f ( x ) and f ε (0) on the right-hand sides of (33) and (35) by the consistent estimators f X ( x ) and f ε (0) Bootstrap estimation of 1/ 0 Enh ( ) [ gx ( ) gx ( )] relies on the strong approximation given by the following theorem Theorem 3: Let assumptions 1-6 hold Let the data Define * E denote the bootstrap expectation conditional on X and Then 1 5/ 1 = τ 0 ε A ( x) d f (0) f ( x) 1 τ A ( x) = BK fx( x) τ X 1/ (i) For all x, and any η > 0 1/ n * * A1( x) κ x lim P sup E [ Tnb ( x)] + g ( x) A( xw ) 1 > h = 0 n x h h0 (ii) There exists a Gaussian process ( x) such that E[ ( x)] = 0 for all x, E[ ( x)] = 1 for all x [0,1], and for any η > 0 β ( x) lim P sup l( x) ( ) 0 n x g ( ) + x > η = s x For any α (0,1) and x define ( π x, α) =Φ z λ( x) Φ z λ( x) 1 α/ 1 α/ The following corollary to Theorem 3 is used to establish the asymptotic coverage probabilities of the confidence bands constructed in Steps 6 and 7 of Section 1 Corollary 33: Let assumptions 1-6 hold Then for any η > 0 and 0< α < 1 β( x) β( x) lim P sup p( x, α) Φ z ( x) Φ z ( x) > η = 0 ( ) ( x) 1 α/ 1 α/ n x sg x sg 33 Coverage Probabilities of Confidence Bands This section shows that the pointwise and uniform confidence bands constructed in Steps 6 and 7 of Section 1 have asymptotic coverage probabilities of at least 1 α0 We use the following notation Let α0 (0,1/ ) Define ( α x, α 0) as in Step 5 Define T( x, α 0) as the solution in T to 11
14 Define β( x) β( x) Φ T ( x) Φ T ( x) = 1 α0 σg( x) σg( x) Ax (, α ) = {1 Φ [ T( x, α )]} 0 0 As in (1), define α( x, α 0) to be the solution in a to β( x) β( x) Φ z1 a/ Φ z1 a/ = 1 a0 σg( x) σg( x) Let αξ ( α 0) denote the ξ -level quantile of points in the set { α( x, α0): x } Define ξ ( α0) as in () The following theorem gives the asymptotic coverage probability of the pointwise confidence band constructed in Step 6 Theorem 34: Let assumptions 1-6 hold For all C > 0 and η > 0, (i) lim P sup α( x, α0) Ax (, α0) > η = 0 n x, ( x) C (ii) lim P α ξ ( α0) α ξ ( α0) = 1 n liminf [, ( )] [ α ( α )] 1 α for each x x ( α0) (iii) P{ xgx n x } n 0 0 Theorem 34(iii) shows that the pointwise band n[ αξ ( α0)] covers gx ( ) with probability at least 1 α0 except for a proportion ξ of points x Now consider the uniform confidence band constructed in Step 7 of Section 1 It follows from Theorem 31 that up to asymptotically negligible terms 1/ ( nh) [ gx ( ) gx ( )] β ( x) x = + W1 σg( x) σg( x) h0 uniformly over x Recall that λ( x) = β( x)/ σ ( x) If λ ( x) were known, an asymptotic uniform g 1 α 0 confidence band for g would be x tu λ( x) + W1 t h0 where t U is the solution in t to U, Define x P t λ( x) W1 t λ( x) x = 1 α0 h0 1
15 and Then λmax = max λ( x) x λmin = min λ( x) x x x P tu λmin W1 tu λmax x P tu λ( x) W1 tu λ( x) x h0 h0 Therefore, asymptotically (36) 1/ ( nh) [ gx ( ) gx ( )] P tu tu x 1 α0 σ g ( x) if t U is chosen so that x P tu λmin W1 tu λmax x = 1 a0 h0 The quantities λ max and λ min are unknown in applications A feasible confidence band can be obtained by replacing them with the bootstrap estimates λ = max λ( x) and λ max min x = min λ( x) x Similarly, the unknown quantities σ g ( x ) and h 0 can be replaced with σ g ( x ) and h, respectively The critical value t U is replaced by t Û, which is the solution in t to x h 1, (37) P t λmin W1 t λmax x = a0 where max λ and min λ are treated as non-stochastic constants, not random variables, when calculating the probability on the right-hand side of (37) The resulting uniform confidence band is 1/ ( ) [ nh gx ( ) gx ( )] t U tu; x σ ( x) g The following theorem establishes the asymptotic coverage probability of this interval Theorem 35: Let assumptions 1-6 hold Then 1/ ( nh) [ gx ( ) gx ( )] lim inf P t U tu; x 1 α0 n σ g ( x) 13
16 The covariance function of W 1 can be estimated consistently See equation (A13) in the supplementary appendix The probability on the right-hand side of (37) can be computed by simulation with arbitrary accuracy by replacing the covariance function of W 1 with the consistent estimate and the continuum with a grid of equally spaced points 4 NUMERICAL EXPERIMENTS This section reports the results of a set of Monte Carlo experiments that illustrate the finitesample performance of the method described in Section 41 Design Data { Y, X : i = 1,, n} were generated from the models i i Y = g ( X) + ε; j = 1,,3, j where g ( x ) = x+ 5 φ (10 x ), 1 sin(15 π x) g( x) =, x [sgn( x) + 1] sin(15 π x) g3( x) =, 1 + x [sgn( x) + 1] φ is the standard normal probability density function, and X ~ U[ 1,1] The distribution of ε is N ( µ,1) for τ = 05, τ = 05, and τ = 075, respectively, where µ 05 = 06745, µ 050 = 0, and τ µ 075 = Thus, P( ε 0) = τ for each value of τ The functions g j were used in numerical experiments by HH and other authors Graphs of these functions are shown in Figure 1 The function g 1 has a sharp peak and is the most challenging for our method The function g is less challenging than g 1 The function g 3 is the smoothest and least challenging The sample sizes in the experiments were n = 100, 500, and 1000 The kernel function was Kv ( ) = 075(1 v) I( v 1) The bandwidth h for local linear estimation of the g j s was chosen using the plug-in method of Yu and Jones (1998) Bandwidths for estimating fx ( x ) and f ε (0) were chosen by Silverman s rule of thumb To avoid boundary effects, the set was chosen so that its boundaries were at least one bandwidth from the boundaries of [ 1,1] This resulted in = [ 09,09] for experiments with g 1 and g and = [ 085,085] for experiments with g 3 is narrower for the experiments with g 3 because that 14
17 function is smoother than g 1 and g and has a larger bandwidth We set α 0 = 005 and 1 ξ = 095 Pointwise confidence bands were computed using an equally spaced grid of points x with a spacing of 005 The grid spacing was 00 for uniform confidence bands The proportion of points x at which the coverage probability is at least 095 was estimated by the proportion of grid points at which the coverage probability equals or exceeds this value There were 1000 Monte Carlo replications in each experiment We also computed pointwise confidence bands using undersmoothing and the explicit bias correction method of Schucany and Sommers (1977) There are no satisfactory empirical methods for choosing an undersmoothing bandwidth or the auxiliary bandwidth required for explicit bias correction Therefore, for undersmoothing, we set the bandwidth equal to γ 1 h, where h is the bandwidth selected by the method of Yu and Jones (1998) and γ1 1 is a constant For explicit bias correction, we set the auxiliary bandwidth equal to dn γ /5, where γ 1 is a constant and d is as in assumption 6 The values of γ 1 and γ were chosen to achieve coverage probabilities of at least 095 for as large a proportion of values of x in the grid as possible This approach cannot be used in applications and gives an advantage to undersmoothing and explicit bias correction Nonetheless, it will be seen in Section 4 that the performance of these methods is poor compared to that of the method of Section 1 4 Results of the Experiments Tables 1-3 show properties of pointwise confidence bands for τ = 05, 050 and 075, respectively At all quantiles and sample sizes, the bootstrap method described in Section 1 has much higher proportions of values of x for which the probability of covering of gx ( ) exceeds 095 than do the undersmoothing and explicit bias correction methods When n = 100, the bootstrap method s proportions exceed 070 for j = 1 and, and 095 for j = 3 When n = 1000, the bootstrap method s proportions exceed 09 for all values of j By contrast, the proportion of values of x for which undersmoothing achieves a coverage probability of at least 095 is below 065 for all values of n and j The absolute error in the coverage probability (column 5 of Tables 1-3) is the absolute value of the difference between the actual coverage probability and the nominal probability of 095 Thus, the absolute error increases when the actual coverage probability exceeds 095 as well as when the actual coverage probability is less than 095 The proportion of values of x for which explicit bias correction achieves a coverage probability of at least 095 is below 00 for all values of n and j Undersmoothing and explicit bias correction 15
18 perform poorly despite choosing the bandwidth for undersmoothing and the auxiliary bandwidth for explicit bias correction to achieve optimal performance of these methods Although confidence intervals based on undersmoothing and explicit bias correction rarely achieve the nominal coverage probability of 095, intervals based on these methods often have coverage probabilities of at least 093 if n = 1000 and γ 1 and γ are chosen to maximize the proportions of x values at which the coverage probability equals or exceeds this value For example, with g 1 ( x ), τ = 050, and undersmoothing, the proportion of x values with coverage probabilities of at least 093 is 097 With explicit bias correction, the proportion of x values with coverage probabilities of at least 093 is 065 Figure shows the coverage probabilities obtained by the three methods as functions of x with n = 1000 The relatively low proportions of points at which the coverage probability of the bootstrap method equals or exceeds 095 for g 1 are due to the sharp peak of this function in the vicinity of x = 0, which causes the bias of ĝ 1 to be especially large HH provide a theoretical explanation for why the bootstrap method performs poorly in regions of unusually high bias The phenomenon is illustrated in Table 4, which shows the proportion of points for which the bootstrap confidence band covers g 1 ( x ) with probability exceeding 095 when the interval [ 005,005] containing the peak is excluded from The proportions of points for which the coverage probability equals or exceeds 095 is at least 094 when n = 500 or n = 1000 except for n = 500 and τ = 05, when the proportion is 091 The function g has peaks and troughs at x = 015 and x = 035, but these are not as sharp as the peak of g 1 Consequently, they have little effect on the coverage probabilities for g when n 500 Table 5 shows the coverage probabilities of uniform confidence bands obtained with the bootstrap method The coverage probabilities for g and g 3 at all quantiles equal or exceed 095 if n 500 and 093 if n = 100 The coverage probabilities for g 1 with n 500 equal or exceed 094 except for n = 500 and τ = 05, when the coverage probability is 09 5 CONCLUSIONS This paper has described a bootstrap method for constructing pointwise and uniform confidence bands for a conditional quantile function that is estimated nonparametrically The method is based on local polynomial estimation and uses only a bandwidth that can be selected using standard methods such as cross validation or plug-in In contrast to other methods for constructing confidence bands, the bootstrap method does not require bandwidths that under- or oversmooth the nonparametric function estimator This is an important advantage of the bootstrap method, because there are no satisfactory 16
19 empirical methods for selecting bandwidths that under- or oversmooth a nonparametric estimator The bootstrap method presented here is an extension of the method of Hall and Horowitz (013) for conditional mean functions to conditional quantile functions and uniform confidence bands The results of Monte Carlo experiments have illustrated the good finite-sample performance of the bootstrap method and the poor performance of methods based on under- or oversmoothing 17
20 TABLE 1: SIMULATION RESULTS FOR τ=05 Method n j Prop Av Abs Av Width with Error of Cov Cov Prob Prob 095 Bootstrap method of Sec Undersmooth Bias Corr
21 TABLE : SIMULATION RESULTS FOR τ=050 Method n j Prop Av Abs Av Width with Error of Cov Cov Prob Prob 095 Bootstrap method of Sec Undersmooth Bias Corr
22 TABLE 3: SIMULATION RESULTS FOR τ=075 Method n j Prop Av Abs Av Width with Error of Cov Cov Prob Prob 095 Bootstrap method of Sec Undersmooth Bias Corr
23 TABLE 4: THE BOOTSTRAP METHOD S COVERAGE PROBABILITIES FOR g 1 WHEN THE INTERVAL [-005,005] IS REMOVED FROM S n τ Prop with Cov Prob Av Abs Error of Cov Prob Av Width
24 TABLE 5: COVERAGE PROBABILITIES OF UNIFORM CONFIDENCE BANDS OBTAINED BY THE BOOTSTRAP METHOD τ n j Cov Prob
25 g(x) X Figure 1: Conditional quantile functions Solid line is g 1 ( x ) Long dashes are g ( x ) Short dashes are g 3 ( x ) 3
26 X Figure a: Coverage probabilities of nominal 95% pointwise confidence bands with gx ( ) = x+ 5 φ(10 x) and n = 1000 Solid line: proposed bootstrap method Dashes: explicit bias correction method Dashdots: undersmoothing method Dots: 95% line 4
27 X Figure b: Coverage probabilities of nominal 95% pointwise confidence bands with gx ( ) = sin(15 π x) /{ x[sgn( x) + 1]} and n = 1000 Solid line: proposed bootstrap method Dashes: explicit bias correction method Dash-dots: undersmoothing method Dots: 95% line 5
28 X Figure c: Coverage probabilities of nominal 95% pointwise confidence bands with gx ( ) = sin(15 π x) / {1 + x[sgn( x) + 1]} and n = 1000 Solid line: proposed bootstrap method Dashes: explicit bias correction method Dash-dots: undersmoothing method Dots: 95% line 6
29 REFERENCES Bjerve, S, KA Doksum, and BS Yandell (1985) Uniform confidence bounds for regression based on a simple moving average Scandinavian Journal of Statistics, 1, Boucheron, S, G Lugosi, and P Massart (013) Concentration Inequalities: A Nonasymptotic Theory of Independence Oxford, UK: Oxford University Press Boyd, S and L Vandenberghe (004) Convex Optimization Cambridge University Press: Cambridge, UK Calonico, S, MD Cattaneo, and MH Farrell (016) On the effect of bias estimation on coverage accuracy in nonparametric inference Working paper, Department of Economics, University of Michigan Chen, SX (1996) Empirical likelihood confidence intervals for nonparametric density estimation Biometrika, 83, Chen, SX, W Härdle, and M Li (003) An empirical likelihood goodness-of-fit test for time series Journal of the Royal Statistical Society Series B, 65, Claeskens, G and I Van Keilegom (003) Bootstrap confidence bands for regression curves and their derivatives Annals of Statistics, 31, Eubank, RL and PL Speckman (1993) Confidence regions in nonparametric regression Journal of the American Statistical Association, 88, Fan, J, T-C Hu, and YK Truong (1994) Robust nonparametric function estimation Scandinavian Journal of Statistics, 1, Hall, P (199) Effect of bias estimation on coverage accuracy of bootstrap confidence intervals for a probability density Annals of Statistics, 0, Hall, P and JL Horowitz (013) A simple bootstrap method for constructing nonparametric confidence bands for functions Annals of Statistics, 41, Hall, P and AB Owen (1993) Empirical likelihood confidence bands in density estimation Journal of Computational and Graphical Statistics,, Härdle, W and AW Bowman (1988) Bootstrapping in nonparametric regression: Local adaptive smoothing and confidence bands Journal of the American Statistical Association, 83, Härdle, W, S Huet, and E Jolivet (1995) Better bootstrap confidence intervals for regression curve estimation Statistics, 6, Härdle, W, S Huet, E Mammen, and S Sperlich (004) Bootstrap inference in semiparametric generalized additive models Econometric Theory, 0,
30 Härdle, W and JS Marron (1991) Bootstrap simultaneous error bars for nonparametric regression Annals of Statistics, 19, McMurry, TL and DN Politis (008) Bootstrap confidence intervals in nonparametric regression with built-in bias correction Statistics and Probability Letters, 78, Neumann, MH (1995) Automatic bandwidth choice and confidence intervals in nonparametric regression Annals of Statistics, Neumann, MH and J Polzehl (1998) Simultaneous bootstrap confidence bands in nonparametric regression Journal of Nonparametric Statistics, 9, Picard, D and K Tribouley (000) Adaptive confidence interval for pointwise curve estimation Annals of Statistics, 8, Schucany, WR and JP Sommers (1977) Improvement of kernel type density estimators Journal of the American Statistical Association, 7, Sun, J and CR Loader (1994) Simultaneous confidence bands for linear regression and smoothing Annals of Statistics,, Xia, Y (1998) Bias-corrected confidence bands in nonparametric regression Journal of the Royal Statistical Society Series B, 60, Yu, K and MC Jones (1998) Local linear quantile regression Journal of the American Statistical Association,
A simple bootstrap method for constructing nonparametric confidence bands for functions
A simple bootstrap method for constructing nonparametric confidence bands for functions Peter Hall Joel Horowitz The Institute for Fiscal Studies Department of Economics, UCL cemmap working paper CWP4/2
More informationNonparametric Inference via Bootstrapping the Debiased Estimator
Nonparametric Inference via Bootstrapping the Debiased Estimator Yen-Chi Chen Department of Statistics, University of Washington ICSA-Canada Chapter Symposium 2017 1 / 21 Problem Setup Let X 1,, X n be
More informationConfidence intervals for kernel density estimation
Stata User Group - 9th UK meeting - 19/20 May 2003 Confidence intervals for kernel density estimation Carlo Fiorio c.fiorio@lse.ac.uk London School of Economics and STICERD Stata User Group - 9th UK meeting
More informationUniversity, Tempe, Arizona, USA b Department of Mathematics and Statistics, University of New. Mexico, Albuquerque, New Mexico, USA
This article was downloaded by: [University of New Mexico] On: 27 September 2012, At: 22:13 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered
More informationNonparametric confidence intervals. for receiver operating characteristic curves
Nonparametric confidence intervals for receiver operating characteristic curves Peter G. Hall 1, Rob J. Hyndman 2, and Yanan Fan 3 5 December 2003 Abstract: We study methods for constructing confidence
More informationWorking Paper No Maximum score type estimators
Warsaw School of Economics Institute of Econometrics Department of Applied Econometrics Department of Applied Econometrics Working Papers Warsaw School of Economics Al. iepodleglosci 64 02-554 Warszawa,
More informationSmooth simultaneous confidence bands for cumulative distribution functions
Journal of Nonparametric Statistics, 2013 Vol. 25, No. 2, 395 407, http://dx.doi.org/10.1080/10485252.2012.759219 Smooth simultaneous confidence bands for cumulative distribution functions Jiangyan Wang
More informationInference in Nonparametric Series Estimation with Data-Dependent Number of Series Terms
Inference in Nonparametric Series Estimation with Data-Dependent Number of Series Terms Byunghoon ang Department of Economics, University of Wisconsin-Madison First version December 9, 204; Revised November
More informationPreface. 1 Nonparametric Density Estimation and Testing. 1.1 Introduction. 1.2 Univariate Density Estimation
Preface Nonparametric econometrics has become one of the most important sub-fields in modern econometrics. The primary goal of this lecture note is to introduce various nonparametric and semiparametric
More informationAdditive Isotonic Regression
Additive Isotonic Regression Enno Mammen and Kyusang Yu 11. July 2006 INTRODUCTION: We have i.i.d. random vectors (Y 1, X 1 ),..., (Y n, X n ) with X i = (X1 i,..., X d i ) and we consider the additive
More informationNonparametric Econometrics
Applied Microeconometrics with Stata Nonparametric Econometrics Spring Term 2011 1 / 37 Contents Introduction The histogram estimator The kernel density estimator Nonparametric regression estimators Semi-
More informationLocal linear multiple regression with variable. bandwidth in the presence of heteroscedasticity
Local linear multiple regression with variable bandwidth in the presence of heteroscedasticity Azhong Ye 1 Rob J Hyndman 2 Zinai Li 3 23 January 2006 Abstract: We present local linear estimator with variable
More information11. Bootstrap Methods
11. Bootstrap Methods c A. Colin Cameron & Pravin K. Trivedi 2006 These transparencies were prepared in 20043. They can be used as an adjunct to Chapter 11 of our subsequent book Microeconometrics: Methods
More informationSupplemental Material for KERNEL-BASED INFERENCE IN TIME-VARYING COEFFICIENT COINTEGRATING REGRESSION. September 2017
Supplemental Material for KERNEL-BASED INFERENCE IN TIME-VARYING COEFFICIENT COINTEGRATING REGRESSION By Degui Li, Peter C. B. Phillips, and Jiti Gao September 017 COWLES FOUNDATION DISCUSSION PAPER NO.
More informationSMOOTHED BLOCK EMPIRICAL LIKELIHOOD FOR QUANTILES OF WEAKLY DEPENDENT PROCESSES
Statistica Sinica 19 (2009), 71-81 SMOOTHED BLOCK EMPIRICAL LIKELIHOOD FOR QUANTILES OF WEAKLY DEPENDENT PROCESSES Song Xi Chen 1,2 and Chiu Min Wong 3 1 Iowa State University, 2 Peking University and
More informationUniform Inference for Conditional Factor Models with Instrumental and Idiosyncratic Betas
Uniform Inference for Conditional Factor Models with Instrumental and Idiosyncratic Betas Yuan Xiye Yang Rutgers University Dec 27 Greater NY Econometrics Overview main results Introduction Consider a
More informationUNIVERSITÄT POTSDAM Institut für Mathematik
UNIVERSITÄT POTSDAM Institut für Mathematik Testing the Acceleration Function in Life Time Models Hannelore Liero Matthias Liero Mathematische Statistik und Wahrscheinlichkeitstheorie Universität Potsdam
More informationInference on distributions and quantiles using a finite-sample Dirichlet process
Dirichlet IDEAL Theory/methods Simulations Inference on distributions and quantiles using a finite-sample Dirichlet process David M. Kaplan University of Missouri Matt Goldman UC San Diego Midwest Econometrics
More informationDEPARTMENT MATHEMATIK ARBEITSBEREICH MATHEMATISCHE STATISTIK UND STOCHASTISCHE PROZESSE
Estimating the error distribution in nonparametric multiple regression with applications to model testing Natalie Neumeyer & Ingrid Van Keilegom Preprint No. 2008-01 July 2008 DEPARTMENT MATHEMATIK ARBEITSBEREICH
More informationA New Method for Varying Adaptive Bandwidth Selection
IEEE TRASACTIOS O SIGAL PROCESSIG, VOL. 47, O. 9, SEPTEMBER 1999 2567 TABLE I SQUARE ROOT MEA SQUARED ERRORS (SRMSE) OF ESTIMATIO USIG THE LPA AD VARIOUS WAVELET METHODS A ew Method for Varying Adaptive
More informationLocal Polynomial Regression
VI Local Polynomial Regression (1) Global polynomial regression We observe random pairs (X 1, Y 1 ),, (X n, Y n ) where (X 1, Y 1 ),, (X n, Y n ) iid (X, Y ). We want to estimate m(x) = E(Y X = x) based
More informationHigh Dimensional Empirical Likelihood for Generalized Estimating Equations with Dependent Data
High Dimensional Empirical Likelihood for Generalized Estimating Equations with Dependent Data Song Xi CHEN Guanghua School of Management and Center for Statistical Science, Peking University Department
More informationGoodness-of-fit tests for the cure rate in a mixture cure model
Biometrika (217), 13, 1, pp. 1 7 Printed in Great Britain Advance Access publication on 31 July 216 Goodness-of-fit tests for the cure rate in a mixture cure model BY U.U. MÜLLER Department of Statistics,
More informationSingle Index Quantile Regression for Heteroscedastic Data
Single Index Quantile Regression for Heteroscedastic Data E. Christou M. G. Akritas Department of Statistics The Pennsylvania State University SMAC, November 6, 2015 E. Christou, M. G. Akritas (PSU) SIQR
More informationInference For High Dimensional M-estimates: Fixed Design Results
Inference For High Dimensional M-estimates: Fixed Design Results Lihua Lei, Peter Bickel and Noureddine El Karoui Department of Statistics, UC Berkeley Berkeley-Stanford Econometrics Jamboree, 2017 1/49
More informationIdentification and Estimation of Partially Linear Censored Regression Models with Unknown Heteroscedasticity
Identification and Estimation of Partially Linear Censored Regression Models with Unknown Heteroscedasticity Zhengyu Zhang School of Economics Shanghai University of Finance and Economics zy.zhang@mail.shufe.edu.cn
More informationDiscussion of the paper Inference for Semiparametric Models: Some Questions and an Answer by Bickel and Kwon
Discussion of the paper Inference for Semiparametric Models: Some Questions and an Answer by Bickel and Kwon Jianqing Fan Department of Statistics Chinese University of Hong Kong AND Department of Statistics
More informationA Bootstrap Test for Conditional Symmetry
ANNALS OF ECONOMICS AND FINANCE 6, 51 61 005) A Bootstrap Test for Conditional Symmetry Liangjun Su Guanghua School of Management, Peking University E-mail: lsu@gsm.pku.edu.cn and Sainan Jin Guanghua School
More informationEfficient Estimation of Population Quantiles in General Semiparametric Regression Models
Efficient Estimation of Population Quantiles in General Semiparametric Regression Models Arnab Maity 1 Department of Statistics, Texas A&M University, College Station TX 77843-3143, U.S.A. amaity@stat.tamu.edu
More informationTime Series and Forecasting Lecture 4 NonLinear Time Series
Time Series and Forecasting Lecture 4 NonLinear Time Series Bruce E. Hansen Summer School in Economics and Econometrics University of Crete July 23-27, 2012 Bruce Hansen (University of Wisconsin) Foundations
More informationSingle Index Quantile Regression for Heteroscedastic Data
Single Index Quantile Regression for Heteroscedastic Data E. Christou M. G. Akritas Department of Statistics The Pennsylvania State University JSM, 2015 E. Christou, M. G. Akritas (PSU) SIQR JSM, 2015
More informationDefect Detection using Nonparametric Regression
Defect Detection using Nonparametric Regression Siana Halim Industrial Engineering Department-Petra Christian University Siwalankerto 121-131 Surabaya- Indonesia halim@petra.ac.id Abstract: To compare
More informationSimple and Honest Confidence Intervals in Nonparametric Regression
Simple and Honest Confidence Intervals in Nonparametric Regression Timothy B. Armstrong Yale University Michal Kolesár Princeton University June, 206 Abstract We consider the problem of constructing honest
More informationMichael Lechner Causal Analysis RDD 2014 page 1. Lecture 7. The Regression Discontinuity Design. RDD fuzzy and sharp
page 1 Lecture 7 The Regression Discontinuity Design fuzzy and sharp page 2 Regression Discontinuity Design () Introduction (1) The design is a quasi-experimental design with the defining characteristic
More informationConvergence rates for uniform confidence intervals based on local polynomial regression estimators
Journal of Nonparametric Statistics ISSN: 1048-5252 Print) 1029-0311 Online) Journal homepage: http://www.tandfonline.com/loi/gnst20 Convergence rates for uniform confidence intervals based on local polynomial
More informationEconomics Division University of Southampton Southampton SO17 1BJ, UK. Title Overlapping Sub-sampling and invariance to initial conditions
Economics Division University of Southampton Southampton SO17 1BJ, UK Discussion Papers in Economics and Econometrics Title Overlapping Sub-sampling and invariance to initial conditions By Maria Kyriacou
More informationProgram Evaluation with High-Dimensional Data
Program Evaluation with High-Dimensional Data Alexandre Belloni Duke Victor Chernozhukov MIT Iván Fernández-Val BU Christian Hansen Booth ESWC 215 August 17, 215 Introduction Goal is to perform inference
More informationA Local Generalized Method of Moments Estimator
A Local Generalized Method of Moments Estimator Arthur Lewbel Boston College June 2006 Abstract A local Generalized Method of Moments Estimator is proposed for nonparametrically estimating unknown functions
More informationSome Theories about Backfitting Algorithm for Varying Coefficient Partially Linear Model
Some Theories about Backfitting Algorithm for Varying Coefficient Partially Linear Model 1. Introduction Varying-coefficient partially linear model (Zhang, Lee, and Song, 2002; Xia, Zhang, and Tong, 2004;
More informationMonte Carlo Studies. The response in a Monte Carlo study is a random variable.
Monte Carlo Studies The response in a Monte Carlo study is a random variable. The response in a Monte Carlo study has a variance that comes from the variance of the stochastic elements in the data-generating
More informationSEMIPARAMETRIC QUANTILE REGRESSION WITH HIGH-DIMENSIONAL COVARIATES. Liping Zhu, Mian Huang, & Runze Li. The Pennsylvania State University
SEMIPARAMETRIC QUANTILE REGRESSION WITH HIGH-DIMENSIONAL COVARIATES Liping Zhu, Mian Huang, & Runze Li The Pennsylvania State University Technical Report Series #10-104 College of Health and Human Development
More informationNegative Association, Ordering and Convergence of Resampling Methods
Negative Association, Ordering and Convergence of Resampling Methods Nicolas Chopin ENSAE, Paristech (Joint work with Mathieu Gerber and Nick Whiteley, University of Bristol) Resampling schemes: Informal
More informationNonparametric Modal Regression
Nonparametric Modal Regression Summary In this article, we propose a new nonparametric modal regression model, which aims to estimate the mode of the conditional density of Y given predictors X. The nonparametric
More informationCALCULATION METHOD FOR NONLINEAR DYNAMIC LEAST-ABSOLUTE DEVIATIONS ESTIMATOR
J. Japan Statist. Soc. Vol. 3 No. 200 39 5 CALCULAION MEHOD FOR NONLINEAR DYNAMIC LEAS-ABSOLUE DEVIAIONS ESIMAOR Kohtaro Hitomi * and Masato Kagihara ** In a nonlinear dynamic model, the consistency and
More informationRobust Backtesting Tests for Value-at-Risk Models
Robust Backtesting Tests for Value-at-Risk Models Jose Olmo City University London (joint work with Juan Carlos Escanciano, Indiana University) Far East and South Asia Meeting of the Econometric Society
More informationinterval forecasting
Interval Forecasting Based on Chapter 7 of the Time Series Forecasting by Chatfield Econometric Forecasting, January 2008 Outline 1 2 3 4 5 Terminology Interval Forecasts Density Forecast Fan Chart Most
More informationInference For High Dimensional M-estimates. Fixed Design Results
: Fixed Design Results Lihua Lei Advisors: Peter J. Bickel, Michael I. Jordan joint work with Peter J. Bickel and Noureddine El Karoui Dec. 8, 2016 1/57 Table of Contents 1 Background 2 Main Results and
More informationTESTING REGRESSION MONOTONICITY IN ECONOMETRIC MODELS
TESTING REGRESSION MONOTONICITY IN ECONOMETRIC MODELS DENIS CHETVERIKOV Abstract. Monotonicity is a key qualitative prediction of a wide array of economic models derived via robust comparative statics.
More informationSmooth nonparametric estimation of a quantile function under right censoring using beta kernels
Smooth nonparametric estimation of a quantile function under right censoring using beta kernels Chanseok Park 1 Department of Mathematical Sciences, Clemson University, Clemson, SC 29634 Short Title: Smooth
More informationWeb-based Supplementary Material for. Dependence Calibration in Conditional Copulas: A Nonparametric Approach
1 Web-based Supplementary Material for Dependence Calibration in Conditional Copulas: A Nonparametric Approach Elif F. Acar, Radu V. Craiu, and Fang Yao Web Appendix A: Technical Details The score and
More informationQuantile Regression for Panel Data Models with Fixed Effects and Small T : Identification and Estimation
Quantile Regression for Panel Data Models with Fixed Effects and Small T : Identification and Estimation Maria Ponomareva University of Western Ontario May 8, 2011 Abstract This paper proposes a moments-based
More informationSIMPLE AND HONEST CONFIDENCE INTERVALS IN NONPARAMETRIC REGRESSION. Timothy B. Armstrong and Michal Kolesár. June 2016 Revised March 2018
SIMPLE AND HONEST CONFIDENCE INTERVALS IN NONPARAMETRIC REGRESSION By Timothy B. Armstrong and Michal Kolesár June 2016 Revised March 2018 COWLES FOUNDATION DISCUSSION PAPER NO. 2044R2 COWLES FOUNDATION
More informationSupplement to Quantile-Based Nonparametric Inference for First-Price Auctions
Supplement to Quantile-Based Nonparametric Inference for First-Price Auctions Vadim Marmer University of British Columbia Artyom Shneyerov CIRANO, CIREQ, and Concordia University August 30, 2010 Abstract
More informationAUTOMATIC CONTROL COMMUNICATION SYSTEMS LINKÖPINGS UNIVERSITET. Questions AUTOMATIC CONTROL COMMUNICATION SYSTEMS LINKÖPINGS UNIVERSITET
The Problem Identification of Linear and onlinear Dynamical Systems Theme : Curve Fitting Division of Automatic Control Linköping University Sweden Data from Gripen Questions How do the control surface
More informationWEIGHTED QUANTILE REGRESSION THEORY AND ITS APPLICATION. Abstract
Journal of Data Science,17(1). P. 145-160,2019 DOI:10.6339/JDS.201901_17(1).0007 WEIGHTED QUANTILE REGRESSION THEORY AND ITS APPLICATION Wei Xiong *, Maozai Tian 2 1 School of Statistics, University of
More informationNonparametric Identification of a Binary Random Factor in Cross Section Data - Supplemental Appendix
Nonparametric Identification of a Binary Random Factor in Cross Section Data - Supplemental Appendix Yingying Dong and Arthur Lewbel California State University Fullerton and Boston College July 2010 Abstract
More informationTruncated Regression Model and Nonparametric Estimation for Gifted and Talented Education Program
Global Journal of Pure and Applied Mathematics. ISSN 0973-768 Volume 2, Number (206), pp. 995-002 Research India Publications http://www.ripublication.com Truncated Regression Model and Nonparametric Estimation
More information41903: Introduction to Nonparametrics
41903: Notes 5 Introduction Nonparametrics fundamentally about fitting flexible models: want model that is flexible enough to accommodate important patterns but not so flexible it overspecializes to specific
More informationQuantile Processes for Semi and Nonparametric Regression
Quantile Processes for Semi and Nonparametric Regression Shih-Kang Chao Department of Statistics Purdue University IMS-APRM 2016 A joint work with Stanislav Volgushev and Guang Cheng Quantile Response
More informationSparse Nonparametric Density Estimation in High Dimensions Using the Rodeo
Outline in High Dimensions Using the Rodeo Han Liu 1,2 John Lafferty 2,3 Larry Wasserman 1,2 1 Statistics Department, 2 Machine Learning Department, 3 Computer Science Department, Carnegie Mellon University
More informationStatistical inference on Lévy processes
Alberto Coca Cabrero University of Cambridge - CCA Supervisors: Dr. Richard Nickl and Professor L.C.G.Rogers Funded by Fundación Mutua Madrileña and EPSRC MASDOC/CCA student workshop 2013 26th March Outline
More informationOPTIMAL POINTWISE ADAPTIVE METHODS IN NONPARAMETRIC ESTIMATION 1
The Annals of Statistics 1997, Vol. 25, No. 6, 2512 2546 OPTIMAL POINTWISE ADAPTIVE METHODS IN NONPARAMETRIC ESTIMATION 1 By O. V. Lepski and V. G. Spokoiny Humboldt University and Weierstrass Institute
More informationOn the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference
On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference Sebastian Calonico Matias D. Cattaneo Max H. Farrell November 4, 014 PRELIMINARY AND INCOMPLETE COMMENTS WELCOME Abstract
More informationIdentification and shape restrictions in nonparametric instrumental variables estimation
Identification shape restrictions in nonparametric instrumental variables estimation Joachim Freyberger Joel Horowitz The Institute for Fiscal Studies Department of Economics, UCL cemmap woring paper CWP3/3
More informationOn variable bandwidth kernel density estimation
JSM 04 - Section on Nonparametric Statistics On variable bandwidth kernel density estimation Janet Nakarmi Hailin Sang Abstract In this paper we study the ideal variable bandwidth kernel estimator introduced
More informationA Shape Constrained Estimator of Bidding Function of First-Price Sealed-Bid Auctions
A Shape Constrained Estimator of Bidding Function of First-Price Sealed-Bid Auctions Yu Yvette Zhang Abstract This paper is concerned with economic analysis of first-price sealed-bid auctions with risk
More informationLocally Robust Semiparametric Estimation
Locally Robust Semiparametric Estimation Victor Chernozhukov Juan Carlos Escanciano Hidehiko Ichimura Whitney K. Newey The Institute for Fiscal Studies Department of Economics, UCL cemmap working paper
More informationInference in Regression Discontinuity Designs with a Discrete Running Variable
Inference in Regression Discontinuity Designs with a Discrete Running Variable Michal Kolesár Christoph Rothe June 21, 2016 Abstract We consider inference in regression discontinuity designs when the running
More information7 Semiparametric Estimation of Additive Models
7 Semiparametric Estimation of Additive Models Additive models are very useful for approximating the high-dimensional regression mean functions. They and their extensions have become one of the most widely
More informationNonparametric Methods
Nonparametric Methods Michael R. Roberts Department of Finance The Wharton School University of Pennsylvania July 28, 2009 Michael R. Roberts Nonparametric Methods 1/42 Overview Great for data analysis
More informationNonparametric Identi cation and Estimation of Truncated Regression Models with Heteroskedasticity
Nonparametric Identi cation and Estimation of Truncated Regression Models with Heteroskedasticity Songnian Chen a, Xun Lu a, Xianbo Zhou b and Yahong Zhou c a Department of Economics, Hong Kong University
More informationBickel Rosenblatt test
University of Latvia 28.05.2011. A classical Let X 1,..., X n be i.i.d. random variables with a continuous probability density function f. Consider a simple hypothesis H 0 : f = f 0 with a significance
More informationIV Quantile Regression for Group-level Treatments, with an Application to the Distributional Effects of Trade
IV Quantile Regression for Group-level Treatments, with an Application to the Distributional Effects of Trade Denis Chetverikov Brad Larsen Christopher Palmer UCLA, Stanford and NBER, UC Berkeley September
More informationA Note on Data-Adaptive Bandwidth Selection for Sequential Kernel Smoothers
6th St.Petersburg Workshop on Simulation (2009) 1-3 A Note on Data-Adaptive Bandwidth Selection for Sequential Kernel Smoothers Ansgar Steland 1 Abstract Sequential kernel smoothers form a class of procedures
More informationTransformation and Smoothing in Sample Survey Data
Scandinavian Journal of Statistics, Vol. 37: 496 513, 2010 doi: 10.1111/j.1467-9469.2010.00691.x Published by Blackwell Publishing Ltd. Transformation and Smoothing in Sample Survey Data YANYUAN MA Department
More informationTESTING SERIAL CORRELATION IN SEMIPARAMETRIC VARYING COEFFICIENT PARTIALLY LINEAR ERRORS-IN-VARIABLES MODEL
Jrl Syst Sci & Complexity (2009) 22: 483 494 TESTIG SERIAL CORRELATIO I SEMIPARAMETRIC VARYIG COEFFICIET PARTIALLY LIEAR ERRORS-I-VARIABLES MODEL Xuemei HU Feng LIU Zhizhong WAG Received: 19 September
More informationUniversity of California San Diego and Stanford University and
First International Workshop on Functional and Operatorial Statistics. Toulouse, June 19-21, 2008 K-sample Subsampling Dimitris N. olitis andjoseph.romano University of California San Diego and Stanford
More informationThe EM Algorithm for the Finite Mixture of Exponential Distribution Models
Int. J. Contemp. Math. Sciences, Vol. 9, 2014, no. 2, 57-64 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ijcms.2014.312133 The EM Algorithm for the Finite Mixture of Exponential Distribution
More informationTobit and Interval Censored Regression Model
Global Journal of Pure and Applied Mathematics. ISSN 0973-768 Volume 2, Number (206), pp. 98-994 Research India Publications http://www.ripublication.com Tobit and Interval Censored Regression Model Raidani
More informationLocal Polynomial Modelling and Its Applications
Local Polynomial Modelling and Its Applications J. Fan Department of Statistics University of North Carolina Chapel Hill, USA and I. Gijbels Institute of Statistics Catholic University oflouvain Louvain-la-Neuve,
More informationThe properties of L p -GMM estimators
The properties of L p -GMM estimators Robert de Jong and Chirok Han Michigan State University February 2000 Abstract This paper considers Generalized Method of Moment-type estimators for which a criterion
More informationThe high order moments method in endpoint estimation: an overview
1/ 33 The high order moments method in endpoint estimation: an overview Gilles STUPFLER (Aix Marseille Université) Joint work with Stéphane GIRARD (INRIA Rhône-Alpes) and Armelle GUILLOU (Université de
More informationEstimation of the Conditional Variance in Paired Experiments
Estimation of the Conditional Variance in Paired Experiments Alberto Abadie & Guido W. Imbens Harvard University and BER June 008 Abstract In paired randomized experiments units are grouped in pairs, often
More informationNonparametric regression with rescaled time series errors
Nonparametric regression with rescaled time series errors José E. Figueroa-López Michael Levine Abstract We consider a heteroscedastic nonparametric regression model with an autoregressive error process
More informationSIMPLE AND HONEST CONFIDENCE INTERVALS IN NONPARAMETRIC REGRESSION. Timothy B. Armstrong and Michal Kolesár. June 2016 Revised October 2016
SIMPLE AND HONEST CONFIDENCE INTERVALS IN NONPARAMETRIC REGRESSION By Timothy B. Armstrong and Michal Kolesár June 2016 Revised October 2016 COWLES FOUNDATION DISCUSSION PAPER NO. 2044R COWLES FOUNDATION
More informationFunction of Longitudinal Data
New Local Estimation Procedure for Nonparametric Regression Function of Longitudinal Data Weixin Yao and Runze Li Abstract This paper develops a new estimation of nonparametric regression functions for
More informationLikelihood ratio confidence bands in nonparametric regression with censored data
Likelihood ratio confidence bands in nonparametric regression with censored data Gang Li University of California at Los Angeles Department of Biostatistics Ingrid Van Keilegom Eindhoven University of
More informationModel-free prediction intervals for regression and autoregression. Dimitris N. Politis University of California, San Diego
Model-free prediction intervals for regression and autoregression Dimitris N. Politis University of California, San Diego To explain or to predict? Models are indispensable for exploring/utilizing relationships
More informationDensity estimation Nonparametric conditional mean estimation Semiparametric conditional mean estimation. Nonparametrics. Gabriel Montes-Rojas
0 0 5 Motivation: Regression discontinuity (Angrist&Pischke) Outcome.5 1 1.5 A. Linear E[Y 0i X i] 0.2.4.6.8 1 X Outcome.5 1 1.5 B. Nonlinear E[Y 0i X i] i 0.2.4.6.8 1 X utcome.5 1 1.5 C. Nonlinearity
More informationarxiv: v1 [stat.me] 25 Mar 2019
-Divergence loss for the kernel density estimation with bias reduced Hamza Dhaker a,, El Hadji Deme b, and Youssou Ciss b a Département de mathématiques et statistique,université de Moncton, NB, Canada
More informationStatistical Properties of Numerical Derivatives
Statistical Properties of Numerical Derivatives Han Hong, Aprajit Mahajan, and Denis Nekipelov Stanford University and UC Berkeley November 2010 1 / 63 Motivation Introduction Many models have objective
More informationA Non-parametric bootstrap for multilevel models
A Non-parametric bootstrap for multilevel models By James Carpenter London School of Hygiene and ropical Medicine Harvey Goldstein and Jon asbash Institute of Education 1. Introduction Bootstrapping is
More informationOn block bootstrapping areal data Introduction
On block bootstrapping areal data Nicholas Nagle Department of Geography University of Colorado UCB 260 Boulder, CO 80309-0260 Telephone: 303-492-4794 Email: nicholas.nagle@colorado.edu Introduction Inference
More informationWeak Stochastic Increasingness, Rank Exchangeability, and Partial Identification of The Distribution of Treatment Effects
Weak Stochastic Increasingness, Rank Exchangeability, and Partial Identification of The Distribution of Treatment Effects Brigham R. Frandsen Lars J. Lefgren December 16, 2015 Abstract This article develops
More informationarxiv: v2 [stat.me] 12 Sep 2017
1 arxiv:1704.03924v2 [stat.me] 12 Sep 2017 A Tutorial on Kernel Density Estimation and Recent Advances Yen-Chi Chen Department of Statistics University of Washington September 13, 2017 This tutorial provides
More informationStochastic Optimization with Inequality Constraints Using Simultaneous Perturbations and Penalty Functions
International Journal of Control Vol. 00, No. 00, January 2007, 1 10 Stochastic Optimization with Inequality Constraints Using Simultaneous Perturbations and Penalty Functions I-JENG WANG and JAMES C.
More informationOptimal bandwidth selection for the fuzzy regression discontinuity estimator
Optimal bandwidth selection for the fuzzy regression discontinuity estimator Yoichi Arai Hidehiko Ichimura The Institute for Fiscal Studies Department of Economics, UCL cemmap working paper CWP49/5 Optimal
More informationQuantile methods. Class Notes Manuel Arellano December 1, Let F (r) =Pr(Y r). Forτ (0, 1), theτth population quantile of Y is defined to be
Quantile methods Class Notes Manuel Arellano December 1, 2009 1 Unconditional quantiles Let F (r) =Pr(Y r). Forτ (0, 1), theτth population quantile of Y is defined to be Q τ (Y ) q τ F 1 (τ) =inf{r : F
More informationIssues on quantile autoregression
Issues on quantile autoregression Jianqing Fan and Yingying Fan We congratulate Koenker and Xiao on their interesting and important contribution to the quantile autoregression (QAR). The paper provides
More informationQuantile regression and heteroskedasticity
Quantile regression and heteroskedasticity José A. F. Machado J.M.C. Santos Silva June 18, 2013 Abstract This note introduces a wrapper for qreg which reports standard errors and t statistics that are
More information